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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.

It publishes original research, reviews of the discipline, theoretical and conceptual work, applied research, translational research, program descriptions, research in organizations and the community, clinical work, and curricular developments.

Areas of interest include, but are not limited to, clinical behavior analysis, applied and translational behavior analysis, behavior therapy, behavioral consultation, organizational behavior management, and human performance technology.

Behavior Analysis: Research and Practice presents current experimental and translational research, and applications of behavioral analysis, in ways that can improve human behavior in all its contexts: across the developmental continuum in organizational, community, residential, clinical, and any other settings in which the fruits of behavior analysis can make a positive contribution.

The journal also provides a focused view of behavioral consultation and therapy for the general behavioral intervention community. Additionally, the journal highlights the importance of conducting clinical research from a strong theoretical base. Additional topic areas of interest include contextual research, third-wave research, and clinical articles.

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Joel E. Ringdahl University of Georgia, United States

Associate editors

Jonathan C. Baker, PhD, BCBA-D Western Michigan University, United States

Andrew R. Craig, PhD SUNY Upstate Medical University, United States

Kelly Schieltz, PhD University of Iowa, United States

Maria G. Valdovinos, PhD, BCBA-D Drake University, United States

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Keith D. Allen, PhD, BCBA-D Munroe-Meyer Institute for Genetics and Rehabilitation, United States

Cynthia M. Anderson, PhD, BCBA-D May Institute, United States

Scott P. Ardoin, PhD, BCBA-D University of Georgia, United States

Jennifer Austin, PhD, BCBA-D Georgia State University, United Kingdom

Judah Axe, PhD, BCBA-D, LABA Simmons University, United States

Jessica  Becraft, PhD Kennedy Krieger Institute, United States

Kevin Michael Ayres, PhD, BCBA-D The University of Georgia, United States

Jordan Belisle, PhD, BCBA, LBA Missouri State University, United States

Carrie S.W. Borrero, PhD, BCBA-D, LBA Kennedy Krieger Institute, United States

Rachel R. Cagliani, PhD, BCBA-D University of Georgia, United States

Regina Carroll, PhD University of Nebraska Medical Center, United States

Joseph D. Cautilli, PhD Behavior Analysis and Therapy Partners, United States

Linda J. Cooper-Brown, PhD University of Iowa, United States

Casey  Clay, PhD, BCBA-D Utah State University, United States

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Neil Deochand, PhD University of Cincinnati, United States

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Odessa Luna, BCBA-D, PhD St. Cloud State University, United States

David B. McAdam, PhD University of Rochester, United States

Jennifer McComas, PhD University of Minnesota, United States

Brandon E. McCord, PhD, BCBA-D, LBA West Tennessee Community Homes, United States

Heather M. McGee, PhD Western Michigan University, United States

Raymond G. Miltenberger, PhD University of South Florida, United States

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Sacha T. Pence, PhD Western Michigan University, United States

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Allie E. Rader, PhD, BCBA-D May Institute, United States

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Patrick W. Romani, PhD, BCBA-D University of Colorado School of Medicine, United States

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Mindy Scheithauer, PhD, BCBA-D Emory University School of Medicine and Marcus Autism Center, United States

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Sarah Slocum Freeman, PhD, BCBA-D Emory University and Marcus Autism Center, United States

Julie M. Slowiak, PhD, BCBA-D University of Minnesota Duluth, United States

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Karla A. Zabala-Snow, PhD, BCBA-D Emory University/Marcus Autism Center, United States

Amanda Zangrillo, PsyD, BCBA-D University of Nebraska Medical Center, United States

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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International Society for Behavioral Ecology

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.

Forum editor: Sue Healy

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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

JournalNumber 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
Overall369 (100)2007
JournalNumber 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
Overall369 (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.

