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The future of human behaviour research

Affiliations.

1 Department of Political Science, Ohio State University, Columbus, OH, USA. [email protected].
2 School of Communication and Digital Media Research Centre (DMRC), Queensland University of Technology, Brisbane, Queensland, Australia. [email protected].
3 Australian Research Council Centre of Excellence for Automated Decision-Making and Society (ADM+S), Melbourne, Victoria, Australia. [email protected].
4 Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy. [email protected].
5 Venetian Institute of Molecular Medicine (VIMM), Padova, Italy. [email protected].
6 Annenberg School for Communication and Journalism, University of Southern California, Los Angeles, CA, USA. [email protected].
7 Microsoft Research New York, New York, NY, USA. [email protected].
8 École Normale Supérieure, Paris, France. [email protected].
9 Department of Economics, Massachusetts Institute of Technology, Cambridge, MA, USA. [email protected].
10 Department of Human Behavior, Ecology, and Culture, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany. [email protected].
11 Department of Psychology, University of California at Berkeley, Berkeley, CA, USA. [email protected].
12 American University of Beirut, Beirut, Lebanon. [email protected].
13 Department of Global Development, College of Agriculture and Life Sciences and Cornell Atkinson Center for Sustainability, Cornell University, Ithaca, NY, USA. [email protected].
14 Department of Management, The Chinese University of Hong Kong, Hong Kong, Hong Kong Special Administrative Region, China. [email protected].
15 Center for Social and Environmental Systems Research, Social Systems Division, National Institute for Environmental Studies, Tsukuba, Japan. [email protected].
16 State Key Laboratory of Brain and Cognitive Sciences and Laboratory of Neuropsychology and Human Neuroscience, The University of Hong Kong, Hong Kong, Hong Kong Special Administrative Region, China. [email protected].
17 WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong Special Administrative Region, China. [email protected].
18 Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong, Hong Kong Special Administrative Region, China. [email protected].
19 Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA. [email protected].
20 Department of Experimental Psychology, University of Oxford, Oxford, UK. [email protected].
21 Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK. [email protected].
22 CORE - Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark. [email protected].
23 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. [email protected].
24 Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. [email protected].
25 Complex Human Data Hub, University of Melbourne, Melbourne, Victoria, Australia. [email protected].
26 ODID and SAME, University of Oxford, Oxford, UK. [email protected].
27 School of Public Policy, Georgia Institute of Technology, Atlanta, GA, USA. [email protected].
28 Centre of Excellence FAIR, NHH Norwegian School of Economics, Bergen, Norway. [email protected].
29 GESIS - Leibniz Institute for the Social Sciences, Köln, Germany. [email protected].
30 RWTH Aachen University, Aachen, Germany. [email protected].
31 Complexity Science Hub Vienna, Vienna, Austria. [email protected].
PMID: 35087189
DOI: 10.1038/s41562-021-01275-6

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The Neuroscience of Goals and Behavior Change

Elliot t. berkman.

Department of Psychology, Center for Translational Neuroscience, University of Oregon, and Berkman Consultants, LLC

The ways that people set, pursue, and eventually succeed or fail in accomplishing their goals are central issues for consulting psychology. Goals and behavior change have long been the subject of empirical investigation in psychology, and have been adopted with enthusiasm by the cognitive and social neurosciences in the last few decades. Though relatively new, neuroscientific discoveries have substantially furthered the scientific understanding of goals and behavior change. This article reviews the emerging brain science on goals and behavior change, with particular emphasis on its relevance to consulting psychology. I begin by articulating a framework that parses behavior change into two dimensions, one motivational (the will ) and the other cognitive (the way ). A notable feature of complex behaviors is that they typically require both. Accordingly, I review neuroscience studies on cognitive factors, such as executive function, and motivational factors, such as reward learning and self-relevance, that contribute to goal attainment. Each section concludes with a summary of the practical lessons learned from neuroscience that are relevant to consulting psychology.

Setting goals is easy; achieving them is hard. Why? This question has long stumped humanity and will certainly not be answered in this article. A full explanation of why it is hard to accomplish a goal or change old habits may never be possible. However, all hope is not lost. Research at the interface of neuroscience and psychology has made significant strides in uncovering the machinery behind goal pursuit. This knowledge, in turn, provides clues about the various ways that behavior change can go wrong and how to improve it. In this article, I present a brain-based framework for understanding how goal pursuit works and how to facilitate behavior change. Along the way, I highlight specific and practical lessons learned that are relevant to the science and practice of consulting psychology.

Goals and the Four Types of Behavior

What do I mean by goals? Colloquially, a goal is any desired outcome that wouldn’t otherwise happen without some kind of intervention. In other words, a goal is a detour from the path of least resistance. Formally, a goal is a desired future state (an end) coupled with a set of antecedent acts that promote the attainment of that end state (means; see Kruglanski, Shah, Fishbach, Friedman, Chun, & Sleeth-Keppler, 2002 for a summary). I present the informal definition first because it captures something that is missing from the formal one: a sense of what people actually mean by the word “goals” and how we use them. Technically, according to the formal definition, going out with friends to celebrate someone’s birthday is goal; it is an imagined end state and one must deploy various means to make it happen. But most people wouldn’t think of planning to go to a party later tonight as a goal. In practice, we set goals in cases where we need to do something that hasn’t happened yet and isn’t likely to happen on its own.

The difference between the two definitions of goals highlights an important aspect of goals and the way it is often overlooked. Goals are usually things we want but have difficulty achieving even when we know they are achievable. Otherwise, we wouldn’t need a goal in the first place. That sense of struggle is also captured in the term behavior change , which I use interchangeably with goal pursuit here. It’s not engaging in behavior, per se, but rather new behavior that is hard. To pursue what most people call a goal involves doing something different than what has been done before. For example, a primary incentive underlying achievement motivation (i.e., the need for achievement) is to demonstrate one’s capability to perform well on a new or challenging task ( McClelland, 1985 ).

To understand why new behavior is so hard, it’s useful to think about two dimensions that give rise to behaviors. The first dimension captures the skills, capacities, and knowledge required to engage in a behavior. This includes mapping out the steps to take and having the skill to execute an action, as well as related cognitive processes such as attentional focus, inhibitory control, and working memory capacity. Because it reflects the means used to achieve a goal, I refer to the first dimension as the way . The second dimension captures the desire for and importance of a behavior. This includes wanting to achieve a goal and prioritizing it over other goals, as well as related motivational processes such as volition, intention, and the nature and strength of the drive for achievement. Because it relates to the motivation to engage in a behavior, I refer to the second dimension as the will .

As shown in Figure 1 , these two dimensions give rise to four broad types of action. Complex-Routine behavior, in the top-left quadrant, requires some level of skill or knowledge but little motivation. Habitual behaviors reside in this quadrant: they can be quite complex yet are often triggered by external cues without motivation. For example, many drivers have piloted their car somewhere familiar, such as a child’s school, without thinking and despite an intention to go elsewhere. Indeed, a hallmark of habitual behavior is engaging in it even (or especially) in the absence of a conscious goal to do so ( Wood & Neal, 2007 ). Simple-Routine behavior, in the bottom-left quadrant, requires little skill and motivation. For example, walking, eating, and other behaviors related to primary rewards reside in this quadrant. These behaviors are so easy and effortless that we hardly think of them as goals at all. Because they are located in the same place on the horizontal axis and on different places on the vertical axis, the key difference between the first two types of behaviors is the level of skill they require. Simple-Novel behavior, in the bottom-right quadrant, requires high motivation but low skill to accomplish. Simple but new (and at times unpleasant) tasks such as changing a diaper belong in this quadrant. The most interesting kind of behavior is in the fourth quadrant: Complex-Novel behavior that requires high skill and high motivation. The goals that people care about most reside there.

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Behavior can be divided into four broad categories defined by the level of motivation they demand (horizontal axis) and the level of skill or ability they require (vertical axis). Behavior change typically involves moving from left-to-right, from bottom-to-top, or both. Moving from left-to-right increases the motivational demand ( why ) of an action, whereas moving from bottom-to-top increases the skill level ( how ). It is useful to identify the vector of change required during goal pursuit and to target motivational (horizontal) and cognitive (vertical) processes as necessary.

Differences between adjacent quadrants within this space are instructive. The key distinction between a rote, unpleasant task (bottom-right) and a complex, hard one (top-right) is skill- and knowledge-oriented. Changing one diaper doesn’t take much ability, but building a machine to do the task for you would require decades of schooling. Both require high levels of motivation. The lesson is that moving up and down in this space is a matter of skill-building. In contrast, the distinction between a complex task that happens easily (top-left) and one that requires effort (top-right) is motivational. Driving to your child’s school is easy because you’ve done it so many times that it has become a matter of habit. In contrast, driving for the first time in a new country relies on the same skillset but feels much harder because it forces you to focus and apply the driving and navigation skills you already have. As you do it more it becomes easier, of course, but you can still do it on the first attempt as long as you try hard enough. Moving from left to right in this space, therefore, is a matter of effort more than one of skill or knowledge. Once a person possesses the capacity and knowledge to accomplish a difficult task, the missing piece is motivation.

Lessons learned for consulting psychology

In light of this framework, the first step to facilitating behavior change is to diagnose the source of the difficulty. Consultants and coaches can do foundational work with their clients early in the behavior change process to pinpoint the nature of the behavior change and identify how the new behavior is different from old patterns. The first step to helping a client with behavior change can involve answering these questions:

Does the client already have the skills required for the new task?
Is the barrier to change a lack of a way or a lack of a will?
Is the person trying to move up, to the right, or both on the axes in Figure 1 ?

Once the most relevant dimension of change is identified, the second step is to drill down to learn more about the specific nature of the motivation or skills/capacities that will be the target. For example, consider the questions:

If motivation, is the client lacking motivation to approach a desirable outcome or to avoid an undesirable one (e.g., Berkman & Lieberman, 2010 )?
If motivation, is the client generally unmotivated, or highly motivated to a different goal besides than the behavior change goal?
If skills, are they related to interpersonal abilities (e.g., empathy and perspective taking) or executive functioning (e.g., inhibition and attentional control)?
If skills, is it possible that the client already possesses the skills but is stuck in a closed mindset and overly focused on one aspect of the behavior, such that a broadening of perspective might open new avenues for progress using other skills?

The relevant neuroscience will be quite different depending on the answer to these questions. In the following sections, I summarize the neuroscientific literatures on the will and the way with an emphasis on practical lessons for consulting psychology.

The neuroscience of the “way”: Executive function and cognitive control

Research on “the way” of goals and behavior change has mostly focused on constructs such as attention, working memory, inhibitory control, and planning – collectively known as executive function. A great deal of knowledge has been gained from neuroscientific studies about executive function, mostly about the neural systems and circuits that implement executive function (sometimes referred to as the task-positive network; Fox et al., 2005 ), and also about how disruptions to those circuits can cause alternately specific or broad impairment depending on the precise location and nature of the damage ( Alvarez & Emory, 2006 ; Stuss & Knight, 2012 ). Recent work has even begun to explore the bidirectional relationship between central and peripheral nervous system functioning in the context of goals, such as how activation of the sympathetic nervous system and hypothalamic-pituitary-adrenal axis during stress can influence executive function ( Roos et al., 2017 ). Together, imaging and lesion studies have illuminated many of the mechanistic elements and processes involved in complex goal pursuit ( Stuss, 2011 ). This information, in turn, contains some important lessons for consulting psychology about the capabilities and limits of executive function that are directly relevant to goals.

Despite substantial progress in knowledge of how executive function operate at the level of the brain, there is only sparse neuroscience research about how executive function might be improved. What little research there is suggests that executive function is more fixed than malleable by intervention, but there are some hints that targeted improvement might be possible. In this section, I review recent neuroscientific studies on executive function with respect to three questions that are pertinent to goals and behavior change: What is the nature of executive function? Is executive function a limited resource? And can executive function be improved with practice?

What is the nature of executive function?

Executive function refers to a suite of higher-level cognitive skills and capacities that generally promote successful human functioning. Attention, task switching, working memory, and inhibitory control are usually described as executive functions, though there is debate about the precise definition of the term ( Banich, 2009 ). Executive function involves some degree of updating information, shifting focus between targets or mental sets, and inhibiting irrelevant or distracting information ( Miyake, Friedman, Emerson, Witzki, Howerter, & Wager, 2000 ). Rather than enter that debate, I will describe broad features of executive function that are shared across most definitions. These features are useful for providing clarity and context for the subsequent questions regarding the limits and improvability of executive functions.

Executive function has three characteristic features: it is effortful , operates consciously , and engaged in service of novel goals as opposed to rote or overlearned ones (e.g., Miyake & Friedman, 2012). Effortful means that they feel hard and must be completed serially. In fact, emerging evidence suggests that one function of the dorsal anterior cingulate cortex (dACC; Figure 2 ), among several others, is to efficiently allocate cognitive resources by tracking the amount of mental work a task will require ( Shenhav, Cohen, & Botvinick, 2016 ). For example, activity in the dACC scales with the upcoming demand for control and also the potential payoff of that control ( Kouneiher, Charron, & Koechlin, 2009 ). It appears that the brain has dedicated regions not only to executing control but also allocating that control to various tasks.

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Regions implicated in the will and the way. Left: Lateral view featuring the lateral prefrontal cortex (LPFC) and the ventrolateral prefrontal cortex (VLPFC), premotor cortex (pMC) and motor cortex (MC), and the temporalparietal junction (TPJ) and supramarginal gyrus (SMG). Top Right: Medial view featuring the dorsal anterior cingulate cortex (dACC) and ventral striatum (vS), and the dorsomedial (dmPFC), medial (mPFC) and ventromedial (vmPFC) aspects of the prefrontal cortex. Bottom Right: Coronal view featuring the ventral (vS) and dorsolateral (dlS) aspects of the striatum.

Executive function is conscious, which means that it occurs within awareness and requires conscious attention. People know when they are engaging in executive function because it becomes the center of attention in a given moment. A classic example of executive function is mental math, such as multiplying 13 by 17. In contrast to things such as breathing or adding 1+1, you know when it happens because it occupies all of your attention, and it is generally voluntary. The steps involved in solving that problem recruit a host of executive functions surrounding attention: focusing attention on the appropriate column, swapping information in and out of attention, and restricting attention to the desired part of the operation to the exclusion of others. These short-term memory and attentional processes are supported by complex interactions among lateral prefrontal and parietal cortices including aspects of all three frontal gyri, the superior frontal sulcus and precentral gyrus, and the supramarginal gyrus and temporalparietal junction ( Figure 2 ; Nee, Brown, Askren, Berman, Demiralp, Krawitz, & Jonides, 2012 ). The role of these regions is not just to maintain information, but also to disengage attention from irrelevant or previously-relevant information as appropriate to the task ( Shipstead, Harrison, & Engle, 2016 ). The importance of redirecting attention underscores the limited-capacity nature of working memory and executive function more generally. Extensive cognitive processes and neural resources are dedicated to gating which information enjoys the focus of attention and which must be ignored. In this way, executive function generally, and attention specifically, play a key role in how open or closed we are to new ideas and perspectives during goal setting and goal striving.

In addition to feeling effortful and occupying conscious attention, a third characteristic property of executive function is that it specializes in novel tasks. It enables humans to do things that we’ve never done before. In fact, the basic role of the entire prefrontal cortex has been described broadly as coordinating behavior to achieve novel goals ( Miller & Cohen, 2001 ). The ability of our prefrontal cortex to plan and execute novel behaviors is one of the defining characteristics of humans and one that sets us apart from nearly all other animals. However, this ability is not unlimited. In light of the limited capacity of attention and working memory, the prefrontal cortex has a second function that is nearly as critical: to learn to automate novel behaviors to the point that they no longer take up precious space in consciousness. Research on this process of habit formation shows that as a particular behavior in a particular behavior is repeatedly rewarded, the systems that control it shift from the dorsomedial to the ventral and dorsolateral aspects of the striatum ( Figure 2 ; Yin, Mulcare, Kilario, Clouse, Holloway, Davis, et al., 2009 ). This shift is in part supported by the differential connectivity in these parts of the striatum, with the dorsomedial more strongly connected to the prefrontal and parietal cortices (involved in attention and working memory) and the other two parts of the striatum more strongly connected to the sensory and motor cortices ( Liljeholm & O’Doherty, 2012 ). That the process of routinizing behavior has a robust pathway embedded within some of the oldest structures in the brain speaks to the evolutionary importance of offloading effortful mental activities from the cortex as early and efficiently as possible. Thus, these regions are key for habit formation.

Is executive function a limited resource?

The answer to this question is both yes and no. Many readers will be familiar with the concept of ego depletion, or the idea that the “active self” that implements executive functions draws upon a finite resource that exhausts over time with repeated use, not unlike a fuel tank ( Baumeister, Bratlavsky, Muraven, & Tice, 1998 ). Though there are literally hundreds of published studies showing the effect ( Hagger, Wood, Stiff, & Chatzisarants, 2010 ), it is likely that many of those studies are false positives or unreliable ( Hagger, Chatzisarantis, Alberts, Anggono, Batailler, Birt, et al., 2016 ). A large, highly powered, preregistered study recently failed to replicate the ego depletion effect ( Lurquin, Michaelson, Barker, Gustavson, von Bastian, Carruth, et al., 2016 ), and a meta-analysis uncovered evidence of publication bias in the ego depletion field such that studies finding the effect are much more likely to appear in print than those that do not ( Carter & McCullough, 2014 ).

On a deeper level, there is strong counter-evidence to the basic ego depletion effect, for example that taking a short break, watching a fun film clip, or even smoking a cigarette can reverse the effect (see Inzlicht & Berkman, 2015 for a summary). Active-self processes such as executive function are unlikely to draw upon a limited physiological resource if simple psychological manipulations can replenish it. Even more suggestive, there is strong physiological evidence that the neuronal processes involved in executive function demand no more energy than simpler functions or even than the brain at rest (see Kurzban, 2010 , for a review). There is simply no special physiological resource for executive function to deplete. The bottom line is that people get tired when they work hard – which is nothing new – but that, contrary to popular belief about ego depletion, that sense of fatigue is mostly psychological and can be short circuited by a short rest and a variety of positive experiences.

But what about the experience of depletion? Everyone has the intuition that some mental activities – certainly including executive function – feel hard and seem to drain our energy. The answer may be found by adjusting our understanding what exactly the limited resource is. The original formulation of ego depletion hypothesized a physiological resource, likely centered in the brain. That prediction is no longer tenable given the data. Newer models focus on the contributions of psychological and motivational factors to depletion instead beyond strictly physiological ones. For example, a shift in priorities from effortful, obligation-based, and prevention-focused “have-to” goals to enjoyable, desire-based, promotion-focused “want-to” goals could explain the decline in performance on tough cognitive tasks ( Inzlicht, Schmeichel, & Macrae, 2014 ); perhaps the “resource” is prioritization. Another possibility is that depletion results from an interaction between psychological processes, such as perceptions of upcoming task demands and available resources, and physiological factors including the peripheral nervous system, hormones, and afferent inputs ( Evans, Boggero, & Segerstrom, 2016 ).

A psychological model that fits particularly well with the characterization of executive function above focuses on its opportunity cost ( Kurzban, Duckworth, Kable, & Myers, 2013 ). Because we can only focus our executive function capacity on one task at a time, then any time we engage in one executive function task we are likely forgoing others. The cost of what we’re giving up is reflected in the sense of effort that comes along with executive function. The feeling of depletion, therefore, reflects the tipping point when the cost of putting off alternative tasks begins to outweigh the benefit of continuing on the current course of action ( Berkman, Kahn, & Livingston, 2016 ).