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

TopicQuestionExample references
ProductionWhen 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)
DistributionWith 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)
ReproductionWhy 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)
TopicQuestionExample references
ProductionWhen 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)
DistributionWith 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)
ReproductionWhy 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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Neural Representations of Sensory Uncertainty and Confidence are Associated with Perceptual Curiosity

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Humans are immensely curious and motivated to reduce uncertainty, but little is known about the neural mechanisms that generate curiosity. Curiosity is inversely associated with confidence, suggesting that it is triggered by states of low confidence (subjective uncertainty). The neural mechanisms of this process, however, have been little investigated. What are the mechanisms through which uncertainty about an event gives rise to curiosity about that event? Inspired by studies of sensory uncertainty, we hypothesized that visual areas provide multivariate representations of uncertainty, which are then read out by higher-order structures to generate signals of confidence and, ultimately, trigger curiosity. During fMRI, participants (17 female, 15 male) performed a new task in which they rated their confidence in identifying distorted images of animals and objects and their curiosity to see the clear image. To link sensory certainty and curiosity, we measured the activity evoked by each image in occipitotemporal cortex (OTC) and devised a new metric of “OTC Certainty” indicating the strength of evidence this activity conveys about the animal vs. object categories. We show that, consistent with findings using trivia questions, perceptual curiosity peaked at low confidence. Moreover, OTC Certainty negatively correlated with curiosity, establishing a link between curiosity and a multivariate representation of sensory uncertainty. Finally, univariate (average) activity in two frontal areas – vmPFC and ACC – correlated positively with confidence and negatively with curiosity, and the vmPFC mediated the relationship between OTC Certainty and curiosity. The results suggest that multiple mechanisms link curiosity with representations of confidence and uncertainty.

Significance Statement Curiosity motivates us to explore and learn about the world around us. Traditional perspectives hypothesize that curiosity arises from variability in confidence, but the neural mechanisms by which this occurs have been difficult to evaluate. Here, we harness the human visual system to uncover a neural mechanism of curiosity. We show that a multivariate representation of certainty in occitotemporal cortex is transformed into a univariate representation of confidence in prefrontal cortex to facilitate curiosity. Together, these results illuminate how perceptual input is transformed by successive neural representations to ultimately evoke a feeling of curiosity - elucidating how and why we become curious to learn and delve into diverse domains of knowledge.

The research described in this paper was supported by the National Institute of Mental Health as part of the National Research Service Award (Grant #:1F31MH125589), and the Zuckerman Institute MR Seed Grant Award (Grant #: CU-ZI-MR-S-0017) both awarded to Michael Cohanpour. We thank the Alyssano Group, Gottlieb Lab, Kriegeskorte Lab, Christopher Baldassano, Janet Metcalfe, and Yasmine El-Shamayleh for their valuable insight on this project; Ray Lee and Noreen Violante for their technical support with the MRI scanner; and Serra Favila, Heiko Schütt, and Javier Domínguez Zamora for their crucial revisions to the manuscript.

The authors declare that they have no conflict of interest.

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Research on the human motion recognition method based on wearable.

a research paper about human behavior

1. Introduction

2. materials and methods, 2.1. experimental equipment, 2.1.1. design of the human movement data acquisition platform, 2.1.2. software host computer design.

  • Data selection area: The IP address currently connected to the human motion recognition software is displayed, and the sensor device is connected to the human motion recognition software. The function of data selection can be achieved using the lower pull bar, which can select nine different body parts.
  • Data exhibition area: The acceleration data and angular velocity data of the current human movement are displayed in the form of two rows and three columns to update the data movement information of the current user in real time.
  • Data display area: The acceleration data of the data display area are displayed in the form of data lines.
  • Recognition result display area: When the human body performs different actions, the action recognition result will be displayed in this area.
  • Data storage area: The movement data under different movements of the human body can be stored. Different movement data of different body parts can be selected according to the data selection area. After freely setting the storage path, the acceleration and angular velocity data under the current motion state are stored as a table, as shown in Figure 3 b.

2.2. Human Motion Recognition Algorithm

2.2.1. dmp attitude solution algorithm.

  • Data Acquisition phase

2.2.2. Determination of the Motion State of the Threshold Algorithm

  • When the value set by the threshold is greater than A, due to the high threshold value, only the jumping action can exceed the threshold to carry out the internal cycle of the algorithm. In contrast, the number of walking and standing actions is mostly less than the set threshold, resulting in poor recognition of walking and standing actions.
  • When the threshold is less than A, because the threshold is too low, large numbers of walking and jumping actions can exceed the set threshold during the algorithm, resulting in confusion between walking and jumping actions and a poor recognition effect, and only standing actions have a good recognition effect.
  • When the threshold value is set to A, this method has an ideal recognition effect on standing, walking, and jumping actions. In summary, when the test threshold is A, the recognition effect of this method is relatively ideal.
  • When I 1 ≤ i ≤ I 2 , the output body is currently walking.
  • When i ≥ I 3 is used, the current output of the human body is the jumping action; otherwise, the current output of the human body is the standing action. The algorithm flow chart is shown in Figure 4 (I 1 , I 2 , and I 3 are the set numbers of sensors).

3.1. Experimental Design

3.2. experimental results and analysis, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Click here to enlarge figure

Test Motor Action
(Unit: Unit)
Identification ResultRecognition Rate
(%)
Stand (Unit: Unit)Walk (Unit: Unit)Jump (Unit: Unit)
Stand6005908298.33%
Walk60017580396.67%
Jump60062656894.60%
Reference [ ]Reference [ ]Reference [ ]Textual Method
Stand95.00%100.00%93.70%98.33%
Walk94.00%94.00%90.60%96.67%
jumping (or approximately jumping)92.25%94.00%96.90%94.60%
Average recognition accuracy of two types94.50%97.00%92.15%97.50%
Average recognition accuracy of three types93.75%96.00%93.73%96.53%
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Share and Cite

Wang, Z.; Jin, X.; Huang, Y.; Wang, Y. Research on the Human Motion Recognition Method Based on Wearable. Biosensors 2024 , 14 , 337. https://doi.org/10.3390/bios14070337

Wang Z, Jin X, Huang Y, Wang Y. Research on the Human Motion Recognition Method Based on Wearable. Biosensors . 2024; 14(7):337. https://doi.org/10.3390/bios14070337

Wang, Zhao, Xing Jin, Yixuan Huang, and Yawen Wang. 2024. "Research on the Human Motion Recognition Method Based on Wearable" Biosensors 14, no. 7: 337. https://doi.org/10.3390/bios14070337

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Phillip McGraw: a Journey into Human Behavior and Psychology

This essay about Phillip McGraw popularly known as Dr. Phil explores his impact on psychology and popular culture. It discusses his evolution from a clinical psychologist to a renowned television personality and best-selling author. McGraw’s approachable style and practical advice have made psychology accessible to a wide audience addressing issues such as mental health relationships and personal growth. Through his television show books and philanthropy McGraw advocates for destigmatizing mental health issues and empowering individuals to confront challenges with resilience and empathy. His legacy highlights the intersection of academic rigor with mass appeal influencing how psychology is understood and applied in everyday life.

How it works

Phillip McGraw known affectionately as Dr. Phil stands as a prominent figure in modern psychology and television captivating audiences with his straightforward approach and deep understanding of human behavior. Born in Vinita Oklahoma in 1950 McGraw’s journey into the realm of psychology began with a passion for understanding the complexities of the human mind. His academic pursuits led him to earn a Ph.D. in clinical psychology from the University of North Texas marking the foundation of his illustrious career.

McGraw’s expertise transcends traditional clinical settings as he seamlessly integrates psychology with media making psychology accessible and engaging for millions worldwide.

His television show “Dr. Phil” which debuted in 2002 catapulted him into fame where he became renowned for his no-nonsense advice and empathetic approach to helping individuals navigate life’s challenges. Through a blend of personal anecdotes psychological insights and practical strategies McGraw empowers his audience to confront their issues head-on fostering a deeper understanding of themselves and their relationships.