The evidence at this point indicates that executive function is limited in terms of bandwidth – how much can be done or stored or attended to in a given moment – but not in terms of duration in the ego depletion sense. That limit stems directly from the properties of the executive function system: the facts that only a small amount of information can be consciously accessible and operated upon in a given moment ( Unsworth, Fukuda, Awh, & Vogel, 2015 ), and that we actively track the processing costs of potential cognitive operations with respect to ongoing goals ( Westbrook & Braver, 2015 ). For precisely this reason, executive function was likened by the mathematician and philosopher Alfred North Whitehead to cavalry in an army, “Operations of thought are like cavalry charges in a battle – they are strictly limited in number, they require fresh horses, and must only be made at decisive moments.” (pp. 61; Whitehead, 1911 ).

Can executive function be improved with practice?

There is naturally great interest in the question of whether executive function can be improved, expanded, or strengthened with practice given its bandwidth limitations. Study of this kind of “brain training” is an active research area and a controversial one. Some researchers make claims about the ability to improve executive function with training ( Jaeggi, Buschkuehl, Jonides, & Shah, 2011 ), though these claims have been tempered by compelling counter-evidence ( Redick, Shipstead, Harrison, Hicks, Fried, Hambrick, et al., 2013 ). A fair characterization of the research to date is that people can certainly improve on a given executive function task with practice, but there is no evidence that practice generalizes to other, even closely related tasks, and task-specific improvements are unlikely to endure over time ( Berkman, 2016 ).

The core issue in executive function training is transfer , or whether the improvements on a training task generalize to other tasks. In some theories such as the Strength Model, on which the ego depletion hypothesis is based, executive function is a common resource that is shared across many discrete capacities (e.g., working memory and self-control), so expanding that common resource should improve a range of executive abilities ( Muraven, 2010 ). However, counter-evidence to ego depletion specifically and the Strength Model generally have raised the question about whether a common underlying resource even exists ( Inzlicht et al., 2014 ). A recent meta-analysis of studies attempting to train one form of executive function, self-control, revealed a negligible transfer effect ( Inzlicht & Berkman, 2015 ). Additionally, at least two highly-powered studies have failed to find generalizable training effects on executive function despite showing practice effects on the training task ( Miles, Sheeran, Baird, Macdonald, Webb, & Harris, in press ; Redick et al., 2013 ).

What is happening? Neuroscientific investigations provide some clues. A series of training studies on inhibitory control, an executive function involving the prevention of ongoing or prepotent behavior, found that performance on an inhibitory control task improves with practice and does not transfer to other tasks. Interestingly, to the degree that performance on the training task improved, activity in the lateral prefrontal regions and dACC that is associated with successful inhibitory control shifted earlier in time, peaking in anticipation of the need for control ( Beauchamp, Kahn, & Berkman, 2016 ; Berkman, Kahn, & Merchant, 2014 ). This effect can be characterized as a reactive-to-proactive shift in the neural activation involved in inhibitory control, and is akin to gently applying a car’s brakes when a light turns yellow instead of slamming on the brakes only upon a red light.

The observed shift in brain activity from later to earlier in time fits well with the general characteristics of executive function described earlier. Inhibitory control feels hard and occupies attention, so it is beneficial to the individual to automate the operation when possible. With enough practice and exposure, the habit learning system discovers regularities in the environment that allow the need for inhibitory control to be anticipated using contextual cues. Just as the frequent association of a yellow light with a red light teaches experienced drivers to automatically move their foot to the brake when seeing a yellow, so too do participants in inhibitory control training studies learn the specific task cues that anticipate the need for control. This cue-learning effect in training occurs automatically ( Lenartowicz, Verbruggen, Logan, & Poldrack, 2011 ), suggesting that performance improvements during inhibitory control training studies are a result of the transfer of at least some effortful behavior to the habit system. Habits increase efficiency during goal striving.

This habit learning process also explains the lack of transfer to new tasks. The advantages of executive function are mirrored in the limitations of the habit learning system. Specifically, while executive function evolved to deal with novel challenges, habit learning evolved for routine ones. Habits create efficiency by shrinking the range of responses in a situation down to one behavior. By function, they forestall new and creative behaviors in that situation. Habitual behaviors are triggered by specific contextual cues, which is why habits do not require vigilant and costly monitoring; that work is offloaded to more efficient stimulus-response mappings. The tradeoff is that habitual behaviors are necessarily tied to a particular context. If the cues that had been associated with a response change, then the habitual response will no longer emerge. For example, the ease of slowing on a yellow would be lost if the cue that preceded a red light suddenly became blue instead. In the case of executive function, training doesn’t transfer to new contexts (or tasks) because the cues are different. The brain treats the tests of transfer as novel tasks, which is exactly what executive function evolved to deal with in the first place.

Lessons learned from neuroscience about “the way”

The neuroscience literature on executive function offers some practical if not entirely hopeful advice about the “way” of behavior change. The first lesson is that executive function feels hard for a reason. It is a serial process, so the sense of effort that accompanies executive function is a signal that working on a difficult task necessarily means losing out on other opportunities. In other words, effort reflects an opportunity cost. In this view, effort also signals one’s internal priorities; the more important the alternatives are, the harder a focal task will feel. The inverse is also true: a given task will feel relatively easy when it is more important to a person than the alternative choices. Consultants and coaches can work with clients to reflect on their priorities and make them explicit, which can explain why some goals feel harder than others.

The mental processes related to the “way” operate sequentially, not in parallel. Executive functions can only be performed one at a time, so the most important ones should come first even if executive processing will not exhaust over time with use. Based on the portrait of executive function drawn here, the factors that influence the capacity for executive function most directly are other concurrent cognitive operations and the relative importance of the task compared to other possibilities. Together, this suggests that it is optimal to carve out dedicated, distraction-free time to work on important novel tasks and challenges ( Berkman & Rock, 2014 ). Our cognitive bandwidth is precious and operates most efficiently in (mental) solitude. Licensing clients to reserve work time specifically for new tasks can help.

Our executive function abilities evolved to help us deal with novel challenges. So, the precious resource of executive function should be brought to bear on any and all aspects of behavior change, such as goal setting, that benefit from openness to new ideas, broadened attention, and a wide survey of possibilities. In contrast, habit formation evolved to create efficiency by rigidly attaching one behavior to one cue. Habits can be formed to aid in other aspects of behavior change, such as goal striving, that benefit from a narrower focus and relatively consistent, fixed behaviors in a given situation.

Finally, there is not much evidence that executive function can be improved broadly by focused interventions (e.g., Lumosity; Redick et al., 2013 ; Shute, Ventura, & Ke, 2015 ), and some compelling counter-evidence. However, complex mental operations can become routinized by leveraging the habit learning system ( Foerde, Knowlton, & Poldrack, 2006 ). Habit learning is facilitated when the new behavior is consistently preceded by specific cues and then rewarded. This procedure can be particularly useful for behavior change if the new behavior will occur repeatedly in similar contexts. Research is underway to test whether a highly variable set of cues used in training can broaden the range of contexts to which training effects generalize. Nonetheless, some executive functions such as working memory may simply be fixed capacities for neuroarchitectural reasons ( Zhange & Luck, 2008 ). Rather than attempting to improve executive function generally, consultants and coaches should help their clients focus on improving specifically the skillsets relevant to the goal or new behavior. These will improve with practice and, with some proper motivation, become habitual in time.

The neuroscience of the “will”: Motivation, Reward, and Subjective Value

The question of what motivates behavior, in a general sense, runs at least back to the Greeks, with Plato’s famous analogy of the charioteer and his horses, through William James and Abraham Maslow, and continues to this day. In contrast, the question of what motivates behavior change has received considerably less attention. Psychologists have developed taxonomies of different “stages of change” to capture individual variability in readiness to engage in sustained behavior change (Transtheoretical Model; Prochaska, DiClemente, & Norcross, 1992 ), and of different types of behaviors within a person to capture relatively self-motivated, “intrinsic” versus more externally-motivated, “extrinsic” types of goals (Self-Determination Theory; Deci & Ryan, 2000 ). Much of this work is descriptive rather than prescriptive – it says what motivation is but does not indicate how to increase it. A person can be confidently described as in the precontemplation stage, but there is not much evidence-backed knowledge about moving him or her to the contemplation stage; likewise, some behaviors are clearly extrinsically motivated, though there is a lack of prescriptive advice about how one can transform them into intrinsically motivated ones.

As it did with studies on the “way,” neuroimaging research provides some clues about how to increase motivation to change a specific behavior. In this section, I review neuroscientific insights into the “way” of behavior change surrounding three questions that are relevant to consulting psychology. Which brain systems are involved in motivational processes? How do those systems interact with other networks in the brain? And what does neuroscience indicate about motivating behavior change?

How and where is motivation represented in the brain?

Motivation is conceptualized here as the strength of the desire to attain a particular outcome, irrespective of how pleasant or unpleasant the experience of actually attaining it is. This distinction between the motivational component of a reward – “wanting” – and the hedonic component of consuming it – “liking” – is maintained with remarkable evolutionary consistency in the brains of both humans and animals ( Berridge & Robinson, 2003 ). I focus here on the “wanting” side because of its direct bearing on behavior and behavior change. Wanting a reward is closely tied with activity of mesolimbic dopaminergic neurons, particularly within the ventral striatum and ventromedial prefrontal cortex ( Berridge, 2006 ; Figure 2 ), which is sometimes also called the orbitofrontal cortex ( Wallis, 2007 ). Of course, there are many other regions and interactions involved in reward learning, but I focus on these because they are the best characterized in terms of human functional neuroanatomy to date.

The dopaminergic reward system has been conserved evolutionarily because it plays a critical role in the reinforcement learning cycle. When a particular behavior in a given context it is rewarded, that behavior and context are paired and tagged with reward value for later repetition ( Rescorla & Wagner, 1972 ). Reinforcement learning is why behaviors that are rewarded are likely to be repeated in the future. (This is also why the dopamine system is implicated in addictive behavior.) The amount of cumulative, learned reward value of a behavior is its expected value, sometimes referred to as subjective value ( Rangel & Hare, 2010 ). In short, subjective value represents the amount of reward that an actor expects to receive for a given action, largely based on past learning. This learning cycle is one of the key impediments to behavior change: old behavior has been rewarded and new behavior has not. A protein called brain-derived neurotrophic factor (BDNF) is important for maintaining new behaviors after engaging in them initially because of its critical role in memory consolidation ( Bekinschtein et al., 2008 ). As described in the following sections, the key to launching this reward learning and consolidation cycle is finding ways to increase the subjective value of new behavior.

A notable feature of activity in the ventromedial prefrontal cortex is that it represents the subjective values of diverse types of actions, presumably to facilitate “apples to oranges” decisions between qualitatively different behaviors ( Levy & Glimcher, 2011 ). For example, activity in the ventromedial prefrontal cortex tracks the value of approach appetitive and avoiding aversive stimuli ( Tom, Fox, Trepel, & Poldrack, 2007 ), and also the subjective value of a range of stimulus types, including food, money, gains for the self and others, charitable decisions, and emotional and utilitarian benefits of moral actions ( Hare, Camerer, Knoepfle, O’Doherty, & Rangel, 2010 ; Hutcherson, Montaser-Kouhsari, Woodward, & Rangel, 2015 ; Lebreton, Jorge, Michel, Thirion, & Pessiglione, 2009 ; Zaki, Lopez, & Mitchell, 2014 ). These findings converge on the idea that the ventromedial prefrontal cortex plays a central role in tracking the subjective value of different kinds of actions during choice, which strongly implicates that region in motivational processing during behavior change.

How do motivation regions interact with other brain systems?

One way to approach the deeper issue of where motivation originates is to examine the connectivity of its neural systems. In the same way that it is adaptive to humans and informative to scientists that sensory and motor regions in the brain are adjacent and highly interconnected, the regions involved in motivation are themselves intertwined with several other brain networks. Those interrelations contain insights about how motivation operates and how it might be increased in the service of behavior change.

As Self-Determination Theory suggests, autonomously choosing to engage in a behavior (relative to being forced) increases performance on that behavior because autonomy is an intrinsic motive. At the neural level, autonomy also prevents a reduction in reward system activity in the face of negative feedback, particularly in the ventromedial prefrontal cortex ( Murayama, Matsumoto, Izuma, Sugiura, Ryan, Deci, et al., 2013 ). Interestingly, the ventromedial prefrontal cortex has also been found to be active in studies of self-processing and particularly of self-affirmation , such as considering one’s core personal values ( Cascio, O’Donnell, Tinney, Lieberman, Taylor, Strecher, et al., 2016 ). Brain activation related to self-affirmation during health messaging has even been shown to predict the eventual degree of health behavior change that would follow ( Falk, O’Donnell, Cascio, Tinney, Kang, Lieberman, et al., 2015 ). Finally, a meta-analysis using the Neurosynth study database ( Yarkoni, Poldrack, Nichols, Van Essen, & Wager, 2011 ) found that the ventromedial prefrontal cortex was one of the largest regions of overlap between 812 studies on identity (“self” and “self-referential” terms in the database) and 324 subjective value and reward (“value” term in the database). The meta-analysis contained several regions along the medial cortical wall including the ventromedial prefrontal cortex, the posterior cingulate cortex, and the mid-cingulate. The ventromedial prefrontal cortex was the single largest cluster to be consistently associated with both identity and value.

The overlap between intrinsic goals, core values, and subjective value has several implications for consulting psychology. First, identity (e.g., self-concept) and subjective value are closely functionally connected to one another. This is not a surprise given the extensive evidence from social psychology and other fields that people have disproportionate positive regard for themselves (and behaviors related to the self) compared to others ( Greenwald, 1980 ; Pelham & Swann, 1989 ). We want, and perhaps need, to see our selves as good ( Rosenberg, 1979 ). Second, the value derived from identity and other self-related processes may have a special status compared to other sources of value (e.g., monetary) because of the high degree of overlap in the neural systems and conceptual representation of identity and value. It may even be that identity and value are inseparable, leading one researcher to hypothesize that the defining function of the self is to organize and prioritize the world by assigning it motivational significance ( Northoff & Hayes, 2011 ). By this definition, the self-concept is exactly the set of places, things, and actions in the world that hold value.

It is important to note that the valuation process subserved by the vmPFC reflects not only positive value, but negative value as well. For example, just as social affiliation holds positive value, the threat of social rejection can be highly negative in value. The experience of social rejection invokes similar brain networks as physical pain ( Lieberman & Eisenberger, 2015 ). Beyond its unpleasantness, this experience can enhance defensiveness and facilitate a stress response that detracts from other ongoing goals because it narrows attentional focus on the social threat ( Muscatell et al., 2016 ).

The ventromedial prefrontal cortex and related dopaminergic motivational structures also interact with cognitive networks, including those related to executive function ( Botvinick & Braver, 2015 ). The ventromedial prefrontal cortex appears to be a point of convergence where the motivational value of various options in a choice are integrated, notably including both effortful actions that require cognitive control and also easier, more hedonic ones ( Bartra, McGuire, & Kable, 2013 ). For example, the dorsolateral prefrontal cortex is functionally connected with the ventromedial prefrontal cortex when higher-order goals such as health concerns or social factors are made salient ( Hare et al., 2010 ; Hutcherson, Plassman, Gross, & Rangel, 2012 ). There is also evidence that the value of potential actions are reflected in the ventromedial prefrontal cortex before any specific action plans is selected ( Wunderlich, Rangel, & O’Doherty, 2010 ), but that value signals provide input to downstream brain regions that are responsible for selecting and implementing behavior ( Hare, Schultz, Camerer, O’Doherty, & Rangel, 2011 ). Taken together, then, the emergent view from the neuroscience literature is that the ventromedial prefrontal cortex receives a variety of value signals relevant to decisions about behavior, and its activation reflects a dynamic value integration process that subsequently biases behavior toward higher-valued actions. A promising route to increasing motivation, then, is identifying the value inputs to a new behavior (i.e., the reasons why the behavior is or is not valued) and learning ways to modulate them. I address this possibility in the next section.

How can motivation be increased?

The neurally-informed model described above suggests that motivation is guided by an integration of the value of features of the behavioral options. Behavior change can be accomplished by amplifying the value of the new (goal-related) behavior, reducing the value of old (goal-counter or goal-unrelated) behaviors, or some combination of the two. A clear example of the effectiveness of the first approach is contingency management treatment for substance use disorders ( Bigelow & Silverman, 1999 ), in which the value of drug abstinence is increased with monetary incentives. A meta-analysis found this approach to have an effect size d = 0.42 on treatment for alcohol, tobacco, and illicit drugs, which was larger than therapy (d = 0.25) and outpatient treatment (d = 0.37), and comparable to methadone treatment for opiate use ( Prendergast, Podus, Finney, Greenwell, & Roll, 2006 ). Similarly, “precommitting” to buy more healthy foods at the risk of losing financial incentives is more effective than having the incentives alone ( Schwartz, Mochon, Wyper, Maroba, Patel, & Ariely, 2014 ). Monetary incentives also increase persistence at exercise ( Cabanac, 1986 ), endurance on a cold-pressor task ( Baker & Kirsch, 1991 ), and performance on a difficult cognitive task ( Boksem, Meijman, & Lorist, 2006 ). Simple monetary payments are an effective way to motivate behavior change.

“Money walks,” as the saying goes, but its scarcity makes it a less than ideal option for many goal pursuit contexts. Above, I noted the deep connections between identity and motivation. Other researchers have, too, and are now beginning to deploy identity interventions to increase motivation. For example, one study leveraged the fact that most people consider willpower to be a desirable trait ( Magen & Gross, 2007 ). The participants in that study completed an executive function task twice, and in between were randomly assigned to reconstrue the task itself as a measure of their own willpower or not. Performance improved from the first to the second run only among participants whose perceptions of the task were changed from non-diagnostic to diagnostic of willpower. Similarly, noting that identity is somewhat susceptible to cognitive shifts such as framing, construal, or priming effects, other researchers used a simple “noun-verb” manipulation to increase motivation for behavior change, presumably through a subtle shift in the extent to which the new behavior is construed as identity-relevant. For example, phrasing questions about voting intentions in terms of identity (noun: “being a voter”) instead of an action (verb: “voting”) increased voting intentions and actual turnout in statewide elections ( Bryan, Walton, Rogers, & Dweck, 2011 ). In another study, participants were less likely to cheat by claiming money they were not entitled to if that behavior was described as a (negative) identity (noun: “being a cheater”) instead of an action (verb: “cheating”; Bryan, Adams, & Monin, 2013 ). Each of these results is consistent with the idea that identity can influence motivation, presumably by highlighting the subjective value of desired (e.g., “voter”, “willpower”) or undesired (e.g., “cheater”) identity. This path is a promising future direction for motivation interventions because it is low-cost, modest in scope, and easily scalable to a broad range of populations and types of desired identities.