Beyond his television persona McGraw has authored several best-selling books that delve into various aspects of human psychology and self-improvement. His writings including titles like “Self Matters” and “Life Code” reflect his commitment to providing practical tools for personal growth and empowerment. McGraw’s ability to distill complex psychological concepts into actionable advice resonates with readers seeking transformative change in their lives.

One of McGraw’s most significant contributions lies in his advocacy for mental health awareness and destigmatization. Through his platform he addresses taboo topics with sensitivity and candor encouraging individuals to seek help without shame or hesitation. By spotlighting issues such as addiction domestic violence and mental illness McGraw amplifies the voices of those often marginalized fostering a community of support and understanding.

McGraw’s impact extends beyond the screen and the pages of his books. His philanthropic endeavors particularly through the Dr. Phil Foundation demonstrate his commitment to improving the lives of others. The foundation supports a range of causes including youth development education and health care initiatives reflecting McGraw’s belief in the transformative power of compassion and generosity.

In essence Phillip McGraw’s journey from small-town psychologist to international icon exemplifies the profound influence one individual can have on understanding and improving the human condition. His ability to blend academic rigor with mass appeal has reshaped the landscape of popular psychology making complex psychological principles accessible and applicable to everyday life. Through his television show books and charitable efforts McGraw continues to inspire millions to embrace personal growth confront challenges with resilience and strive for a deeper understanding of themselves and others.

Phillip McGraw’s legacy transcends the boundaries of traditional psychology leaving an indelible mark on popular culture and the field of mental health advocacy. As he continues to evolve and adapt to societal changes his unwavering dedication to empowering individuals remains a beacon of hope and guidance in an increasingly complex world.

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Creativity – help or hindrance? The impact of product review creativity on perceived helpfulness

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Bibliometrics & citations, view options, recommendations, empirical analysis of roles of perceived leadership styles and trust on team members' creativity.

We model members' creativity with two leadership styles and two trust types.We examine the influence of two leadership styles on two trust types.We show both styles of leadership positively influence both types of trust.We examine the influence of two ...

How do gratifications to read reviews and perceived reviewers’ credibility impact behavioural intentions in fashion e-commerce? A mediating-moderating perspective

This study aims to evaluate the role of gratifications sought from online reviews and perceptions about reviewers in affecting the behavioural intentions (continuance and purchase intentions) in the B2C fashion e-commerce context, ...

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We explore digital creativity by examining the role of enterprise mobile applications.Habitual use and task-technology fit positively influence perceived job performance.Higher organizational agility increases the impact of task-technology fit on job ...

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The impact of the digital revolution on human brain and behavior: where do we stand?


El impacto de la revolución digital sobre el comportamiento y el cerebro humano: ¿dónde estamos, État des lieux de l’impact de la révolution numérique sur le cerveau et le comportement, martin korte.

Department of Cellular Neurobiology, Zoological Institute, TU Braunschweig, Germany; Helmholtz Centre for Infection Research, Neuroinflammation and Neurodegeneration Group, Braunschweig, Germany

This overview will outline the current results of neuroscience research on the possible effects of digital media use on the human brain, cognition, and behavior. This is of importance due to the significant amount of time that individuals spend using digital media. Despite several positive aspects of digital media, which include the capability to effortlessly communicate with peers, even over a long distance, and their being used as training tools for students and the elderly, detrimental effects on our brains and minds have also been suggested. Neurological consequences have been observed related to internet/gaming addiction, language development, and processing of emotional signals. However, given that much of the neuroscientific research conducted up to now relies solely on self-reported parameters to assess social media usage, it is argued that neuroscientists need to include datasets with higher precision in terms of what is done on screens, for how long, and at what age.


Esta visión panorámica describirá los resultados actuales de la investigación en neurociencia acerca de los posibles efectos del empleo de los medios digitales sobre el cerebro, la cognición y el comportamiento humano. Esto es importante debido a la gran cantidad de tiempo que las personas utilizan en los medios digitales. A pesar de varios aspectos positivos de los medios digitales, que incluyen la capacidad de comunicarse sin esfuerzo con sus compañeros, incluso a larga distancia y su utilización como herramientas de capacitación para estudiantes y ancianos, también se han sugerido efectos perjudiciales para el cerebro y la mente. Se han observado consecuencias neurológicas relacionadas con la adicción a internet / juegos, el desarrollo del lenguaje y el procesamiento de señales emocionales. Sin embargo, considerando que gran parte de la investigación de neurociencia realizada hasta la fecha se basa únicamente en parámetros auto-reportados para evaluar el empleo de las redes sociales, se argumenta que los neurocientistas deben incluir conjuntos de datos con mayor precisión en términos de lo que se hace en las pantallas, durante cuánto tiempo y a qué edad.

Les résultats actuels de la recherche en neurosciences sur les effets potentiels de l'utilisation des médias numériques sur le cerveau, la cognition et le comportement, présentés dans cet article sont importants compte tenu du temps considérable que les individus consacrent à ces médias. Malgré les avantages que procurent ces outils, comme le fait de pouvoir communiquer sans effort et sans tenir compte des distances géographiques, de servir d’outil d’apprentissage aux étudiants et d’activités aux personnes âgées, des effets néfastes sur nos cerveaux et nos esprits ont eux aussi été constatés. Des conséquences neurologiques liées à la dépendance à Internet et aux jeux vidéo, au développement du langage et au traitement des signaux émotionnels sont observées. Toutefois, une grande partie de la recherche neuroscientifique menée jusqu'à présent étant basée uniquement sur des paramètres autodéclarés pour évaluer l'usage des réseaux sociaux, les neuroscientifiques devraient inclure des données plus précises en termes de nature de pratique sur les écrans, de temps consacré et d’âge des utilisateurs.

Introduction


One hundred eleven years ago, E. M. Forster published a short story (The Machine Stops, 1909, The Oxford and Cambridge Review ) about a futuristic scenario in which a mysterious machine controls everything, from food supply to information technologies. In a situation that evokes internet and digital media events of today, in this dystopia, all communication is remote and face-to-face meetings no longer happen. The machine controls the mindset, as it makes everybody dependent on it. In the short story, when the machine stops working, society collapses.


The story raises many questions, still relevant today, about the impact of digital media and related technology on our brains. This issue of Dialogues in Clinical Neuroscience explores in a multifaceted manner how, by what means, and with what possible effects digital media use affects brain function—for the good, the bad, and the ugly sides of human existence.


Overall, digital media use, from online gaming to smartphone/tablet or internet use, has revolutionized societies worldwide. In the UK alone, according to data collected by a regulatory agency for communication (Ofcom), 95% of people aged 16 to 24 years old own a smartphone and check it on average every 12 minutes. Estimates suggest that 20% of all adults are online more than 40 hours per week. There is no doubt that digital media, most of all the internet, are becoming important aspects of our modern life. Nearly 4.57 billion people worldwide have access to the internet, according to data published December 31, 2019 on the webpage http://www.internetworldstats.com/stats.htm. The speed of change is astonishing, with an exponential increase in the last decade. How and at what possible costs and/or benefits can our brain and mind adapt?