Finally, merely highlighting certain attributes of a behavior can alter the value placed on that behavior. After all, our attentional bandwidth is fairly narrow, so not all relevant properties will be equally salient at all times. For example, people’s motivation to act on a choice option increases as attention is allocated to it ( Krajbich, Armel, & Rangel, 2010 ). In another study ( Hare et al., 2011 ), participants were presented with health-versus-taste decisions with or without reminders about health. As expected, health reminders increased the likelihood of healthy choices. Tellingly, the healthiness rating of the foods (assessed earlier, and separate from the tastiness) was strongly correlated with activity in the ventromedial prefrontal cortex at the moment of decision, which in turn predicted the food choice. In contrast, when unhealthy foods were selected, the earlier tastiness ratings were correlated with ventromedial prefrontal cortex activity during choice. The results of these studies are broadly consistent with psychological framing effects (e.g., gain vs. loss frame; Kahneman & Tversky, 1984 ), whereby altering the relative salience of the features of a decision can dramatically change it. Though they are most often applied to decision-making, the neuroscientific evidence presented here suggests that motivation may also be susceptible to framing effects.

In light of the present framework, I focused on ways to increase motivation that are grounded in valuation. But there are other ways to increase motivation from complementary lines of research that nonetheless may be connected to subjective value. For example, Higgins has argued that people experience “value from fit” when their regulatory style (promotion versus prevention focus) matches the particular means through which goals are pursued ( Higgins, Idson, Freitas, Spiegel, & Molden, 2003 ). A similar “matching” effect on motivation has been observed with achievement motivation and performance goals: people high in achievement motivation experience greater intrinsic motivation when provided with performance (vs. mastery) goals, whereas people low in achievement motivation experience greater intrinsic motivation with mastery (vs. performance) goals ( Elliot & Harackiewicz, 1994 ). A plausible cause of these kinds of “matching” effects, which can be tested in future research, is that there is subjective value in experiencing fit between one’s dispositional tendencies and the nature of the goal at hand.

Lessons learned from neuroscience about “the will”

Neuroscientific investigations of motivation have established the major brain systems for motivation and identified ways that those systems interact with other parts of the brain. This knowledge, in turn, contains clues about how motivation works and how to increase it on the psychological level. Two are particularly relevant to consulting psychology.

The first lesson surrounds the extent to which motivation is tied to the past. The neural mechanisms of reinforcement learning are some of the most basic and ancient parts of our brains. For good reason, we evolved to be highly sensitive to learn where we receive rewards and to work hard to recreate the situations that brought them about. Attempting to change behavior in a systematic way by engaging in new behaviors, which have never been reinforced, often means working against this powerful system. Thus, wise advice for clients that is grounded in the neuroscience of motivation and reinforcement learning is to start behavior change with modest goals and reward even the smallest steps toward them. New behaviors emerge slowly because they are usually working against the power of prior reinforcement. Consultants and coaches can help clients anticipate and understand the difficulty of behavior change by explaining the neuroscience of reinforcement learning. Being cognizant of the challenges of behavior change can prevent frustration on both sides.

The second lesson is to leverage the intrinsic connections between the motivation system and other parts of the brain, particularly self and identity. The elaborated web of memories, beliefs, values, objects, and relationships that comprise our sense of self is paralleled perhaps only by executive function in its distinctiveness to humans. And it may offer a pathway to behavior change and goal achievement that is just as potent. A behavior will hold greater subjective value to the degree that it is related to one’s core values and sense of self. Identity-linked goals are more likely to be successful than identity-irrelevant or identity-counter ones. Consultants and coaches can be particularly helpful to clients in this arena by helping them discover core aspects of their self-concepts and the ways those aspects are linked to the behavior change at hand. And remember that identity is not a fixed construct, but rather is susceptible to framing, reconstrual, and other kinds of subtle influences. To some extent, motivation can be gained by finding ways to think about goals that makes their connection to important parts of one’s identity salient. Sometimes it is easier for other people to make these connections than for us because they have more distance from them ( Berkman & Rock, 2014 ); coaches can be particularly helpful in this regard. Paying people works, too, but connecting goals to the self-concept in various ways may be a more sustainable and accessible approach to increasing motivation.

Pursing goals and changing behavior is hard. Neuroscience will never change that fact, but it can provide some brain-level explanations for the difficulty as well as some new insights about how to mitigate it. This article reviewed the neuroscientific literatures on the “way” of goal pursuit – the set of cognitive skills, capacities, and abilities collectively known as executive function – and the “will” – the motivational factors that propel behavior. Although parts of the “way” are limited by constraints that may be difficult to change, the “will” can be influenced by incentives both within the person and without. Though neuroscientific investigations into long-term behavior change are only just starting to emerge they have already begun to contribute to the body of practical scientific knowledge about goals. The science and practice of consulting psychology will benefit directly from this research in the coming years.

Functional neuroanatomy of key networks

Network	Primary regions	Major functions	Summary citation
Affective salience network	Dorsal anterior cingulate (dACC), anterior insula, subgenual ACC	Interoceptive awareness, emotional distress, pain
Cognitive control/Task-positive network	Lateral prefrontal cortex (lPFC), parietal cortex, dACC, temporalparietal junction (TPJ)	Attentional control, working memory, task switching
Default mode network	Medial prefrontal cortex (mPFC), medial temporal lobes, posterior cingulate cortex (PCC)	Task negative network, mind wandering, self-processing	Greicius, Supekar, Menon, & Dougherty, 2009
Emotion regulation network	Ventrolateral prefrontal cortex (vlPFC), dorsolateral prefrontal cortex (dlPFC), lPFC	Cognitive reappraisal, self-distancing, emotional construal
Self-processing network	mPFC, PCC, TPJ, middle temporal lobe	Self-related cognition, introspection, self-consciousness, self-affirmation
Valuation and reward network	Ventromedial prefrontal cortex (vmPFC), orbitofrontal cortex (OFC), ventral striatum (vS)	Valuation/evaluation, reward anticipation, reward learning, affective significance

Acknowledgments

This work was supported by grants AG048840, CA175241, and DA035763 from the National Institutes of Health to ETB, as well as support from the Bezos Family Foundation and the Center for the Developing Child at Harvard University.

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Behavior Analysis: Research and Practice

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Behavior Analysis: Research and Practice is a multidisciplinary journal committed to increasing the communication between the subdisciplines within behavior analysis and psychology, and bringing up-to-date information on current developments within the field.

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Joel E. Ringdahl University of Georgia, United States

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Abstracting and indexing services providing coverage of Behavior Analysis: Research and Practice

Special issue of APA’s journal Behavior Analysis: Research and Practice, Vol. 21, No. 3, August 2021. This special issue highlights works that offer new or innovative perspectives on the role behavior analysis plays in growing this area of research and practice via (a) informing health and fitness behavior change; (b) designing and evaluating interventions to support health-behavior change or improve fitness and sport performance; and (c) identifying opportunities and recommendations to advance research and inform practice in the areas of health, sport, and fitness.

Special issue of the APA journal Behavior Analysis: Research and Practice, Vol. 18, No. 1, February 2018. Themes of the articles include addressing difficulties associated with neurocognitive disorders such as Alzheimer's disease and the use of stimulus preference assessment procedures.

Special issue of the APA journal Behavior Analysis: Research and Practice, Vol. 16, No. 4, November 2016. Articles discuss behavioral pharmacology's contributions to understanding the behavioral effects of drugs of abuse and other substances, the variables that modulate those effects, and the mechanisms through which they are produced, and offer novel and important suggestions for advancing the discipline.

Special issue of the APA journal Behavior Analysis: Research and Practice, Vol. 17, No. 3, August 2017. The articles in this issue address behavior analysis in education in three domains: replicating procedures established in controlled evaluations in classrooms, expanding access to behavioral intervention, and evaluating variations of procedures designed for school use.

Special issue of the APA journal Behavioral Analysis: Research and Practice, Vol. 15, No. 1, February 2015. Includes articles about operant discrimination learning, class size effects, game research, and behavior research using animals.

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Article Contents

Introduction, what is hbe, a systematic overview of current research, hbe: strengths, weaknesses, opportunities, and open questions, supplementary material, human behavioral ecology: current research and future prospects.

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Daniel Nettle, Mhairi A. Gibson, David W. Lawson, Rebecca Sear, Human behavioral ecology: current research and future prospects, Behavioral Ecology , Volume 24, Issue 5, September-October 2013, Pages 1031–1040, https://doi.org/10.1093/beheco/ars222

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Human behavioral ecology (HBE) is the study of human behavior from an adaptive perspective. It focuses in particular on how human behavior varies with ecological context. Although HBE is a thriving research area, there has not been a major review published in a journal for over a decade, and much has changed in that time. Here, we describe the main features of HBE as a paradigm and review HBE research published since the millennium. We find that the volume of HBE research is growing rapidly, and its composition is changing in terms of topics, study populations, methodology, and disciplinary affiliations of authors. We identify the major strengths of HBE research as its vitality, clear predictions, empirical fruitfulness, broad scope, conceptual coherence, ecological validity, increasing methodological rigor, and topical innovation. Its weaknesses include a relative isolation from the rest of behavioral ecology and evolutionary biology and a somewhat limited current topic base. As HBE continues to grow, there is a major opportunity for it to serve as a bridge between the natural and social sciences and help unify disparate disciplinary approaches to human behavior. HBE also faces a number of open questions, such as how understanding of proximate mechanisms is to be integrated with behavioral ecology’s traditional focus on optimal behavioral strategies, and the causes and extent of maladaptive behavior in humans.

Very soon after behavioral ecology (henceforth BE) emerged as a paradigm in the late 1960s and early 1970s, a tradition of applying behavioral ecological models to human behavior developed. This tradition, henceforth human behavioral ecology (HBE), quickly became an important voice in the human-related sciences, just as BE itself was becoming an established and recognized approach in biology more generally. HBE continues to be an active and innovative area of research. However, it tends not to receive the attention it might, perhaps in part because its adherents are dispersed across a number of different academic disciplines, spanning the life and social sciences. Although there were a number of influential earlier reviews, particularly by Cronk (1991) and Winterhalder and Smith (2000) , there has not been a major review of the HBE literature published in a journal for more than a decade. In this paper, we undertake such a review, with the aim of briefly but systematically characterizing current research activity in HBE, and drawing attention to prospects and issues for the future. The structure of our paper is as follows. In the section “What is HBE?”, we provide a brief overview of the HBE approach to human behavior. The section “A systematic overview of current research” presents our review methodology and briefly describes what we found. We argue that the HBE research published in the period since 2000 represents a distinct phase in the paradigm’s development, with a number of novel trends that require comment. Finally, the section “HBE: strengths, weaknesses, opportunities, and open questions” presents our reflections on the current state and future prospects of HBE, which we structure in terms of strengths, weaknesses, opportunities, and open questions.

BE is the investigation of how behavior evolves in relation to ecological conditions ( Davies et al. 2012 ). Empirically, there are 2 arms to this endeavor. One arm is the study of how measurable variation in ecological conditions predicts variation in the behavioral strategies that individuals display, be it at the between-species, between-population, between-individual, or even within-individual level. (Throughout this paper, “ecological conditions” is to be interpreted in its broadest sense, to include the physical and social aspects of the environment, as well as the state of the individual within that environment.). The other arm concerns the fitness consequences of the behavioral strategies that individuals adopt. Because fitness—the number of descendants left by individuals following a strategy at a point many generations in the future—cannot usually be measured within a study, this generally means measuring the consequences of behavioral strategies in some more immediate proxy currency related to fitness, such as survival, mating success, or energetic return. The 2 arms of BE are tightly linked to one another; the fitness consequences of some behavioral strategy will differ according to the prevailing ecological conditions. Moreover, central to BE is the adaptationist stance. That is, we expect to see, in the natural world, organisms whose behavior is close to optimal in terms of maximizing their fitness given the ecological conditions that they face. This expectation is used as a hypothesis-generating engine about which behaviors we will see under which ecological conditions. The justification for the adaptationist stance is the power of natural selection. Selection, other things being equal, favors genes that contribute to the development of individuals who are prone to behaving optimally across the kinds of environments in which they have to live ( Grafen 2006 ). Note that this does not imply that behavioral strategies are under direct genetic control. On the contrary, selection favors various mechanisms for plasticity, such as individual and social learning, exactly because they allow individuals to acquire locally adaptive behavioral strategies over a range of environments ( Scheiner 1993 ; Pigliucci 2005 ), and it is these plastic mechanisms that are often in immediate control of behavioral decisions. However, the capacity for plasticity is ultimately dependent on genotype, and plasticity is deployed in the service of genetic fitness maximization.

BE is also characterized by a typical approach, to which actual exemplars of research projects conform to varying degrees. This approach is to formulate simple a priori models of what the individual would gain, in fitness terms, by doing A rather than B, and using these models to make predictions either about how variation in ecological conditions will affect the prevalence of behaviors A and B, or about what the payoffs to individuals doing A and B will be, in some currency related to fitness. These models are usually characterized by the assumption that there are no important phylogenetic or developmental constraints on the range of strategies that individuals are able to adopt and also by a relative agnosticism about exactly how individuals arrive at particular behavioral strategies (i.e., about questions of proximate mechanism as opposed to ultimate function; Mayr 1961 ; Tinbergen 1963 ). The assumptions of no mechanistic constraints coming from the genetic architecture or the neural mechanisms are known, respectively, as the phenotypic gambit ( Grafen 1984 ) and the behavioral gambit ( Fawcett et al. 2012 ). To paraphrase Krebs and Davies (1981 ), “think of the strategies and let the mechanisms look after themselves.” We return to the issue of the validity of the behavioral gambit in particular in section “Open questions.” However, one of the remarkable features of early research in BE (what Owens 2006 calls “the romantic period of BE”) was just how well the observed behavior of animals of many different species was explained by very simple optimality models based on the gambits.

HBE is the study of human behavior from an adaptive perspective. Humans are remarkable for their ability to adapt to new niches much faster than the time required for genetic change ( Laland and Brown 2006 ; Wells and Stock 2007 ; Nettle 2009b ). HBE has been particularly concerned with explaining this rapid adaptation and diversity, and thus, the concept of adaptive phenotypic plasticity has been even more central to HBE than it is to BE in general. HBE represents a rejection of the notion that fundamentally different explanatory approaches are necessary for the study of human behavior as opposed to that of any other animal. Note that this does not imply that humans have no unique cognitive and behavioral mechanisms. On the contrary, they clearly do. Rather, it implies that the general scientific strategy for explaining behavior instantiated in BE remains similar for the human case: understand the fitness costs and benefits given the ecological context, make predictions based on the hypothesis of fitness maximization, and test them. There is a pleasing cyclicity to the development of HBE. BE showed that microeconomic models based on maximization, which had come from the human discipline of economics, could be used at least as a first approximation to predict the behavior of nonhuman animals. HBE imported these principles, enriched from their sojourn in biology by a focus on fitness as the relevant currency, back to humans again.

The first recognizably HBE papers appeared in the 1970s (e.g., Wilmsen 1973 ; Dyson-Hudson and Smith 1978 ). The pioneers were anthropologists, and to a lesser extent archaeologists. A major focus was on explaining foraging patterns in hunting and gathering populations ( Smith 1983 ), though other topics were also represented from the outset ( Cronk 1991 ). The focus on foragers was due to the evolutionary antiquity of this mode of subsistence, as well as these being the populations in which optimal foraging theory was most straightforwardly applicable. However, there is no reason in principle for HBE research to be restricted to such populations. The emphasis in HBE is on human adaptability; humans have mechanisms of adaptive learning and plasticity by virtue of which they can rapidly find adaptive solutions to living in many kinds of environments. Thus, we might expect their behavior to be adaptively patterned in societies of all kinds, not just the types of human society, which have existed for many millennia.

The first phase of HBE lasted through the 1980s ( Borgerhoff Mulder 1988 ). In the second phase, the 1990s, HBE grew rapidly, with Winterhalder and Smith (2000) estimating that there were nearly 300 studies published during the decade. Its focus broadened to encompass more studies from nonforaging subsistence populations, such as horticulturalists and pastoralists (e.g., Borgerhoff Mulder 1990 ), and the use of historical demographic data (e.g., Voland 2000 ; Clarke and Low 2001 ). There were also some pioneering forays into the BE of industrialized populations ( Kaplan 1996 ; Wilson and Daly 1997 ). The 1990s were characterized by an increasing emphasis on topics which fall under the general headings of distribution (cooperation and social structure) and particularly reproduction (mate choice, mating systems, reproductive decisions, parental investment), rather than production (foraging). Anthropologists continued to dominate HBE, and the methodologies of the studies reflect this: many of the studies represented the field observations of a single field researcher from a single population, usually a single site. Having briefly outlined what HBE is and where it came from, we now turn to reviewing the HBE research that has appeared in the years since the publication of Winterhalder and Smith (2000) .

Our objective was to ascertain what empirical research has been done within the HBE paradigm since 2000, and characterize its key features, quantitatively where possible. We thus conducted a systematic search of 17 key journals for papers published between the beginning of 2000 and late 2011, which clearly belong in the HBE tradition (see Supplementary material for full methodology). This involved some contentious decisions about how to draw the boundaries of HBE and in the end, we drew it narrowly, including only papers containing quantitative data on naturally occurring behavior in human populations and employing a clearly adaptive perspective. This excludes a large number of studies that take an adaptive perspective but measure hypothetical preferences or decisions in experimental scenarios. It also excludes many studies that focus on nonbehavioral traits such as stature or physical maturation. The sample is not exhaustive even of our chosen subset of HBE, given that some HBE research is published in edited volumes, books, or journals other than those we searched. However, we feel that our strategy provides a good transect through current research, which is prototypically HBE, and the sampling method is at least repeatable and self-consistent over time.

We used the full text of the papers identified to code a number of key variables relevant to our review, including year of publication, journal, first author country of affiliation, and first author academic discipline. We also adopted Winterhalder and Smith’s (2000) ternary classification of topics into production (foraging and other productive activity), distribution (resource sharing, cooperation, social structure), and reproduction (mate choice decisions, sexual selection, life-history decisions, parental and alloparental investment). Finally, we coded the presence of some key features we wished to examine: the presence of any data from foraging populations, the presence of any data from industrialized populations, the use of secondary data, and the use of comparative data from more than one population.

The search resulted in a database of 369 papers (see Supplementary material for reference list and formal statistical analysis; an endnote library of the references of the papers in the database is also available from the corresponding author). The distribution of papers across journals is shown in Table 1 , which also shows the median year of publication of a paper in that journal. The overall median year of publication for the full sample was 2007; thus, the table can be used to identify those journals that carried HBE papers disproportionately earlier in the study interval (e.g., American Anthropologist , median 2004), and those which carried them disproportionately more recently (e.g., American Journal of Human Biology , median 2009). The total number of papers found per year increased significantly over the 12 years sampled, from around 20 at the beginning to nearly 50 in 2011 ( Figure 1a ; regression analysis suggests an average increase of 2.4 papers per year). In the Supplementary material , we show that HBE papers also increased as a proportion of all papers published in our target journals. First authors were affiliated with institutions in 28 different countries, with 57.5% based in the United States and 20.1% in the United Kingdom. In terms of discipline, anthropology (including archaeology) was strongly represented (49.9% of papers), followed by psychology (19.5%) and biology (12.7%). The remaining papers came from demography (3.3%), medicine and public health (3.0%), sociology and social policy (2.4%), economics and political science (2.2%), or were for various reasons unclassifiable (7.0%). However, the growth in number of papers over time was due to increasing HBE activity outside anthropology ( Figure 1a ). In 2000–2003, 64.0% of papers were from anthropology departments, whereas by 2009–2011, this figure was 47.4%. Our search strategy may, if anything, have underestimated the growth in HBE research from outside anthropology, because our search strategy was based on the journals that had carried important BE or HBE research prior to 2000 and did not include any specialist journals from disciplines such as demography or public health.

Numbers and percentages of papers in the database by journal. Also shown is the median year of publication of an HBE paper in the sample in that journal

Journal .	Number of papers (percentage of sample) .	Median year of publication .
	10 (2.7)	2004
	38 (10.3)	2009
	3 (0.8)	2010
	5 (1.4)	2004
	37 (10.0)	2005.5
	91 (24.7)	2007
(2003–2011)	17 (4.6)	2008
	87 (23.6)	2007
	17 (4.6)	2007
(2003–2011)	7 (1.9)	2006
	3 (0.8)	2010
(2003–2011)	6 (1.6)	2011
	1 (0.3)	2004
	5 (1.4)	2011
	27 (7.3)	2006
	10 (2.7)	2008
	5 (1.4)	2009
Overall	369 (100)	2007

Journal .	Number of papers (percentage of sample) .	Median year of publication .
	10 (2.7)	2004
	38 (10.3)	2009
	3 (0.8)	2010
	5 (1.4)	2004
	37 (10.0)	2005.5
	91 (24.7)	2007
(2003–2011)	17 (4.6)	2008
	87 (23.6)	2007
	17 (4.6)	2007
(2003–2011)	7 (1.9)	2006
	3 (0.8)	2010
(2003–2011)	6 (1.6)	2011
	1 (0.3)	2004
	5 (1.4)	2011
	27 (7.3)	2006
	10 (2.7)	2008
	5 (1.4)	2009
Overall	369 (100)	2007

a Formerly Journal of Cultural and Evolutionary Psychology .

b Targeted search only; for all other journals, all abstracts read.

Number of published papers identified by year over the study period (a) by disciplinary affiliation of first author; (b) by type of study population (other = agriculturalist, pastoralist, horticulturalist, or multiple types); (c) by tripartite classification of topic.

In terms of type of population studied, 80 papers (21.7%) contained some data from foragers, broadly defined to include any subsistence population for whom foraging forms a substantial part of the diet. One hundred and forty-five papers (39.3%) contained data from industrialized populations. The remainder of papers studied either contemporary or historical agricultural, horticultural, and pastoral populations. As Figure 1b shows, the amount of work on industrialized populations has tended to increase over time, with 22 such papers in 2000–2002 (29.3% of total) and 58 in 2009–2011 (43.0%). By contrast, the amount of work on forager populations is much more stable (20 papers [26.7%] in 2000–2002, 27 papers [20.0%] in 2009–2011). As for topic, we classified 64.8% of our papers as concerning reproduction, with 9.5% concerning production and 13.3% distribution. The remaining 12.5% either spanned several topics or fit none of the 3 categories. Table 2 gives some examples of popular research questions addressed in each of the 3 topic areas. The preponderance of reproduction has increased over time ( Figure 1c ); in 2000–2002, 53.3% of the papers fell into this category, whereas by 2009–2011, it was 68.9%. In fact, the growth of HBE papers during the study period has been completely driven by an increase in papers on reproductive topics (see Supplementary material ). We classified papers according to whether they involved analysis of secondary data sets gathered for other purposes. The number of papers involving such secondary analysis increased sharply through the study period, whereas those involving primary data did not (see Supplementary material ). Comparative analyses also increased significantly over time, but not faster than the overall growth in paper numbers.

Some examples of popular research questions in our database of recent HBE papers

Topic .	Question .	Example references .
Production	When and why do men and women favor different productive tasks?	Bliege Bird et al. (2009); Codding et al. (2011); Hilton and Greaves (2008); Pacheco-Cobos et al. (2010); Panter-Brick (2002)
	How does the way people use their time change with age and why?	Bock (2002); Gurven and Kaplan (2006); Kramer and Greaves (2011)
	What determines the spatial distribution of human forager groups?	Hamilton et al. (2007)
Distribution	With whom do people share food with and why?	Gurven (2004); Hames and McCabe (2007); Hawkes et al. (2001); Patton (2005); Ziker and Schnegg (2005)
	How do interactions with kin differ from those with nonkin?	Borgerhoff Mulder (2007); Burton-Chellew and Dunbar (2011); Hadley (2004); Næss et al. (2010); Stewart-Williams (2007)
	Why do some societies have more unequal distributions of resources than others?	Borgerhoff Mulder et al. (2009); Gurven et al. (2010); Roth (2000); Shenk et al. (2010)
Reproduction	Why do women sometimes marry polygynously?	Gibson and Mace (2007); Pollet and Nettle (2009)
	What determines how much effort and resources parents invest in a child?	Anderson et al. (2007); Quinlan (2007); Strassmann and Gillespie (2002); Tifferet et al. (2007); Tracer (2009)
	What factors determine the age at which people begin to reproduce?	Bulled and Sosis (2010); Chisholm et al. (2005); Davis and Werre (2008); Migliano et al. (2007)
	Which grandchildren do grandparents favor and why?	Fox et al. (2010); Pashos and McBurney (2008); Sear et al. (2002); Tanskanen et al. (2011); Voland and Beise (2002)

Topic .	Question .	Example references .
Production	When and why do men and women favor different productive tasks?	Bliege Bird et al. (2009); Codding et al. (2011); Hilton and Greaves (2008); Pacheco-Cobos et al. (2010); Panter-Brick (2002)
	How does the way people use their time change with age and why?	Bock (2002); Gurven and Kaplan (2006); Kramer and Greaves (2011)
	What determines the spatial distribution of human forager groups?	Hamilton et al. (2007)
Distribution	With whom do people share food with and why?	Gurven (2004); Hames and McCabe (2007); Hawkes et al. (2001); Patton (2005); Ziker and Schnegg (2005)
	How do interactions with kin differ from those with nonkin?	Borgerhoff Mulder (2007); Burton-Chellew and Dunbar (2011); Hadley (2004); Næss et al. (2010); Stewart-Williams (2007)
	Why do some societies have more unequal distributions of resources than others?	Borgerhoff Mulder et al. (2009); Gurven et al. (2010); Roth (2000); Shenk et al. (2010)
Reproduction	Why do women sometimes marry polygynously?	Gibson and Mace (2007); Pollet and Nettle (2009)
	What determines how much effort and resources parents invest in a child?	Anderson et al. (2007); Quinlan (2007); Strassmann and Gillespie (2002); Tifferet et al. (2007); Tracer (2009)
	What factors determine the age at which people begin to reproduce?	Bulled and Sosis (2010); Chisholm et al. (2005); Davis and Werre (2008); Migliano et al. (2007)
	Which grandchildren do grandparents favor and why?	Fox et al. (2010); Pashos and McBurney (2008); Sear et al. (2002); Tanskanen et al. (2011); Voland and Beise (2002)

To summarize, the data suggest that HBE has changed measurably in the period since 2000. Some of the changes in this period represent continuations of trends already incipient before, such as the expansion away from foraging and foragers toward reproduction and other types of population ( Winterhalder and Smith 2000 ). Our analysis suggests that it is primarily research into the BE of industrialized societies, which has expanded in the subsequent years, such that over 40% of HBE research published in the most recent 3-year period was conducted on such populations. More “traditional” HBE studies of foraging and small-scale food producing societies have continued, but only at a modestly increased rate compared with the 1990s. An unexpected feature of HBE post-2000 is the expansion of HBE in disciplines outside anthropology. Much of the growth has come from the adoption of HBE ideas by researchers based in departments of psychology, and, to a modest extent, other social sciences such as demography, public health, economics, and sociology. This is concomitant with the increasing focus on large-scale industrialized societies, as well as changes in methodology. Anthropologists often work alone or in small teams to gather special-purpose, opportunistic data sets from a particular field site, and many of the pioneering HBE studies were done in this way. In demography, public health, and sociology, by contrast, research tends to be based on very large, systematically collected, representative data sets, such as censuses, cohort, and panel studies, which are designed with multiple purposes in mind. Particular researchers can then interrogate them secondarily to address their particular questions. As HBE has welcomed more researchers from these other social sciences, it has also adopted these secondary methods more strongly (see section “Strengths” for further discussion). We also note the increase in the number of comparative studies. Comparative methods (albeit usually comparing related species rather than populations of the same species) have been a strong feature of BE since the outset (or before, Cullen 1957 ), and thus this is a natural development for HBE. HBE comparative studies use existing cross-cultural databases ( Quinlan 2007 ), integrate multiple ethnographic or historical sources ( Brown et al. 2009 ), or, increasingly, coordinate researchers to collect or derive standardized measures across multiple populations ( Walker et al. 2006 ; Borgerhoff Mulder et al. 2009 ). Comparative studies have become more powerful in their analytical strategies (see section “Strengths”).

The literature review in section “A systematic overview of current research” allowed us to characterize current HBE research and show some of the ways it has changed in the last decade. In this section, we discuss what we see as the strengths, weaknesses, opportunities, and open questions for HBE as a paradigm. This is inevitably more of a personal assessment than the preceding sections, and we appreciate that not everyone in the field will share our views.

The first obvious strength of HBE is vitality . As Darwinians, it comes naturally to us to assume that something that is increasing in frequency has some beneficial features. Thus, the fact that the number of recognizably HBE papers per year found by our search strategy has doubled in a decade, and that there are more and more adopters outside of anthropology, indicates that a range of people find an HBE approach useful. Where does this utility spring from? In part, it is that HBE models tend to make very clear, a priori predictions motivated by theory. The same cannot be said of all other approaches in the human sciences, and, arguably, the more we complicate behavioral ecological models by including details about how proximate mechanisms work, the more this clarity tends to disappear. We return in section “Open questions” to the issue of whether agnosticism about mechanism can be justified, but we note here that a great strength of (and defense for) simple HBE models is that they so often turn out to be empirically fruitful, despite their simplicity. Whether we are considering when to have a first baby ( Nettle 2011 ), what the effects of having an extra child will be in different ecologies ( Lawson and Mace 2011 ), whether to marry polygynously, polyandrously, or monogamously ( Fortunato and Archetti 2010 ; Starkweather and Hames 2012 ), or which relatives to invest time and resources in ( Fox et al. 2010 ), predictions using simple behavioral ecological principles turn out to be useful in making sense of empirically observed diversity in behavior. HBE has also demonstrated the generality of certain principles, such as the fact that male culturally defined social success is positively associated with reproductive success in many different types of society, albeit that the slope of the relationship differs according to features of the social system ( Irons 1979 ; Kaplan and Hill 1985 ; Borgerhoff Mulder 1987 ; Hopcroft 2006 ; Fieder and Huber 2007 ; Nettle and Pollet 2008 ).

A related strength of HBE is its broad scope . HBE models can apply to many kinds of behavioral decision (in principle, all kinds) and in all kinds of society. It is relatively rare in the human sciences for the same set of predictive principles to apply to variation both within and between societies and to societies ranging from small-scale subsistence populations to large-scale industrial states, but HBE thinking about, for example, reproductive decisions has exactly this scope ( Nettle 2011 ; Sear and Coall 2011 ). This would be a strength indeed, even without the crucial additional feature that the explanatory principles invoked are closely related to those that can be applied to species other than our own. Thus, HBE brings a relative conceptual coherence to the study of human behavior, a study that has traditionally been spread across a number of different disciplines each with different conceptual starting points.

Another strength of HBE as we have defined it here is its relatively high ecological validity . Much psychological research into human behavior relies on hypothetical self-reports and self-descriptions, or contrived experimental situations ( Baumeister et al. 2007 ), and much of behavioral economics consists of artificial games whose relevance to actual allocation decisions outwith the laboratory has been questioned ( Levitt and List 2007 ; Bardsley 2008 ; Gurven and Winking 2008 ). Although human behavioral ecologists use such techniques as their purposes require, at the heart of HBE is still a commitment to looking at what people really do, in the environments in which they really live, as a central component of the endeavor. Furthermore, HBE’s focus on behavioral diversity means that it has studied a much wider range of populations than other approaches in the human sciences (see Henrich et al. 2010 ), and this has led to a healthy skepticism of simple generalizations about human universal preferences or motivations ( Brown et al. 2009 ). Measuring relationships between behavior and fitness-relevant outcomes across a broad range of environments, HBE has now amassed considerable evidence in favor of its core assumptions that context matters when studying the adaptive consequences of human behavior and that behavioral diversity arises because the payoffs to alternative behavioral strategies are ecologically contingent.

HBE is also characterized by increasing methodological rigor. The early phases of HBE were defined by exciting theoretical developments, as evolutionary hypotheses for human behavioral variation were first formulated and presented in the literature. However, conducting empirical studies capable of rigorously testing hypotheses derived from HBE theory presents a number of methodological challenges, not least because the human species is relatively long lived and rarely amenable to experimental manipulation. These challenges are now being increasingly overcome, as HBE expands its tool kit to include new sources of data, statistical methods, and study designs. As noted in the section “A systematic overview of current research,” recent years have witnessed an increased use of secondary demographic and social survey data sets, which often provide larger, more representative samples and a broader range of variables than afforded by field research. Some sources of secondary data have also enabled lineages to be tracked beyond the life span of any individual researcher, providing valuable new data on the correlates of long-term fitness (e.g., Lahdenpera et al. 2004 ; Goodman and Koupil 2009 ).

Statistical methods have also become more advanced. Multilevel analyses are now routinely used in HBE research to deal with hierarchically structured data and accurately partition sources of behavioral variance at different levels (e.g., within and between villages; Lamba and Mace 2011 ). Phylogenetic comparative methods, which utilize information on historical relationships between populations, have become popular for testing coevolutionary hypotheses since they were first applied to human populations in the early 1990s ( Mace and Pagel 1994 ; Mace and Holden 2005 ), though debate remains about their suitability for modeling behavioral transmission in humans ( Borgerhoff Mulder et al. 2006 ). Issues of causal inference are also being addressed with more sophisticated analytical techniques. For example, structural equation modeling and longitudinal methods such as event history analysis have enabled researchers to achieve greater confidence when controlling for potential cofounding relationships (e.g., Sear et al. 2002 ; Lawson and Mace 2009 ; Nettle et al. 2011 ). HBE researchers are also following wider trends in the social and natural sciences by exploring alternatives to classic significance testing, such as information-theoretic and Bayesian approaches for considering competing hypotheses ( Towner and Luttbeg 2007 ). Some researchers have also been able to harness “natural experiments” in situations where comparable populations or individuals are selectively exposed to socioecological change. For example, Gibson and Gurmu (2011) examined the effect of changes in land tenure (from family inheritance to government redistribution) on a population in rural Ethiopia, demonstrating that competition between siblings for marital and reproductive success only occurs when land is inherited across generations. These advancements represent an exciting and necessary step forward, as empirical methods “catch up” with the powerful theoretical framework set out in the early days of HBE.

Finally, HBE has shown itself capable of topical innovation. A pertinent recent example is cooperative breeding (typically loosely defined in HBE as the system whereby women receive help from other individuals in raising their offspring). The idea that human females might breed cooperatively had been around for several decades ( Williams 1957 ), and began to be tested empirically in the late 1980s and 1990s (e.g., Hill and Hurtado 1991 ), but it was the 21st century that saw a real upsurge in interest in this topic, leading to a revitalization of the study of kinship in humans ( Shenk and Mattison 2011 ). HBE has now mined many of the rich demographic databases available for our species to test empirically the hypothesis that the presence of other kin members is associated with reproductive outcomes such as child survival rates and fertility rates. These analyses typically find support for the hypothesis that women adopt a flexible cooperative breeding strategy where they corral help variously from the fathers of their children, other men, and pre- and postreproductive women ( Hrdy 2009 ).

Though we see HBE as a strong paradigm, there are some important weaknesses of its current research to be noted. The first is HBE’s relative isolation from the rest of BE. The core journals of BE are Behavioral Ecology and Behavioral Ecology and Sociobiology . Our search revealed only 8 HBE papers in these journals (2.2% of the sample). The vast majority of papers in our sample appeared in journals which never carry studies of species other than humans, and we know of rather few human behavioral ecologists who also work on other systems. West et al. (2011) have recently argued that evolutionary concepts are widely misapplied (or outdated understandings are applied, a phenomenon colloquially dubbed “the disco problem”) in human research, due to insufficient active integration between HBE and the rest of evolutionary biology.

HBE is clearly not completely decoupled from the rest of BE (see Machery and Cohen 2012 for quantitative evidence on this point). For example, within BE, there has been a decline in interest in foraging theory and a rise in interest in sexual selection ( Owens 2006 ), which are mirrored in the changes in HBE described in section “A systematic overview of current research.” Behavioral ecologists have also become less concerned with simply showing that animals make adaptive decisions, and more concerned with the nature of the neurobiological and genetic mechanisms underlying this ( Owens 2006 ). Parallel developments have occurred in the human literature, with the rise of adaptive studies of psychological mechanisms (see e.g., Buss 1995 ). Our search strategy did not include these studies, because their methodologies are different from those of “classical” HBE, but there is no doubt that they have increased in number. Finally, we note that there has been a recent increase in interest in measuring natural selection directly in contemporary human populations ( Nettle and Pollet 2008 ; Byars et al. 2010 ; Stearns et al. 2010 ; Milot et al. 2011 ; Courtiol et al. 2012 ). This anchors HBE much more strongly to evolutionary biology in general. Despite these developments, we see the isolation of HBE from the rest of biology as a potential risk. We hope to see more behavioral ecologists start to work on humans, and more projects across taxonomic boundaries, in the future.

Finally, we note the rather restricted topic base. HBE has had a great deal to say recently about mating strategies, reproductive decisions, fertility, and reproductive success, but much less about diet, resource extraction, resource storage, navigation, spatial patterns of habitat use, hygiene, social coordination, or the many other elements involved in staying alive. In part, this is because, as HBE expands to focus more on large-scale populations, it discovers that there are already disciplines (economics, sociology, human geography, public health) that deal extensively with these topics. It is in the general area of reproduction that it is easiest to come up with predictions that are obviously Darwinian and differentiate HBE from existing social science approaches. Nonetheless, the explanatory strategy of HBE is of potential use for any topic where behavioral effort has to be allocated in one way rather than another, and thus we would hope to see a broadening of the range of questions addressed as HBE continues to grow.

Opportunities

As HBE continues to expand, we see a major opportunity for HBE to build bridges to the social sciences. At the moment, most HBE papers are published in journals that only carry papers that take an adaptive evolutionary perspective, not general social science journals. Thus, HBE is possibly as separated from other approaches to human behavior as it is from parallel approaches to the behavior of other species. This may be because early proponents of HBE saw it as radically different from existing social science approaches to the same problems, by virtue of its generalizing hypothetico-deductive framework and commitment to quantitative hypothesis testing ( Winterhalder and Smith 2000 ). However, the social science those authors came into closest contact with was sociocultural anthropology, which is perhaps not a very typical social science (see Irons 2000 for an account of the hostile reception of HBE within sociocultural anthropology). As HBE’s expansion brings it into closer proximity with disciplines like economics, sociology, demography, public health, development studies, and political science, there may be more common ground than was previously thought. Social scientists are united in the notion that human behavior is very variable and that context is extremely important in giving rise to this variation. These are commitments that HBE obviously shares. Indeed, although it is still common in the human sciences for authors to rhetorically oppose “evolutionary” to “nonevolutionary” (or “social” and “biological”) explanations of the same problem as if these were mutually exclusive endeavors ( Nettle 2009a ), HBE defies such dichotomies adeptly.

Much of social science is highly quantitative and, generally lacking the ability to perform true experiments, relies on multivariate statistical approaches applied to observational data sets to test between competing explanations for behavior patterns. HBE is just the same, and indeed, since the millennium, has become much more closely allied to other social sciences, adopting the large-scale data resources they provide, as well as methodological tools like multilevel modeling, which they have developed to deal with these. HBE employs a priori models based on the individual as maximizer, a position not shared explicitly by all social sciences. However, this approach is widespread in economics and political science. Indeed, it was economics that gave it to BE. The big difference between HBE and much of social science is the explicit invocation of inclusive fitness (or its proxies) as the end to which behavior is deployed. This does not necessarily make it a competing endeavor, especially because what is measured in HBE is not usually fitness itself, but more immediate proxies. Rather, HBE models can often be seen as adding an explicitly ultimate layer of explanation, giving rise to new predictions and unifying diverse empirical observations, without being incompatible with existing, more proximate theories.

Indeed, our perception is that a number of social science theories make assumptions about the ends of behavior, which are quite similar to those of HBE, just not explicitly expressed in Darwinian terms; basically, people’s sets of choices are constrained by the environment in which they have to live, and they make the best choices they can given these constraints, often with knock-on effects that behavioral ecologists would describe as trade-offs. Examples include the work of Geronimus on how African American women adjust their patterns of childbearing to the prevailing rates of mortality and morbidity in their neighborhoods ( Geronimus et al. 1999 ), the work of Drewnowski and colleagues on how people adjust the type of foodstuffs they consume to the budgets they have to spend ( Drewnowski and Specter 2004 ; Drewnowski et al. 2007 ), or Downey’s work on the effects of increasing family size on socioeconomic outcomes of the children ( Downey 2001 ). If the introductory sections of any of these papers were written from a more explicitly Darwinian perspective, they would look perfectly at home in a BE journal. The breaking down of the social science–natural science divide has long been held as desirable, but is not easy to achieve in practice. HBE’s boundary with the social sciences may be one frontier where some progress can occur. Social scientists have long lamented the fragmentation of their field into multiple disciplinary areas with little common ground (e.g., Davis 1994 ). Given HBE’s broad scope and general principles, it has the potential to serve as something of a lingua franca across social scientists working on different kinds of problems.

A related opportunity for HBE is the potential for applied impact . HBE models have the potential to provide new and practical insights into contemporary world issues, from natural resource management ( Tucker 2007 ) to the consequences of inequality within developed populations ( Nettle 2010 ). The causes and consequences of recent human behavioral and environmental changes (including urbanization, economic development, and population growth) are recurring themes in recent studies in HBE. The utility of an ecological approach is clearly demonstrated in studies exploring the effectiveness of public policies or intervention schemes seeking to change human behavior or environments. HBE models clarify that human behavior tends to be deployed in the service of reproductive success, not financial prudence, health, personal or societal wellbeing ( Hill 1993 ), an important insight that differs from some economic or psychological theories. By providing insights into ultimate motivations and proximate pathways to human behavioral change, HBE studies can sometimes offer direct recommendations for the design and implementation of future initiatives ( Gibson and Mace 2006 ; Shenk 2007 ; Gibson and Gurmu 2011 ). Addressing contemporary world issues does, however, present methodological and theoretical challenges for HBE, requiring more explicit consideration of how research insights may be translated into interventions and communicated to policymakers and users ( Tucker and Taylor 2007 ).

Open questions

An open question for HBE is how the study of mechanism can be integrated into functional enquiry. This is an issue for BE generally, not just the human case. As mentioned in the section “What is HBE?”, BE has tended to proceed by the behavioral gambit—the assumption that the nature of the proximate mechanisms underlying behavioral decisions is not important in theorizing about the functions of behavior. It is important to understand the status of the behavioral gambit because it has sometimes been unfairly criticized (see Parker and Maynard Smith 1990 ). In the natural world, individuals do not always behave optimally with respect to any particular decision because there are phylogenetic or mechanistic constraints on their ability to reach adaptive solutions. However, in general terms, the only way to discover the existence of such departures from optimality is to have a theoretical model that shows what the optimal behavior would be and to test empirically whether individual behavior shows the predicted pattern. Where it does not, this may point to unappreciated constraints or trade-offs and thus shed light on the biology of the organism under study. Thus, the use of the term gambit is entirely apt; the behavioral gambit is a way of opening the enquiry designed to gain some advantage in the quest to understand. It is not the end game.

Where there is no sizable departure from predicted optimality, the ultimate adaptive explanation does not depend critically on understanding the mechanisms. This does not mean the question of mechanism is unimportant, of course; mechanistic explanations must still be sought and integrated with functional ones. This is beginning to occur in some cases. In the field of human reproductive ecology, the physiological mechanisms involved in adaptive strategies are beginning to be understood ( Kuzawa et al. 2009 ; Flinn et al. 2011 ), and there is also increasing interchange between HBE researchers and experimentalists studying psychological mechanisms ( Sear et al. 2007 ), which is clearly a development to be welcomed.

Where there is a patterned departure from optimality, understanding the mechanism becomes more critical. Aspects of mechanism can then be modeled as additional constraints, which may explain the strategies individuals pursue. For example, Kacelnik and Bateson (1996) showed that the pattern of risk aversion for variability in food amount and risk proneness for variability in food delay is not predicted by optimal foraging theory, except when Weber’s law (the principle that perceptions of stimulus magnitude are logarithmically, not linearly, related to actual stimulus magnitude) is incorporated into models as a mechanistic constraint. At a deeper level, though, this just raises further questions. Why should Weber’s law have evolved, and once it has evolved, can selection relax it for any particular task? These are what McNamara and Houston call “evo-mecho” questions ( McNamara and Houston 2009 ). Departures from optimality in one particular context raise such questions pervasively. Issues such as the robustness, neural instantiability, efficiency, and developmental cost of different kinds of mechanisms become salient here, and many apparently irrational quirks of behavior become interpretable as side effects of evolved mechanisms whose overall benefits have exceeded their costs over evolutionary time ( Fawcett et al. 2012 ). However, we would still argue that the best first approximation in understanding a question is to employ the behavioral gambit to generate and test simple optimality predictions, even though an understanding of mechanism will be essential for explaining why these may fail.

Although the issue of how incorporation of mechanism changes the predictions of BE models is a general one, in the human case, it has been discussed in particular with reference to transmitted culture because this is a class of mechanism on which humans are reliant to a unique extent ( Richerson and Boyd 2005 ). Transmitted culture refers to the behavioral traditions that arise from repeated social learning. Social learning can be an evolutionarily adaptive strategy, and the equilibrium solutions reached by it will often be the fitness-maximizing ones under reasonable assumptions ( Henrich and McElreath 2003 ). After all, if reliance on culture on average led to maladaptive outcomes, there would be strong selection on humans to rely on it less. Indeed, there is evidence that humans tend to forage efficiently for socially acquired information, using it when it is adaptive to do so ( Morgan et al. 2012 ). Thus, we would argue that culture can be treated, to a first approximation, just like any other proximate mechanism: that is, it can be set aside in the initial formulation of functional explanations ( Scott-Phillips et al. 2011 , though see Laland et al. 2011 for a different view). As an example, we could take Henrich and Henrich’s (2010) data on food taboos for pregnant and lactating women in Fiji. These authors show that the taboos reduce women’s chances of fish poisoning by 30% during pregnancy and 60% during breastfeeding and thus are plausibly adaptive. The fact that in this case it is culture by which women acquire them, rather than genes or individual learning, does not affect this conclusion or the data needed to test it. However, the quirks of how human social learning works may well explain some nonadaptive taboos that are found alongside the adaptive ones, which are in effect carried along by the generally adaptive reliance on social learning. Thus, although the behavioral gambit can be used to explain the major adaptive features of these taboos, an understanding of the cultural mechanisms is required to explain the details of how the observed behavior departs in subtle ways from the optimal pattern. Culture may often lead to maladaptive side effects in this way ( Richerson and Boyd 2005 ). Although its general effect is to allow humans to rapidly reach adaptive equilibria, nonadaptive traits can be carried along by it, and, compared with other proximate mechanisms, it produces very different dynamics of adaptive change.

A final open question is the extent of human maladaptation. Humans have increased their absolute numbers by orders of magnitude and colonized all major habitats of the planet, so they are clearly adept at finding adaptive solutions to the problem of living. However, there are also some clear cases of quite systematic departures from adaptive behavior. Perhaps most pertinently, the low fertility rate typical of industrial populations still defies a convincing adaptive explanation, despite being a longstanding topic for HBE research (see Borgerhoff Mulder 1998 ; Kaplan et al. 2002 ; Shenk 2009 ). There are patterns in the fertility of modernizing populations, which can be readily understood from an HBE perspective: parents in industrialized populations who have large families suffer a cost to the quality of their offspring, particularly with regard to educational achievement and adult socioeconomic success, so there is a quality–quantity trade-off ( Lawson and Mace 2011 ). Moreover, the reduction in fertility rate is closely associated with improvement in the survival of offspring to breed themselves, so that, as the transition to small families proceeds, the probability of having at least one grandchild may remain roughly constant ( Liu and Lummaa 2011 ). However, despite all this, it remains the case that people in affluent societies could still have many more grandchildren and great-grandchildren by having more children, and yet they do not ( Goodman et al. 2012 ). Any explanation of the demographic transition must, therefore, invoke some kind of maladaptation or mismatch between the conditions under which decision-making mechanisms evolved and those under which they are now operating.

Our review has shown that HBE is a growing and rapidly developing research area. The weaknesses of HBE mostly amount to a need for more research activity, and the unresolved questions, though important, do not in our view undermine HBE’s core strengths of theoretical coherence and empirical utility. HBE is being applied to more questions in more human populations with better methods than ever before. Our hope is that HBE will inspire more behavioral biologists to work on humans, for whom a wealth of data is available, and more social scientists to adopt an adaptive, ecological perspective on their behavioral questions, thus adding a layer of deeper explanations, as well as generating new insights.

Supplementary material can be found at Supplementary Data

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Author notes

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OPINION article

Challenges and opportunities for human behavior research in the coronavirus disease (covid-19) pandemic.

$\nClaudio Gentili$

1 Department of General Psychology, University of Padova, Padua, Italy
2 Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy

The COVID-19 pandemic is a serious public health crisis that is causing major worldwide disruption. So far, the most widely deployed interventions have been non-pharmacological (NPI), such as various forms of social distancing, pervasive use of personal protective equipment (PPE), such as facemasks, shields, or gloves, and hand washing and disinfection of fomites. These measures will very likely continue to be mandated in the medium or even long term until an effective treatment or vaccine is found ( Leung et al., 2020 ). Even beyond that time frame, many of these public health recommendations will have become part of individual lifestyles and hence continue to be observed. Moreover, it is implausible that the disruption caused by COVID-19 will dissipate soon. Analysis of transmission dynamics suggests that the disease could persist into 2025, with prolonged or intermittent social distancing in place until 2022 ( Kissler et al., 2020 ).

Human behavior research will be profoundly impacted beyond the stagnation resulting from the closure of laboratories during government-mandated lockdowns. In this viewpoint article, we argue that disruption provides an important opportunity for accelerating structural reforms already underway to reduce waste in planning, conducting, and reporting research ( Cristea and Naudet, 2019 ). We discuss three aspects relevant to human behavior research: (1) unavoidable, extensive changes in data collection and ensuing untoward consequences; (2) the possibility of shifting research priorities to aspects relevant to the pandemic; (3) recommendations to enhance adaptation to the disruption caused by the pandemic.

Data collection is very unlikely to return to the “old” normal for the foreseeable future. For example, neuroimaging studies usually involve placing participants in the confined space of a magnetic resonance imaging scanner. Studies measuring stress hormones, electroencephalography, or psychophysiology also involve close contact to collect saliva and blood samples or to place electrodes. Behavioral studies often involve interaction with persons who administer tasks or require that various surfaces and materials be touched. One immediate solution would be conducting “socially distant” experiments, for instance, by keeping a safe distance and making participants and research personnel wear PPE. Though data collection in this way would resemble pre-COVID times, it would come with a range of unintended consequences ( Table 1 ). First, it would significantly augment costs in terms of resources, training of personnel, and time spent preparing experiments. For laboratories or researchers with scarce resources, these costs could amount to a drastic reduction in the experiments performed, with an ensuing decrease in publication output, which might further affect the capacity to attract new funding and retain researchers. Secondly, even with the use of PPE, some participants might be reluctant or anxious to expose themselves to close and unnecessary physical interaction. Participants with particular vulnerabilities, like neuroticism, social anxiety, or obsessive-compulsive traits, might find the trade-off between risks, and gains unacceptable. Thirdly, some research topics (e.g., face processing, imitation, emotional expression, dyadic interaction) or study populations (e.g., autistic spectrum, social anxiety, obsessive-compulsive) would become difficult to study with the current experimental paradigms ( Table 1 ). New paradigms can be developed, but they will need to first be assessed for reliability and validated, which will undoubtedly take time. Finally, generalized use of PPE by participants and personnel could alter the “usual” experimental setting, introducing additional biases, similarly to the experimenter effect ( Rosenthal, 1976 ).

Table 1 . Possible consequences of non-pharmacological interventions for COVID-19 on human behavior research.

Data collection could also adapt by leveraging technology, such as running experiments remotely via available platforms, like for instance Amazon's Mechanical Turk (MTurk), where any task that programmable with standard browser technology can be used ( Crump et al., 2013 ). Templates of already-programmed and easily customizable experimental tasks, such as the Stroop or Balloon Analog Risk Task, are also available on platforms like Pavlovia. Ecological momentary assessment is another feasible option, since it was conceived from the beginning for remote use, with participants logging in to fill in scales or activity journals in a naturalistic environment ( Shiffman et al., 2008 ). Increasingly affordable wearables can be used for collecting physiological data ( Javelot et al., 2014 ). Web-based research was already expanding before the pandemic, and the quality of the data collected in this way is comparable with that of laboratory studies ( Germine et al., 2012 ). Still, there are lingering issues. For instance, for some MTurk experiments, disparities have been evidenced between laboratory and online data collection ( Crump et al., 2013 ). Further clarifications about quality, such as consistency or interpretability ( Abdolkhani et al., 2020 ), are also needed for data collected using wearables.

Beyond updating data collection practices, a significant portion of human behavior research might change course to focus on the effects of the pandemic. For example, the incidence of mental disorders or of negative effects on psychological and physical well-being, particularly across populations of interest (e.g., recovered patients, caregivers, and healthcare workers), are crucial areas of inquiry. Many researchers might feel hard-pressed to not miss out on studying this critical period and embark on hastily planned and conducted studies. Multiplication and fragmentation of efforts are likely, for instance, by conducting highly overlapping surveys in widely accessible and oversampled populations (e.g., university students). Moreover, rushed planning is bound to lead to taking shortcuts and cutting corners in study design and conduct, e.g., skipping pre-registration or even ethical committee approval or using not validated measurement tools, like ad hoc surveys. Surveys using non-probability and convenience samples, especially for social and mental health problems, frequently produce biased and misleading findings, particularly for estimates of prevalence ( Pierce et al., 2020 ). A significant portion of human behavior research that re-oriented itself to study the pandemic could result in to a heap of non-reproducible, unreliable, or overlapping findings.

Human behavior studies could also aim to inform the planning and enforcement of public health responses in the pandemic. Behavioral scientists might focus on finding and testing ways to increase adherence to NPIs or to lessen the negative effects of isolation, particularly in vulnerable groups, e.g., the elderly or the chronically ill and their caretakers. Studies could also attempt to elucidate factors that make individuals uncollaborative with recommendations from public health authorities. Though all of these topics are important, important caveats must be considered. Psychology and neuroscience have been affected by a crisis in reproducibility and credibility, with several established findings proving unreliable and even non-reproducible ( Button et al., 2013 ; Open Science Collaboration, 2015 ). It is crucial to ensure that only robust and reproducible results are applied or even proposed in the context of a serious public health crisis. For instance, the possible influence of psychological factors on susceptibility to infection and potential psychological interventions to address them could be interesting topics. However, the existing literature is marked by inconsistency, heterogeneity, reverse causality, or other biases ( Falagas et al., 2010 ). Even for robust and reproducible findings, translation is doubtful, particularly when these are based on convenience samples or on simplified and largely artificial experimental contexts. For example, the scarcity of medical resources (e.g., N-95 masks, drugs, or ventilators) in a pandemic with its unavoidable ethical conundrum about allocation principles and triage might appeal to moral reasoning researchers. Even assuming, implausibly, that most of the existent research in this area is robust, translation to dramatic real-life situations and highly specialized contexts, such as intensive care, would be difficult and error-prone. Translation might not even be useful, given that comprehensive ethical guidance and decision rules to support medical professionals already exist ( Emanuel et al., 2020 ).

The COVID-19 pandemic and the corresponding global public health response pose significant and lasting difficulties for human behavior research. In many contexts, such as laboratories with limited resources and uncertain funding, challenges will lead to a reduced research output, which might have further domino effects on securing funding and retaining researchers. As a remedy, modifying data collection practices is useful but insufficient. Conversely, adaptation might require the implementation of radical changes—producing less research but of higher quality and more utility ( Cristea and Naudet, 2019 ). To this purpose, we advocate for the acceleration and generalization of proposed structural reforms (i.e., “open science”) in how research is planned, conducted, and reported ( Munafò et al., 2017 ; Cristea and Naudet, 2019 ) and summarize six key recommendations.

First, a definitive move from atomized and fragmented experimental research to large-scale collaboration should be encouraged through incentives from funders and academic institutions alike. In the current status quo, interdisciplinary research has systematically lower odds of being funded ( Bromham et al., 2016 ). Conversely, funders could favor top-down funding on topics of prominent interest and encourage large consortia with international representativity and interdisciplinarity over bottom-up funding for a select number of excellent individual investigators. Second, particularly for research focused on the pandemic, relevant priorities need to be identified before conducting studies. This can be achieved through assessing the concrete needs of the populations targeted (e.g., healthcare workers, families of victims, individuals suffering from isolation, disabilities, pre-existing physical and mental health issues, and the economically vulnerable) and subsequently conducting systematic reviews so as to avoid fragmentation and overlap. To this purpose, journals could require that some reports of primary research also include rapid reviews ( Tricco et al., 2015 ), a simplified form of systematic reviews. For instance, The Lancet journals require a “Research in context” box, which needs to be based on a systematic search. Study formats like Registered Reports, in which a study is accepted in principle after peer review of its rationale and methods ( Hardwicke and Ioannidis, 2018 ), are uniquely suited for this change. Third, methodological rigor and reproducibility in design, conduct, analysis, and reporting should move to the forefront of the human behavior research agenda ( Cristea and Naudet, 2019 ). For example, preregistration of studies ( Nosek et al., 2019 ) in a public repository should be widely employed to support transparent reporting. Registered reports ( Hardwicke and Ioannidis, 2018 ) and study protocols are formats that ensure rigorous evaluation of the experimental design and statistical analysis plan before commencing data collection, thus making sure shortcuts and methodological shortcomings are eliminated. Fourth, data and code sharing, along with the use of publicly available datasets (e.g., 1000 Functional Connectomes Project, Human Connectome Project), should become the norm. These practices allow the use of already-collected data to be maximized, including in terms of assessing reproducibility, conducting re-analyses using different methods, and exploring new hypotheses on large collections of data ( Cristea and Naudet, 2019 ). Fifth, to reduce publication bias, submission of all unpublished studies, the so-called “file drawer,” should be encouraged and supported. Reporting findings in preprints can aid this desideratum, but stronger incentives are necessary to ensure that preprints also transparently and completely report conducted research. The Preprint Review at eLife ( Elife, 2020 ), in which the journal effectively takes into review manuscripts posted on the preprint server BioRxiv, is a promising initiative in this direction. Journals could also create study formats specifically designed for publishing studies that resulted in inconclusive findings, even when caused by procedural issues, e.g., unclear manipulation checks, insufficient stimulus presentation times, or other technical errors. This would both aid transparency and help other researchers better prepare their own experiments. Sixth, peer review of both articles and preprints should be regarded as on par with the production of new research. Platforms like Publons help track reviewing activity, which could be rewarded by funders and academic institutions involved in hiring, promotion, or tenure ( Moher et al., 2018 ). Researchers who manage to publish less during the pandemic could still be compensated for the onerous activity of peer review, to the benefit of the entire community.

Of course, individual researchers cannot implement such sweeping changes on their own, without decisive action from policymakers like funding bodies, academic institutions, and journals. For instance, decisions related to hiring, promotion, or tenure of academics could reward several of the behaviors described, such as complete and transparent publication regardless of the results, availability of data and code, or contributions to peer review ( Moher et al., 2018 ). Academic institutions and funders should acknowledge the slowdown of experimental research during the pandemic and hence accelerate the move toward more “responsible indicators” that would incentivize best publication practices over productivity and citations ( Moher et al., 2018 ). Funders could encourage submissions leveraging existing datasets or developing tools for data re-use, e.g., to track multiple uses of the same dataset. Journals could stimulate data sharing by assigning priority to manuscripts sharing or re-using data and code, like re-analyses, or individual participant data meta-analyses.

Author Contributions

CG and IC contributed equally to this manuscript in terms of its conceivement and preparation. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

This work was carried out within the scope of the project “use-inspired basic research”, for which the Department of General Psychology of the University of Padova has been recognized as “Dipartimento di eccellenza” by the Ministry of University and Research.

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Keywords: open science, data sharing, social distancing, preprint, preregistration, coronavirus disease, neuroimaging, experimental psychology

Citation: Gentili C and Cristea IA (2020) Challenges and Opportunities for Human Behavior Research in the Coronavirus Disease (COVID-19) Pandemic. Front. Psychol. 11:1786. doi: 10.3389/fpsyg.2020.01786

Received: 29 April 2020; Accepted: 29 June 2020; Published: 10 July 2020.

Reviewed by:

Copyright © 2020 Gentili and Cristea. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Claudio Gentili, c.gentili@unipd.it

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Pro-ecological consumer behavior versus energy reduction and sustainable consumption: a case from poland.

1. Introduction

2. literature review, 3. materials and methods, 4. results and discussion.

X 1 —I try to buy environmentally friendly products that do not contain harmful substances and preservatives (e.g., with an ecological label),
X 2 —I try to buy energy- and water-saving products,
X 3 —I try to buy products either in glass or paper packaging instead of plastic,
X 4 —Where possible, I pack products into my own bags, boxes, and jars,
X 5 —I try not to buy food products in too large packages, which I cannot use within the expiration date,
X 6 —I try to limit buying new products if I can still use those that I possess,
X 7 —If I have a choice between two similar products with a comparable price, I try to choose the more ecological option,
X 8 —I try to reduce consumer waste in the household.

5. Conclusions

Author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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	y	X	X	X	X	X	X	X	X
y	1.00	0.36	0.36	0.33	0.34	0.41	0.49	0.35	0.43
X	0.36	1.00	0.62	0.66	0.55	0.49	0.49	0.70	0.59
X	0.36	0.62	1.00	0.58	0.48	0.49	0.50	0.57	0.58
X	0.33	0.66	0.58	1.00	0.53	0.46	0.43	0.63	0.57
X	0.34	0.55	0.48	0.53	1.00	0.42	0.51	0.50	0.57
X	0.41	0.49	0.49	0.46	0.42	1.00	0.57	0.51	0.56
X	0.49	0.49	0.50	0.43	0.51	0.57	1.00	0.51	0.62
X	0.35	0.70	0.57	0.63	0.50	0.51	0.51	1.00	0.56
X	0.43	0.59	0.58	0.57	0.57	0.56	0.62	0.56	1.00

Variable	Description of the Variable	Relative Weights	Share of Relative Weight (%)
Z	I try not to buy food products in too large packages, which I cannot use within the expiration date.	0.561192	24.95
Z	I try to limit buying new products if I can still use those that I possess.	0.498308	22.15
Z	I try to buy energy- and water-saving products.	0.417736	18.57
Z	I try to reduce consumer waste in the household.	0.249116	11.08
Z	I try to buy products either in glass or paper packaging instead of plastic.	0.188661	8.39
Z	I try to buy environmentally friendly products that do not contain harmful substances and preservatives (e.g., with an ecological label),	0.154555	6.87
Z	Where possible, I pack products into my own bags, boxes and jars.	0.137689	6.12
Z	If I have a choice between two similar products with a comparable price, I try to choose the more ecological option.	0.042000	1.87

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Share and Cite

Wyrzykowska, B.; Czech, A.; Dąbrowska, A.; Rytko, A. Pro-Ecological Consumer Behavior versus Energy Reduction and Sustainable Consumption: A Case from Poland. Sustainability 2024 , 16 , 7556. https://doi.org/10.3390/su16177556

Wyrzykowska B, Czech A, Dąbrowska A, Rytko A. Pro-Ecological Consumer Behavior versus Energy Reduction and Sustainable Consumption: A Case from Poland. Sustainability . 2024; 16(17):7556. https://doi.org/10.3390/su16177556

Wyrzykowska, Barbara, Artur Czech, Anna Dąbrowska, and Anna Rytko. 2024. "Pro-Ecological Consumer Behavior versus Energy Reduction and Sustainable Consumption: A Case from Poland" Sustainability 16, no. 17: 7556. https://doi.org/10.3390/su16177556

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Published: 31 August 2024

Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023

Xianru Shang ORCID: orcid.org/0009-0000-8906-3216 1 ,
Zijian Liu 1 ,
Chen Gong 1 ,
Zhigang Hu 1 ,
Yuexuan Wu 1 &
Chengliang Wang ORCID: orcid.org/0000-0003-2208-3508 2

Humanities and Social Sciences Communications volume 11 , Article number: 1115 ( 2024 ) Cite this article

Metrics details

Science, technology and society

The rapid expansion of information technology and the intensification of population aging are two prominent features of contemporary societal development. Investigating older adults’ acceptance and use of technology is key to facilitating their integration into an information-driven society. Given this context, the technology acceptance of older adults has emerged as a prioritized research topic, attracting widespread attention in the academic community. However, existing research remains fragmented and lacks a systematic framework. To address this gap, we employed bibliometric methods, utilizing the Web of Science Core Collection to conduct a comprehensive review of literature on older adults’ technology acceptance from 2013 to 2023. Utilizing VOSviewer and CiteSpace for data assessment and visualization, we created knowledge mappings of research on older adults’ technology acceptance. Our study employed multidimensional methods such as co-occurrence analysis, clustering, and burst analysis to: (1) reveal research dynamics, key journals, and domains in this field; (2) identify leading countries, their collaborative networks, and core research institutions and authors; (3) recognize the foundational knowledge system centered on theoretical model deepening, emerging technology applications, and research methods and evaluation, uncovering seminal literature and observing a shift from early theoretical and influential factor analyses to empirical studies focusing on individual factors and emerging technologies; (4) moreover, current research hotspots are primarily in the areas of factors influencing technology adoption, human-robot interaction experiences, mobile health management, and aging-in-place technology, highlighting the evolutionary context and quality distribution of research themes. Finally, we recommend that future research should deeply explore improvements in theoretical models, long-term usage, and user experience evaluation. Overall, this study presents a clear framework of existing research in the field of older adults’ technology acceptance, providing an important reference for future theoretical exploration and innovative applications.

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Introduction.

In contemporary society, the rapid development of information technology has been intricately intertwined with the intensifying trend of population aging. According to the latest United Nations forecast, by 2050, the global population aged 65 and above is expected to reach 1.6 billion, representing about 16% of the total global population (UN 2023 ). Given the significant challenges of global aging, there is increasing evidence that emerging technologies have significant potential to maintain health and independence for older adults in their home and healthcare environments (Barnard et al. 2013 ; Soar 2010 ; Vancea and Solé-Casals 2016 ). This includes, but is not limited to, enhancing residential safety with smart home technologies (Touqeer et al. 2021 ; Wang et al. 2022 ), improving living independence through wearable technologies (Perez et al. 2023 ), and increasing medical accessibility via telehealth services (Kruse et al. 2020 ). Technological innovations are redefining the lifestyles of older adults, encouraging a shift from passive to active participation (González et al. 2012 ; Mostaghel 2016 ). Nevertheless, the effective application and dissemination of technology still depends on user acceptance and usage intentions (Naseri et al. 2023 ; Wang et al. 2023a ; Xia et al. 2024 ; Yu et al. 2023 ). Particularly, older adults face numerous challenges in accepting and using new technologies. These challenges include not only physical and cognitive limitations but also a lack of technological experience, along with the influences of social and economic factors (Valk et al. 2018 ; Wilson et al. 2021 ).

User acceptance of technology is a significant focus within information systems (IS) research (Dai et al. 2024 ), with several models developed to explain and predict user behavior towards technology usage, including the Technology Acceptance Model (TAM) (Davis 1989 ), TAM2, TAM3, and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al. 2003 ). Older adults, as a group with unique needs, exhibit different behavioral patterns during technology acceptance than other user groups, and these uniquenesses include changes in cognitive abilities, as well as motivations, attitudes, and perceptions of the use of new technologies (Chen and Chan 2011 ). The continual expansion of technology introduces considerable challenges for older adults, rendering the understanding of their technology acceptance a research priority. Thus, conducting in-depth research into older adults’ acceptance of technology is critically important for enhancing their integration into the information society and improving their quality of life through technological advancements.

Reviewing relevant literature to identify research gaps helps further solidify the theoretical foundation of the research topic. However, many existing literature reviews primarily focus on the factors influencing older adults’ acceptance or intentions to use technology. For instance, Ma et al. ( 2021 ) conducted a comprehensive analysis of the determinants of older adults’ behavioral intentions to use technology; Liu et al. ( 2022 ) categorized key variables in studies of older adults’ technology acceptance, noting a shift in focus towards social and emotional factors; Yap et al. ( 2022 ) identified seven categories of antecedents affecting older adults’ use of technology from an analysis of 26 articles, including technological, psychological, social, personal, cost, behavioral, and environmental factors; Schroeder et al. ( 2023 ) extracted 119 influencing factors from 59 articles and further categorized these into six themes covering demographics, health status, and emotional awareness. Additionally, some studies focus on the application of specific technologies, such as Ferguson et al. ( 2021 ), who explored barriers and facilitators to older adults using wearable devices for heart monitoring, and He et al. ( 2022 ) and Baer et al. ( 2022 ), who each conducted in-depth investigations into the acceptance of social assistive robots and mobile nutrition and fitness apps, respectively. In summary, current literature reviews on older adults’ technology acceptance exhibit certain limitations. Due to the interdisciplinary nature and complex knowledge structure of this field, traditional literature reviews often rely on qualitative analysis, based on literature analysis and periodic summaries, which lack sufficient objectivity and comprehensiveness. Additionally, systematic research is relatively limited, lacking a macroscopic description of the research trajectory from a holistic perspective. Over the past decade, research on older adults’ technology acceptance has experienced rapid growth, with a significant increase in literature, necessitating the adoption of new methods to review and examine the developmental trends in this field (Chen 2006 ; Van Eck and Waltman 2010 ). Bibliometric analysis, as an effective quantitative research method, analyzes published literature through visualization, offering a viable approach to extracting patterns and insights from a large volume of papers, and has been widely applied in numerous scientific research fields (Achuthan et al. 2023 ; Liu and Duffy 2023 ). Therefore, this study will employ bibliometric methods to systematically analyze research articles related to older adults’ technology acceptance published in the Web of Science Core Collection from 2013 to 2023, aiming to understand the core issues and evolutionary trends in the field, and to provide valuable references for future related research. Specifically, this study aims to explore and answer the following questions:

RQ1: What are the research dynamics in the field of older adults’ technology acceptance over the past decade? What are the main academic journals and fields that publish studies related to older adults’ technology acceptance?

RQ2: How is the productivity in older adults’ technology acceptance research distributed among countries, institutions, and authors?

RQ3: What are the knowledge base and seminal literature in older adults’ technology acceptance research? How has the research theme progressed?

RQ4: What are the current hot topics and their evolutionary trajectories in older adults’ technology acceptance research? How is the quality of research distributed?

Methodology and materials

Research method.

In recent years, bibliometrics has become one of the crucial methods for analyzing literature reviews and is widely used in disciplinary and industrial intelligence analysis (Jing et al. 2023 ; Lin and Yu 2024a ; Wang et al. 2024a ; Xu et al. 2021 ). Bibliometric software facilitates the visualization analysis of extensive literature data, intuitively displaying the network relationships and evolutionary processes between knowledge units, and revealing the underlying knowledge structure and potential information (Chen et al. 2024 ; López-Robles et al. 2018 ; Wang et al. 2024c ). This method provides new insights into the current status and trends of specific research areas, along with quantitative evidence, thereby enhancing the objectivity and scientific validity of the research conclusions (Chen et al. 2023 ; Geng et al. 2024 ). VOSviewer and CiteSpace are two widely used bibliometric software tools in academia (Pan et al. 2018 ), recognized for their robust functionalities based on the JAVA platform. Although each has its unique features, combining these two software tools effectively constructs mapping relationships between literature knowledge units and clearly displays the macrostructure of the knowledge domains. Particularly, VOSviewer, with its excellent graphical representation capabilities, serves as an ideal tool for handling large datasets and precisely identifying the focal points and hotspots of research topics. Therefore, this study utilizes VOSviewer (version 1.6.19) and CiteSpace (version 6.1.R6), combined with in-depth literature analysis, to comprehensively examine and interpret the research theme of older adults’ technology acceptance through an integrated application of quantitative and qualitative methods.

Data source

Web of Science is a comprehensively recognized database in academia, featuring literature that has undergone rigorous peer review and editorial scrutiny (Lin and Yu 2024b ; Mongeon and Paul-Hus 2016 ; Pranckutė 2021 ). This study utilizes the Web of Science Core Collection as its data source, specifically including three major citation indices: Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI). These indices encompass high-quality research literature in the fields of science, social sciences, and arts and humanities, ensuring the comprehensiveness and reliability of the data. We combined “older adults” with “technology acceptance” through thematic search, with the specific search strategy being: TS = (elder OR elderly OR aging OR ageing OR senile OR senior OR old people OR “older adult*”) AND TS = (“technology acceptance” OR “user acceptance” OR “consumer acceptance”). The time span of literature search is from 2013 to 2023, with the types limited to “Article” and “Review” and the language to “English”. Additionally, the search was completed by October 27, 2023, to avoid data discrepancies caused by database updates. The initial search yielded 764 journal articles. Given that searches often retrieve articles that are superficially relevant but actually non-compliant, manual screening post-search was essential to ensure the relevance of the literature (Chen et al. 2024 ). Through manual screening, articles significantly deviating from the research theme were eliminated and rigorously reviewed. Ultimately, this study obtained 500 valid sample articles from the Web of Science Core Collection. The complete PRISMA screening process is illustrated in Fig. 1 .

Presentation of the data culling process in detail.

Data standardization

Raw data exported from databases often contain multiple expressions of the same terminology (Nguyen and Hallinger 2020 ). To ensure the accuracy and consistency of data, it is necessary to standardize the raw data (Strotmann and Zhao 2012 ). This study follows the data standardization process proposed by Taskin and Al ( 2019 ), mainly executing the following operations:

(1) Standardization of author and institution names is conducted to address different name expressions for the same author. For instance, “Chan, Alan Hoi Shou” and “Chan, Alan H. S.” are considered the same author, and distinct authors with the same name are differentiated by adding identifiers. Diverse forms of institutional names are unified to address variations caused by name changes or abbreviations, such as standardizing “FRANKFURT UNIV APPL SCI” and “Frankfurt University of Applied Sciences,” as well as “Chinese University of Hong Kong” and “University of Hong Kong” to consistent names.

(2) Different expressions of journal names are unified. For example, “International Journal of Human-Computer Interaction” and “Int J Hum Comput Interact” are standardized to a single name. This ensures consistency in journal names and prevents misclassification of literature due to differing journal names. Additionally, it involves checking if the journals have undergone name changes in the past decade to prevent any impact on the analysis due to such changes.

(3) Keywords data are cleansed by removing words that do not directly pertain to specific research content (e.g., people, review), merging synonyms (e.g., “UX” and “User Experience,” “aging-in-place” and “aging in place”), and standardizing plural forms of keywords (e.g., “assistive technologies” and “assistive technology,” “social robots” and “social robot”). This reduces redundant information in knowledge mapping.

Bibliometric results and analysis

Distribution power (rq1), literature descriptive statistical analysis.

Table 1 presents a detailed descriptive statistical overview of the literature in the field of older adults’ technology acceptance. After deduplication using the CiteSpace software, this study confirmed a valid sample size of 500 articles. Authored by 1839 researchers, the documents encompass 792 research institutions across 54 countries and are published in 217 different academic journals. As of the search cutoff date, these articles have accumulated 13,829 citations, with an annual average of 1156 citations, and an average of 27.66 citations per article. The h-index, a composite metric of quantity and quality of scientific output (Kamrani et al. 2021 ), reached 60 in this study.

Trends in publications and disciplinary distribution

The number of publications and citations are significant indicators of the research field’s development, reflecting its continuity, attention, and impact (Ale Ebrahim et al. 2014 ). The ranking of annual publications and citations in the field of older adults’ technology acceptance studies is presented chronologically in Fig. 2A . The figure shows a clear upward trend in the amount of literature in this field. Between 2013 and 2017, the number of publications increased slowly and decreased in 2018. However, in 2019, the number of publications increased rapidly to 52 and reached a peak of 108 in 2022, which is 6.75 times higher than in 2013. In 2022, the frequency of document citations reached its highest point with 3466 citations, reflecting the widespread recognition and citation of research in this field. Moreover, the curve of the annual number of publications fits a quadratic function, with a goodness-of-fit R 2 of 0.9661, indicating that the number of future publications is expected to increase even more rapidly.

A Trends in trends in annual publications and citations (2013–2023). B Overlay analysis of the distribution of discipline fields.

Figure 2B shows that research on older adults’ technology acceptance involves the integration of multidisciplinary knowledge. According to Web of Science Categories, these 500 articles are distributed across 85 different disciplines. We have tabulated the top ten disciplines by publication volume (Table 2 ), which include Medical Informatics (75 articles, 15.00%), Health Care Sciences & Services (71 articles, 14.20%), Gerontology (61 articles, 12.20%), Public Environmental & Occupational Health (57 articles, 11.40%), and Geriatrics & Gerontology (52 articles, 10.40%), among others. The high output in these disciplines reflects the concentrated global academic interest in this comprehensive research topic. Additionally, interdisciplinary research approaches provide diverse perspectives and a solid theoretical foundation for studies on older adults’ technology acceptance, also paving the way for new research directions.

Knowledge flow analysis

A dual-map overlay is a CiteSpace map superimposed on top of a base map, which shows the interrelationships between journals in different domains, representing the publication and citation activities in each domain (Chen and Leydesdorff 2014 ). The overlay map reveals the link between the citing domain (on the left side) and the cited domain (on the right side), reflecting the knowledge flow of the discipline at the journal level (Leydesdorff and Rafols 2012 ). We utilize the in-built Z-score algorithm of the software to cluster the graph, as shown in Fig. 3 .

The left side shows the citing journal, and the right side shows the cited journal.

Figure 3 shows the distribution of citing journals clusters for older adults’ technology acceptance on the left side, while the right side refers to the main cited journals clusters. Two knowledge flow citation trajectories were obtained; they are presented by the color of the cited regions, and the thickness of these trajectories is proportional to the Z-score scaled frequency of citations (Chen et al. 2014 ). Within the cited regions, the most popular fields with the most records covered are “HEALTH, NURSING, MEDICINE” and “PSYCHOLOGY, EDUCATION, SOCIAL”, and the elliptical aspect ratio of these two fields stands out. Fields have prominent elliptical aspect ratios, highlighting their significant influence on older adults’ technology acceptance research. Additionally, the major citation trajectories originate in these two areas and progress to the frontier research area of “PSYCHOLOGY, EDUCATION, HEALTH”. It is worth noting that the citation trajectory from “PSYCHOLOGY, EDUCATION, SOCIAL” has a significant Z-value (z = 6.81), emphasizing the significance and impact of this development path. In the future, “MATHEMATICS, SYSTEMS, MATHEMATICAL”, “MOLECULAR, BIOLOGY, IMMUNOLOGY”, and “NEUROLOGY, SPORTS, OPHTHALMOLOGY” may become emerging fields. The fields of “MEDICINE, MEDICAL, CLINICAL” may be emerging areas of cutting-edge research.

Main research journals analysis

Table 3 provides statistics for the top ten journals by publication volume in the field of older adults’ technology acceptance. Together, these journals have published 137 articles, accounting for 27.40% of the total publications, indicating that there is no highly concentrated core group of journals in this field, with publications being relatively dispersed. Notably, Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction each lead with 15 publications. In terms of citation metrics, International Journal of Medical Informatics and Computers in Human Behavior stand out significantly, with the former accumulating a total of 1,904 citations, averaging 211.56 citations per article, and the latter totaling 1,449 citations, with an average of 96.60 citations per article. These figures emphasize the academic authority and widespread impact of these journals within the research field.

Research power (RQ2)

Countries and collaborations analysis.

The analysis revealed the global research pattern for country distribution and collaboration (Chen et al. 2019 ). Figure 4A shows the network of national collaborations on older adults’ technology acceptance research. The size of the bubbles represents the amount of publications in each country, while the thickness of the connecting lines expresses the closeness of the collaboration among countries. Generally, this research subject has received extensive international attention, with China and the USA publishing far more than any other countries. China has established notable research collaborations with the USA, UK and Malaysia in this field, while other countries have collaborations, but the closeness is relatively low and scattered. Figure 4B shows the annual publication volume dynamics of the top ten countries in terms of total publications. Since 2017, China has consistently increased its annual publications, while the USA has remained relatively stable. In 2019, the volume of publications in each country increased significantly, this was largely due to the global outbreak of the COVID-19 pandemic, which has led to increased reliance on information technology among the elderly for medical consultations, online socialization, and health management (Sinha et al. 2021 ). This phenomenon has led to research advances in technology acceptance among older adults in various countries. Table 4 shows that the top ten countries account for 93.20% of the total cumulative number of publications, with each country having published more than 20 papers. Among these ten countries, all of them except China are developed countries, indicating that the research field of older adults’ technology acceptance has received general attention from developed countries. Currently, China and the USA were the leading countries in terms of publications with 111 and 104 respectively, accounting for 22.20% and 20.80%. The UK, Germany, Italy, and the Netherlands also made significant contributions. The USA and China ranked first and second in terms of the number of citations, while the Netherlands had the highest average citations, indicating the high impact and quality of its research. The UK has shown outstanding performance in international cooperation, while the USA highlights its significant academic influence in this field with the highest h-index value.

A National collaboration network. B Annual volume of publications in the top 10 countries.

Institutions and authors analysis

Analyzing the number of publications and citations can reveal an institution’s or author’s research strength and influence in a particular research area (Kwiek 2021 ). Tables 5 and 6 show the statistics of the institutions and authors whose publication counts are in the top ten, respectively. As shown in Table 5 , higher education institutions hold the main position in this research field. Among the top ten institutions, City University of Hong Kong and The University of Hong Kong from China lead with 14 and 9 publications, respectively. City University of Hong Kong has the highest h-index, highlighting its significant influence in the field. It is worth noting that Tilburg University in the Netherlands is not among the top five in terms of publications, but the high average citation count (130.14) of its literature demonstrates the high quality of its research.

After analyzing the authors’ output using Price’s Law (Redner 1998 ), the highest number of publications among the authors counted ( n = 10) defines a publication threshold of 3 for core authors in this research area. As a result of quantitative screening, a total of 63 core authors were identified. Table 6 shows that Chen from Zhejiang University, China, Ziefle from RWTH Aachen University, Germany, and Rogers from Macquarie University, Australia, were the top three authors in terms of the number of publications, with 10, 9, and 8 articles, respectively. In terms of average citation rate, Peek and Wouters, both scholars from the Netherlands, have significantly higher rates than other scholars, with 183.2 and 152.67 respectively. This suggests that their research is of high quality and widely recognized. Additionally, Chen and Rogers have high h-indices in this field.

Knowledge base and theme progress (RQ3)

Research knowledge base.

Co-citation relationships occur when two documents are cited together (Zhang and Zhu 2022 ). Co-citation mapping uses references as nodes to represent the knowledge base of a subject area (Min et al. 2021). Figure 5A illustrates co-occurrence mapping in older adults’ technology acceptance research, where larger nodes signify higher co-citation frequencies. Co-citation cluster analysis can be used to explore knowledge structure and research boundaries (Hota et al. 2020 ; Shiau et al. 2023 ). The co-citation clustering mapping of older adults’ technology acceptance research literature (Fig. 5B ) shows that the Q value of the clustering result is 0.8129 (>0.3), and the average value of the weight S is 0.9391 (>0.7), indicating that the clusters are uniformly distributed with a significant and credible structure. This further proves that the boundaries of the research field are clear and there is significant differentiation in the field. The figure features 18 cluster labels, each associated with thematic color blocks corresponding to different time slices. Highlighted emerging research themes include #2 Smart Home Technology, #7 Social Live, and #10 Customer Service. Furthermore, the clustering labels extracted are primarily classified into three categories: theoretical model deepening, emerging technology applications, research methods and evaluation, as detailed in Table 7 .

A Co-citation analysis of references. B Clustering network analysis of references.

Seminal literature analysis

The top ten nodes in terms of co-citation frequency were selected for further analysis. Table 8 displays the corresponding node information. Studies were categorized into four main groups based on content analysis. (1) Research focusing on specific technology usage by older adults includes studies by Peek et al. ( 2014 ), Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ), who investigated the factors influencing the use of e-technology, smartphones, mHealth, and smart wearables, respectively. (2) Concerning the development of theoretical models of technology acceptance, Chen and Chan ( 2014 ) introduced the Senior Technology Acceptance Model (STAM), and Macedo ( 2017 ) analyzed the predictive power of UTAUT2 in explaining older adults’ intentional behaviors and information technology usage. (3) In exploring older adults’ information technology adoption and behavior, Lee and Coughlin ( 2015 ) emphasized that the adoption of technology by older adults is a multifactorial process that includes performance, price, value, usability, affordability, accessibility, technical support, social support, emotion, independence, experience, and confidence. Yusif et al. ( 2016 ) conducted a literature review examining the key barriers affecting older adults’ adoption of assistive technology, including factors such as privacy, trust, functionality/added value, cost, and stigma. (4) From the perspective of research into older adults’ technology acceptance, Mitzner et al. ( 2019 ) assessed the long-term usage of computer systems designed for the elderly, whereas Guner and Acarturk ( 2020 ) compared information technology usage and acceptance between older and younger adults. The breadth and prevalence of this literature make it a vital reference for researchers in the field, also providing new perspectives and inspiration for future research directions.

Research thematic progress

Burst citation is a node of literature that guides the sudden change in dosage, which usually represents a prominent development or major change in a particular field, with innovative and forward-looking qualities. By analyzing the emergent literature, it is often easy to understand the dynamics of the subject area, mapping the emerging thematic change (Chen et al. 2022 ). Figure 6 shows the burst citation mapping in the field of older adults’ technology acceptance research, with burst citations represented by red nodes (Fig. 6A ). For the ten papers with the highest burst intensity (Fig. 6B ), this study will conduct further analysis in conjunction with literature review.

A Burst detection of co-citation. B The top 10 references with the strongest citation bursts.

As shown in Fig. 6 , Mitzner et al. ( 2010 ) broke the stereotype that older adults are fearful of technology, found that they actually have positive attitudes toward technology, and emphasized the centrality of ease of use and usefulness in the process of technology acceptance. This finding provides an important foundation for subsequent research. During the same period, Wagner et al. ( 2010 ) conducted theory-deepening and applied research on technology acceptance among older adults. The research focused on older adults’ interactions with computers from the perspective of Social Cognitive Theory (SCT). This expanded the understanding of technology acceptance, particularly regarding the relationship between behavior, environment, and other SCT elements. In addition, Pan and Jordan-Marsh ( 2010 ) extended the TAM to examine the interactions among predictors of perceived usefulness, perceived ease of use, subjective norm, and convenience conditions when older adults use the Internet, taking into account the moderating roles of gender and age. Heerink et al. ( 2010 ) adapted and extended the UTAUT, constructed a technology acceptance model specifically designed for older users’ acceptance of assistive social agents, and validated it using controlled experiments and longitudinal data, explaining intention to use by combining functional assessment and social interaction variables.

Then the research theme shifted to an in-depth analysis of the factors influencing technology acceptance among older adults. Two papers with high burst strengths emerged during this period: Peek et al. ( 2014 ) (Strength = 12.04), Chen and Chan ( 2014 ) (Strength = 9.81). Through a systematic literature review and empirical study, Peek STM and Chen K, among others, identified multidimensional factors that influence older adults’ technology acceptance. Peek et al. ( 2014 ) analyzed literature on the acceptance of in-home care technology among older adults and identified six factors that influence their acceptance: concerns about technology, expected benefits, technology needs, technology alternatives, social influences, and older adult characteristics, with a focus on differences between pre- and post-implementation factors. Chen and Chan ( 2014 ) constructed the STAM by administering a questionnaire to 1012 older adults and adding eight important factors, including technology anxiety, self-efficacy, cognitive ability, and physical function, based on the TAM. This enriches the theoretical foundation of the field. In addition, Braun ( 2013 ) highlighted the role of perceived usefulness, trust in social networks, and frequency of Internet use in older adults’ use of social networks, while ease of use and social pressure were not significant influences. These findings contribute to the study of older adults’ technology acceptance within specific technology application domains.

Recent research has focused on empirical studies of personal factors and emerging technologies. Ma et al. ( 2016 ) identified key personal factors affecting smartphone acceptance among older adults through structured questionnaires and face-to-face interviews with 120 participants. The study found that cost, self-satisfaction, and convenience were important factors influencing perceived usefulness and ease of use. This study offers empirical evidence to comprehend the main factors that drive smartphone acceptance among Chinese older adults. Additionally, Yusif et al. ( 2016 ) presented an overview of the obstacles that hinder older adults’ acceptance of assistive technologies, focusing on privacy, trust, and functionality.

In summary, research on older adults’ technology acceptance has shifted from early theoretical deepening and analysis of influencing factors to empirical studies in the areas of personal factors and emerging technologies, which have greatly enriched the theoretical basis of older adults’ technology acceptance and provided practical guidance for the design of emerging technology products.

Research hotspots, evolutionary trends, and quality distribution (RQ4)

Core keywords analysis.

Keywords concise the main idea and core of the literature, and are a refined summary of the research content (Huang et al. 2021 ). In CiteSpace, nodes with a centrality value greater than 0.1 are considered to be critical nodes. Analyzing keywords with high frequency and centrality helps to visualize the hot topics in the research field (Park et al. 2018 ). The merged keywords were imported into CiteSpace, and the top 10 keywords were counted and sorted by frequency and centrality respectively, as shown in Table 9 . The results show that the keyword “TAM” has the highest frequency (92), followed by “UTAUT” (24), which reflects that the in-depth study of the existing technology acceptance model and its theoretical expansion occupy a central position in research related to older adults’ technology acceptance. Furthermore, the terms ‘assistive technology’ and ‘virtual reality’ are both high-frequency and high-centrality terms (frequency = 17, centrality = 0.10), indicating that the research on assistive technology and virtual reality for older adults is the focus of current academic attention.

Research hotspots analysis

Using VOSviewer for keyword co-occurrence analysis organizes keywords into groups or clusters based on their intrinsic connections and frequencies, clearly highlighting the research field’s hot topics. The connectivity among keywords reveals correlations between different topics. To ensure accuracy, the analysis only considered the authors’ keywords. Subsequently, the keywords were filtered by setting the keyword frequency to 5 to obtain the keyword clustering map of the research on older adults’ technology acceptance research keyword clustering mapping (Fig. 7 ), combined with the keyword co-occurrence clustering network (Fig. 7A ) and the corresponding density situation (Fig. 7B ) to make a detailed analysis of the following four groups of clustered themes.

A Co-occurrence clustering network. B Keyword density.

Cluster #1—Research on the factors influencing technology adoption among older adults is a prominent topic, covering age, gender, self-efficacy, attitude, and and intention to use (Berkowsky et al. 2017 ; Wang et al. 2017 ). It also examined older adults’ attitudes towards and acceptance of digital health technologies (Ahmad and Mozelius, 2022 ). Moreover, the COVID-19 pandemic, significantly impacting older adults’ technology attitudes and usage, has underscored the study’s importance and urgency. Therefore, it is crucial to conduct in-depth studies on how older adults accept, adopt, and effectively use new technologies, to address their needs and help them overcome the digital divide within digital inclusion. This will improve their quality of life and healthcare experiences.

Cluster #2—Research focuses on how older adults interact with assistive technologies, especially assistive robots and health monitoring devices, emphasizing trust, usability, and user experience as crucial factors (Halim et al. 2022 ). Moreover, health monitoring technologies effectively track and manage health issues common in older adults, like dementia and mild cognitive impairment (Lussier et al. 2018 ; Piau et al. 2019 ). Interactive exercise games and virtual reality have been deployed to encourage more physical and cognitive engagement among older adults (Campo-Prieto et al. 2021 ). Personalized and innovative technology significantly enhances older adults’ participation, improving their health and well-being.

Cluster #3—Optimizing health management for older adults using mobile technology. With the development of mobile health (mHealth) and health information technology, mobile applications, smartphones, and smart wearable devices have become effective tools to help older users better manage chronic conditions, conduct real-time health monitoring, and even receive telehealth services (Dupuis and Tsotsos 2018 ; Olmedo-Aguirre et al. 2022 ; Kim et al. 2014 ). Additionally, these technologies can mitigate the problem of healthcare resource inequality, especially in developing countries. Older adults’ acceptance and use of these technologies are significantly influenced by their behavioral intentions, motivational factors, and self-management skills. These internal motivational factors, along with external factors, jointly affect older adults’ performance in health management and quality of life.

Cluster #4—Research on technology-assisted home care for older adults is gaining popularity. Environmentally assisted living enhances older adults’ independence and comfort at home, offering essential support and security. This has a crucial impact on promoting healthy aging (Friesen et al. 2016 ; Wahlroos et al. 2023 ). The smart home is a core application in this field, providing a range of solutions that facilitate independent living for the elderly in a highly integrated and user-friendly manner. This fulfills different dimensions of living and health needs (Majumder et al. 2017 ). Moreover, eHealth offers accurate and personalized health management and healthcare services for older adults (Delmastro et al. 2018 ), ensuring their needs are met at home. Research in this field often employs qualitative methods and structural equation modeling to fully understand older adults’ needs and experiences at home and analyze factors influencing technology adoption.

Evolutionary trends analysis

To gain a deeper understanding of the evolutionary trends in research hotspots within the field of older adults’ technology acceptance, we conducted a statistical analysis of the average appearance times of keywords, using CiteSpace to generate the time-zone evolution mapping (Fig. 8 ) and burst keywords. The time-zone mapping visually displays the evolution of keywords over time, intuitively reflecting the frequency and initial appearance of keywords in research, commonly used to identify trends in research topics (Jing et al. 2024a ; Kumar et al. 2021 ). Table 10 lists the top 15 keywords by burst strength, with the red sections indicating high-frequency citations and their burst strength in specific years. These burst keywords reveal the focus and trends of research themes over different periods (Kleinberg 2002 ). Combining insights from the time-zone mapping and burst keywords provides more objective and accurate research insights (Wang et al. 2023b ).

Reflecting the frequency and time of first appearance of keywords in the study.

An integrated analysis of Fig. 8 and Table 10 shows that early research on older adults’ technology acceptance primarily focused on factors such as perceived usefulness, ease of use, and attitudes towards information technology, including their use of computers and the internet (Pan and Jordan-Marsh 2010 ), as well as differences in technology use between older adults and other age groups (Guner and Acarturk 2020 ). Subsequently, the research focus expanded to improving the quality of life for older adults, exploring how technology can optimize health management and enhance the possibility of independent living, emphasizing the significant role of technology in improving the quality of life for the elderly. With ongoing technological advancements, recent research has shifted towards areas such as “virtual reality,” “telehealth,” and “human-robot interaction,” with a focus on the user experience of older adults (Halim et al. 2022 ). The appearance of keywords such as “physical activity” and “exercise” highlights the value of technology in promoting physical activity and health among older adults. This phase of research tends to make cutting-edge technology genuinely serve the practical needs of older adults, achieving its widespread application in daily life. Additionally, research has focused on expanding and quantifying theoretical models of older adults’ technology acceptance, involving keywords such as “perceived risk”, “validation” and “UTAUT”.

In summary, from 2013 to 2023, the field of older adults’ technology acceptance has evolved from initial explorations of influencing factors, to comprehensive enhancements in quality of life and health management, and further to the application and deepening of theoretical models and cutting-edge technologies. This research not only reflects the diversity and complexity of the field but also demonstrates a comprehensive and in-depth understanding of older adults’ interactions with technology across various life scenarios and needs.

Research quality distribution

To reveal the distribution of research quality in the field of older adults’ technology acceptance, a strategic diagram analysis is employed to calculate and illustrate the internal development and interrelationships among various research themes (Xie et al. 2020 ). The strategic diagram uses Centrality as the X-axis and Density as the Y-axis to divide into four quadrants, where the X-axis represents the strength of the connection between thematic clusters and other themes, with higher values indicating a central position in the research field; the Y-axis indicates the level of development within the thematic clusters, with higher values denoting a more mature and widely recognized field (Li and Zhou 2020 ).

Through cluster analysis and manual verification, this study categorized 61 core keywords (Frequency ≥5) into 11 thematic clusters. Subsequently, based on the keywords covered by each thematic cluster, the research themes and their directions for each cluster were summarized (Table 11 ), and the centrality and density coordinates for each cluster were precisely calculated (Table 12 ). Finally, a strategic diagram of the older adults’ technology acceptance research field was constructed (Fig. 9 ). Based on the distribution of thematic clusters across the quadrants in the strategic diagram, the structure and developmental trends of the field were interpreted.

Classification and visualization of theme clusters based on density and centrality.

As illustrated in Fig. 9 , (1) the theme clusters of #3 Usage Experience and #4 Assisted Living Technology are in the first quadrant, characterized by high centrality and density. Their internal cohesion and close links with other themes indicate their mature development, systematic research content or directions have been formed, and they have a significant influence on other themes. These themes play a central role in the field of older adults’ technology acceptance and have promising prospects. (2) The theme clusters of #6 Smart Devices, #9 Theoretical Models, and #10 Mobile Health Applications are in the second quadrant, with higher density but lower centrality. These themes have strong internal connections but weaker external links, indicating that these three themes have received widespread attention from researchers and have been the subject of related research, but more as self-contained systems and exhibit independence. Therefore, future research should further explore in-depth cooperation and cross-application with other themes. (3) The theme clusters of #7 Human-Robot Interaction, #8 Characteristics of the Elderly, and #11 Research Methods are in the third quadrant, with lower centrality and density. These themes are loosely connected internally and have weak links with others, indicating their developmental immaturity. Compared to other topics, they belong to the lower attention edge and niche themes, and there is a need for further investigation. (4) The theme clusters of #1 Digital Healthcare Technology, #2 Psychological Factors, and #5 Socio-Cultural Factors are located in the fourth quadrant, with high centrality but low density. Although closely associated with other research themes, the internal cohesion within these clusters is relatively weak. This suggests that while these themes are closely linked to other research areas, their own development remains underdeveloped, indicating a core immaturity. Nevertheless, these themes are crucial within the research domain of elderly technology acceptance and possess significant potential for future exploration.

Discussion on distribution power (RQ1)

Over the past decade, academic interest and influence in the area of older adults’ technology acceptance have significantly increased. This trend is evidenced by a quantitative analysis of publication and citation volumes, particularly noticeable in 2019 and 2022, where there was a substantial rise in both metrics. The rise is closely linked to the widespread adoption of emerging technologies such as smart homes, wearable devices, and telemedicine among older adults. While these technologies have enhanced their quality of life, they also pose numerous challenges, sparking extensive research into their acceptance, usage behaviors, and influencing factors among the older adults (Pirzada et al. 2022 ; Garcia Reyes et al. 2023 ). Furthermore, the COVID-19 pandemic led to a surge in technology demand among older adults, especially in areas like medical consultation, online socialization, and health management, further highlighting the importance and challenges of technology. Health risks and social isolation have compelled older adults to rely on technology for daily activities, accelerating its adoption and application within this demographic. This phenomenon has made technology acceptance a critical issue, driving societal and academic focus on the study of technology acceptance among older adults.

The flow of knowledge at the level of high-output disciplines and journals, along with the primary publishing outlets, indicates the highly interdisciplinary nature of research into older adults’ technology acceptance. This reflects the complexity and breadth of issues related to older adults’ technology acceptance, necessitating the integration of multidisciplinary knowledge and approaches. Currently, research is primarily focused on medical health and human-computer interaction, demonstrating academic interest in improving health and quality of life for older adults and addressing the urgent needs related to their interactions with technology. In the field of medical health, research aims to provide advanced and innovative healthcare technologies and services to meet the challenges of an aging population while improving the quality of life for older adults (Abdi et al. 2020 ; Wilson et al. 2021 ). In the field of human-computer interaction, research is focused on developing smarter and more user-friendly interaction models to meet the needs of older adults in the digital age, enabling them to actively participate in social activities and enjoy a higher quality of life (Sayago, 2019 ). These studies are crucial for addressing the challenges faced by aging societies, providing increased support and opportunities for the health, welfare, and social participation of older adults.

Discussion on research power (RQ2)

This study analyzes leading countries and collaboration networks, core institutions and authors, revealing the global research landscape and distribution of research strength in the field of older adults’ technology acceptance, and presents quantitative data on global research trends. From the analysis of country distribution and collaborations, China and the USA hold dominant positions in this field, with developed countries like the UK, Germany, Italy, and the Netherlands also excelling in international cooperation and research influence. The significant investment in technological research and the focus on the technological needs of older adults by many developed countries reflect their rapidly aging societies, policy support, and resource allocation.

China is the only developing country that has become a major contributor in this field, indicating its growing research capabilities and high priority given to aging societies and technological innovation. Additionally, China has close collaborations with countries such as USA, the UK, and Malaysia, driven not only by technological research needs but also by shared challenges and complementarities in aging issues among these nations. For instance, the UK has extensive experience in social welfare and aging research, providing valuable theoretical guidance and practical experience. International collaborations, aimed at addressing the challenges of aging, integrate the strengths of various countries, advancing in-depth and widespread development in the research of technology acceptance among older adults.

At the institutional and author level, City University of Hong Kong leads in publication volume, with research teams led by Chan and Chen demonstrating significant academic activity and contributions. Their research primarily focuses on older adults’ acceptance and usage behaviors of various technologies, including smartphones, smart wearables, and social robots (Chen et al. 2015 ; Li et al. 2019 ; Ma et al. 2016 ). These studies, targeting specific needs and product characteristics of older adults, have developed new models of technology acceptance based on existing frameworks, enhancing the integration of these technologies into their daily lives and laying a foundation for further advancements in the field. Although Tilburg University has a smaller publication output, it holds significant influence in the field of older adults’ technology acceptance. Particularly, the high citation rate of Peek’s studies highlights their excellence in research. Peek extensively explored older adults’ acceptance and usage of home care technologies, revealing the complexity and dynamics of their technology use behaviors. His research spans from identifying systemic influencing factors (Peek et al. 2014 ; Peek et al. 2016 ), emphasizing familial impacts (Luijkx et al. 2015 ), to constructing comprehensive models (Peek et al. 2017 ), and examining the dynamics of long-term usage (Peek et al. 2019 ), fully reflecting the evolving technology landscape and the changing needs of older adults. Additionally, the ongoing contributions of researchers like Ziefle, Rogers, and Wouters in the field of older adults’ technology acceptance demonstrate their research influence and leadership. These researchers have significantly enriched the knowledge base in this area with their diverse perspectives. For instance, Ziefle has uncovered the complex attitudes of older adults towards technology usage, especially the trade-offs between privacy and security, and how different types of activities affect their privacy needs (Maidhof et al. 2023 ; Mujirishvili et al. 2023 ; Schomakers and Ziefle 2023 ; Wilkowska et al. 2022 ), reflecting a deep exploration and ongoing innovation in the field of older adults’ technology acceptance.

Discussion on knowledge base and thematic progress (RQ3)

Through co-citation analysis and systematic review of seminal literature, this study reveals the knowledge foundation and thematic progress in the field of older adults’ technology acceptance. Co-citation networks and cluster analyses illustrate the structural themes of the research, delineating the differentiation and boundaries within this field. Additionally, burst detection analysis offers a valuable perspective for understanding the thematic evolution in the field of technology acceptance among older adults. The development and innovation of theoretical models are foundational to this research. Researchers enhance the explanatory power of constructed models by deepening and expanding existing technology acceptance theories to address theoretical limitations. For instance, Heerink et al. ( 2010 ) modified and expanded the UTAUT model by integrating functional assessment and social interaction variables to create the almere model. This model significantly enhances the ability to explain the intentions of older users in utilizing assistive social agents and improves the explanation of actual usage behaviors. Additionally, Chen and Chan ( 2014 ) extended the TAM to include age-related health and capability features of older adults, creating the STAM, which substantially improves predictions of older adults’ technology usage behaviors. Personal attributes, health and capability features, and facilitating conditions have a direct impact on technology acceptance. These factors more effectively predict older adults’ technology usage behaviors than traditional attitudinal factors.

With the advancement of technology and the application of emerging technologies, new research topics have emerged, increasingly focusing on older adults’ acceptance and use of these technologies. Prior to this, the study by Mitzner et al. ( 2010 ) challenged the stereotype of older adults’ conservative attitudes towards technology, highlighting the central roles of usability and usefulness in the technology acceptance process. This discovery laid an important foundation for subsequent research. Research fields such as “smart home technology,” “social life,” and “customer service” are emerging, indicating a shift in focus towards the practical and social applications of technology in older adults’ lives. Research not only focuses on the technology itself but also on how these technologies integrate into older adults’ daily lives and how they can improve the quality of life through technology. For instance, studies such as those by Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ) have explored factors influencing older adults’ use of smartphones, mHealth, and smart wearable devices.

Furthermore, the diversification of research methodologies and innovation in evaluation techniques, such as the use of mixed methods, structural equation modeling (SEM), and neural network (NN) approaches, have enhanced the rigor and reliability of the findings, enabling more precise identification of the factors and mechanisms influencing technology acceptance. Talukder et al. ( 2020 ) employed an effective multimethodological strategy by integrating SEM and NN to leverage the complementary strengths of both approaches, thus overcoming their individual limitations and more accurately analyzing and predicting older adults’ acceptance of wearable health technologies (WHT). SEM is utilized to assess the determinants’ impact on the adoption of WHT, while neural network models validate SEM outcomes and predict the significance of key determinants. This combined approach not only boosts the models’ reliability and explanatory power but also provides a nuanced understanding of the motivations and barriers behind older adults’ acceptance of WHT, offering deep research insights.

Overall, co-citation analysis of the literature in the field of older adults’ technology acceptance has uncovered deeper theoretical modeling and empirical studies on emerging technologies, while emphasizing the importance of research methodological and evaluation innovations in understanding complex social science issues. These findings are crucial for guiding the design and marketing strategies of future technology products, especially in the rapidly growing market of older adults.

Discussion on research hotspots and evolutionary trends (RQ4)

By analyzing core keywords, we can gain deep insights into the hot topics, evolutionary trends, and quality distribution of research in the field of older adults’ technology acceptance. The frequent occurrence of the keywords “TAM” and “UTAUT” indicates that the applicability and theoretical extension of existing technology acceptance models among older adults remain a focal point in academia. This phenomenon underscores the enduring influence of the studies by Davis ( 1989 ) and Venkatesh et al. ( 2003 ), whose models provide a robust theoretical framework for explaining and predicting older adults’ acceptance and usage of emerging technologies. With the widespread application of artificial intelligence (AI) and big data technologies, these theoretical models have incorporated new variables such as perceived risk, trust, and privacy issues (Amin et al. 2024 ; Chen et al. 2024 ; Jing et al. 2024b ; Seibert et al. 2021 ; Wang et al. 2024b ), advancing the theoretical depth and empirical research in this field.

Keyword co-occurrence cluster analysis has revealed multiple research hotspots in the field, including factors influencing technology adoption, interactive experiences between older adults and assistive technologies, the application of mobile health technology in health management, and technology-assisted home care. These studies primarily focus on enhancing the quality of life and health management of older adults through emerging technologies, particularly in the areas of ambient assisted living, smart health monitoring, and intelligent medical care. In these domains, the role of AI technology is increasingly significant (Qian et al. 2021 ; Ho 2020 ). With the evolution of next-generation information technologies, AI is increasingly integrated into elder care systems, offering intelligent, efficient, and personalized service solutions by analyzing the lifestyles and health conditions of older adults. This integration aims to enhance older adults’ quality of life in aspects such as health monitoring and alerts, rehabilitation assistance, daily health management, and emotional support (Lee et al. 2023 ). A survey indicates that 83% of older adults prefer AI-driven solutions when selecting smart products, demonstrating the increasing acceptance of AI in elder care (Zhao and Li 2024 ). Integrating AI into elder care presents both opportunities and challenges, particularly in terms of user acceptance, trust, and long-term usage effects, which warrant further exploration (Mhlanga 2023 ). These studies will help better understand the profound impact of AI technology on the lifestyles of older adults and provide critical references for optimizing AI-driven elder care services.

The Time-zone evolution mapping and burst keyword analysis further reveal the evolutionary trends of research hotspots. Early studies focused on basic technology acceptance models and user perceptions, later expanding to include quality of life and health management. In recent years, research has increasingly focused on cutting-edge technologies such as virtual reality, telehealth, and human-robot interaction, with a concurrent emphasis on the user experience of older adults. This evolutionary process demonstrates a deepening shift from theoretical models to practical applications, underscoring the significant role of technology in enhancing the quality of life for older adults. Furthermore, the strategic coordinate mapping analysis clearly demonstrates the development and mutual influence of different research themes. High centrality and density in the themes of Usage Experience and Assisted Living Technology indicate their mature research status and significant impact on other themes. The themes of Smart Devices, Theoretical Models, and Mobile Health Applications demonstrate self-contained research trends. The themes of Human-Robot Interaction, Characteristics of the Elderly, and Research Methods are not yet mature, but they hold potential for development. Themes of Digital Healthcare Technology, Psychological Factors, and Socio-Cultural Factors are closely related to other themes, displaying core immaturity but significant potential.

In summary, the research hotspots in the field of older adults’ technology acceptance are diverse and dynamic, demonstrating the academic community’s profound understanding of how older adults interact with technology across various life contexts and needs. Under the influence of AI and big data, research should continue to focus on the application of emerging technologies among older adults, exploring in depth how they adapt to and effectively use these technologies. This not only enhances the quality of life and healthcare experiences for older adults but also drives ongoing innovation and development in this field.

Research agenda

Based on the above research findings, to further understand and promote technology acceptance and usage among older adults, we recommend future studies focus on refining theoretical models, exploring long-term usage, and assessing user experience in the following detailed aspects:

Refinement and validation of specific technology acceptance models for older adults: Future research should focus on developing and validating technology acceptance models based on individual characteristics, particularly considering variations in technology acceptance among older adults across different educational levels and cultural backgrounds. This includes factors such as age, gender, educational background, and cultural differences. Additionally, research should examine how well specific technologies, such as wearable devices and mobile health applications, meet the needs of older adults. Building on existing theoretical models, this research should integrate insights from multiple disciplines such as psychology, sociology, design, and engineering through interdisciplinary collaboration to create more accurate and comprehensive models, which should then be validated in relevant contexts.

Deepening the exploration of the relationship between long-term technology use and quality of life among older adults: The acceptance and use of technology by users is a complex and dynamic process (Seuwou et al. 2016 ). Existing research predominantly focuses on older adults’ initial acceptance or short-term use of new technologies; however, the impact of long-term use on their quality of life and health is more significant. Future research should focus on the evolution of older adults’ experiences and needs during long-term technology usage, and the enduring effects of technology on their social interactions, mental health, and life satisfaction. Through longitudinal studies and qualitative analysis, this research reveals the specific needs and challenges of older adults in long-term technology use, providing a basis for developing technologies and strategies that better meet their requirements. This understanding aids in comprehensively assessing the impact of technology on older adults’ quality of life and guiding the optimization and improvement of technological products.

Evaluating the Importance of User Experience in Research on Older Adults’ Technology Acceptance: Understanding the mechanisms of information technology acceptance and use is central to human-computer interaction research. Although technology acceptance models and user experience models differ in objectives, they share many potential intersections. Technology acceptance research focuses on structured prediction and assessment, while user experience research concentrates on interpreting design impacts and new frameworks. Integrating user experience to assess older adults’ acceptance of technology products and systems is crucial (Codfrey et al. 2022 ; Wang et al. 2019 ), particularly for older users, where specific product designs should emphasize practicality and usability (Fisk et al. 2020 ). Researchers need to explore innovative age-appropriate design methods to enhance older adults’ usage experience. This includes studying older users’ actual usage preferences and behaviors, optimizing user interfaces, and interaction designs. Integrating feedback from older adults to tailor products to their needs can further promote their acceptance and continued use of technology products.

Conclusions

This study conducted a systematic review of the literature on older adults’ technology acceptance over the past decade through bibliometric analysis, focusing on the distribution power, research power, knowledge base and theme progress, research hotspots, evolutionary trends, and quality distribution. Using a combination of quantitative and qualitative methods, this study has reached the following conclusions:

Technology acceptance among older adults has become a hot topic in the international academic community, involving the integration of knowledge across multiple disciplines, including Medical Informatics, Health Care Sciences Services, and Ergonomics. In terms of journals, “PSYCHOLOGY, EDUCATION, HEALTH” represents a leading field, with key publications including Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction . These journals possess significant academic authority and extensive influence in the field.

Research on technology acceptance among older adults is particularly active in developed countries, with China and USA publishing significantly more than other nations. The Netherlands leads in high average citation rates, indicating the depth and impact of its research. Meanwhile, the UK stands out in terms of international collaboration. At the institutional level, City University of Hong Kong and The University of Hong Kong in China are in leading positions. Tilburg University in the Netherlands demonstrates exceptional research quality through its high average citation count. At the author level, Chen from China has the highest number of publications, while Peek from the Netherlands has the highest average citation count.

Co-citation analysis of references indicates that the knowledge base in this field is divided into three main categories: theoretical model deepening, emerging technology applications, and research methods and evaluation. Seminal literature focuses on four areas: specific technology use by older adults, expansion of theoretical models of technology acceptance, information technology adoption behavior, and research perspectives. Research themes have evolved from initial theoretical deepening and analysis of influencing factors to empirical studies on individual factors and emerging technologies.

Keyword analysis indicates that TAM and UTAUT are the most frequently occurring terms, while “assistive technology” and “virtual reality” are focal points with high frequency and centrality. Keyword clustering analysis reveals that research hotspots are concentrated on the influencing factors of technology adoption, human-robot interaction experiences, mobile health management, and technology for aging in place. Time-zone evolution mapping and burst keyword analysis have revealed the research evolution from preliminary exploration of influencing factors, to enhancements in quality of life and health management, and onto advanced technology applications and deepening of theoretical models. Furthermore, analysis of research quality distribution indicates that Usage Experience and Assisted Living Technology have become core topics, while Smart Devices, Theoretical Models, and Mobile Health Applications point towards future research directions.

Through this study, we have systematically reviewed the dynamics, core issues, and evolutionary trends in the field of older adults’ technology acceptance, constructing a comprehensive Knowledge Mapping of the domain and presenting a clear framework of existing research. This not only lays the foundation for subsequent theoretical discussions and innovative applications in the field but also provides an important reference for relevant scholars.

Limitations

To our knowledge, this is the first bibliometric analysis concerning technology acceptance among older adults, and we adhered strictly to bibliometric standards throughout our research. However, this study relies on the Web of Science Core Collection, and while its authority and breadth are widely recognized, this choice may have missed relevant literature published in other significant databases such as PubMed, Scopus, and Google Scholar, potentially overlooking some critical academic contributions. Moreover, given that our analysis was confined to literature in English, it may not reflect studies published in other languages, somewhat limiting the global representativeness of our data sample.

It is noteworthy that with the rapid development of AI technology, its increasingly widespread application in elderly care services is significantly transforming traditional care models. AI is profoundly altering the lifestyles of the elderly, from health monitoring and smart diagnostics to intelligent home systems and personalized care, significantly enhancing their quality of life and health care standards. The potential for AI technology within the elderly population is immense, and research in this area is rapidly expanding. However, due to the restrictive nature of the search terms used in this study, it did not fully cover research in this critical area, particularly in addressing key issues such as trust, privacy, and ethics.

Consequently, future research should not only expand data sources, incorporating multilingual and multidatabase literature, but also particularly focus on exploring older adults’ acceptance of AI technology and its applications, in order to construct a more comprehensive academic landscape of older adults’ technology acceptance, thereby enriching and extending the knowledge system and academic trends in this field.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/6K0GJH .

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This research was supported by the Social Science Foundation of Shaanxi Province in China (Grant No. 2023J014).

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Shang, X., Liu, Z., Gong, C. et al. Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023. Humanit Soc Sci Commun 11 , 1115 (2024). https://doi.org/10.1057/s41599-024-03658-2

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