Indeed, concerns about the effects of digital media use on brain function and structure, as well as physical and mental health, education, social interaction, and politics, are increasing. In 2019, the World Health Organization (WHO) published strict guidelines about children’s screen time. And—announced a law (Assembly Bill 272) that permits schools to restrict smartphone usage. These actions were taken after results were published implicating intensive digital media use in reducing working memory capacity 1 - 3 ; in psychological problems, from depression to anxiety and sleep disorders 4 , 5 ; and in influencing the level of text comprehension while reading on screens. 6 , 7 The latter is a rather surprising example showing that reading complex stories or interconnected facts in a printed book leads to better recall of the story, of details, and of the connection between facts than reading the same text on screen. 7 - 9 The reason for the astonishing results, considering that the words on a light emitting diode (LED) screen or in a printed book are the same, seems to be related to how we use associations of facts with spatial and other sensory cues: the location on a page in a book we read something in addition, for instance, to the fact that each book smells differently seems to boost recall. 9 In addition, the language scientist Naomi Baron, cited in an article by Makin, 10 argues that reading habits are different in such a way that digital environments lead to superficial engagement in text analysis. This possibly depends on the fact that most digital media users glance at and multitask from one item to the next—a habit that might reduce attention span and contribute to the fact that diagnosis of attention-deficit hyperactivity disorder (ADHD) is higher than it was 10 years ago. 1 Is this just a correlation or does it indicate that multitasking with digital media contributes to, or even causes, the higher incidence of ADHD? Two arguments support the hypothesis that intensive digital media use is related to impairments in working memory: simply seeing a smartphone (not even using it) lowers working memory capacity and leads to decreased performance in cognitive tasks, due to the fact that part of the working memory resources are busy ignoring the phone. 11 In addition, the more that people use their smartphones in a multitasking modus (switching quickly between different engagements of the mind), the easier they respond to distraction and indeed perform more poorly in task-switching exams than users who rarely try to multitask. 11 The results have been disputed (see ref 10), and this discrepancy in results might be related to the fact that digital media per se are neither good nor bad for our minds; it is rather how we use digital media. What we use smartphones or any other digital media for and how often are the important parameters to analyze, a point often ignored in this discussion.


Brain plasticity related to the use of digital media


The most straightforward and simple approach to elucidating whether digital media use has a profound effect on the human brain is to explore whether the use of fingertips on touchscreens changes cortical activity in the motor or the somatosensory cortex. Gindrat et al 12 , 13 used this approach. It was already known that cortical space assigned to the tactile receptors on fingertips is influenced by how often the hand is used. 14 For example, string instrument players have more cortical neurons of the somatosensory cortex allotted to the fingers they use in playing the instrument. 15 This so-called “cortical plasticity of sensory representation” is not limited to musicians; for example, it also occurs with often-repeated grasp movements. 16 As repeated finger movements occur with use of touchscreen smartphones, Gindrat et al 12 , 13 used electroencephalography (EEG) to measure cortical potentials resulting from touching tips of the thumb, middle, or index fingers of touchscreen phone users and control subjects who used only non-touch-sensitive mobile phones. Indeed, the results were remarkable, as only touchscreen users showed an increase in the cortical potentials from the thumb and also for the index fingertips. These responses were statistically highly significantly correlated to the intensity of use. For the thumb, the size of cortical representation was correlated even with the day-to-day fluctuations in touchscreen use. These results clearly demonstrate that repetitive use of touchscreens can reshape somatosensory processing in fingertips, and they also indicate that such representation in the thumb can change within a short time frame (days), depending on use.


Taken together, this shows that intensive touchscreen use can reorganize the somatosensory cortex. Therefore, one can conclude that cortical processing is continuously shaped via digital media use. What was not investigated but should be explored in the future is whether such expansion of cortical representation in the fingertips and thumb occurred at the expense of other motor coordination skills. This response is of tremendous importance considering that motor skills are inversely correlated with screen time, due to either competition between cortical space and motor programs or because of an overall lack of exercise (eg, see ref 17).


Influences on the developing brain


Effect on motor skills is one aspect to consider with digital media use, other aspects are effects on language, cognition, and perception of visual objects in the developing brain. In this respect, it is remarkable that Gomez et al 18 showed that details of the development of the visual system can be affected by the content of digital media. To explore this, functional magnetic resonance imaging (fMRI) was used to scan brain from adult subjects who had played the game Pokémon intensively when they were children. It was already known that object and face recognition is achieved in higher visual areas of the ventral visual stream, mainly in the ventral temporal lobe. 19 Typical Pokémon figures are a mixture of animal-like humanized characters and are a unique type of object otherwise not visible in human environments. Only adults with intensive Pokémon experience during childhood showed distinct distributed cortical responsiveness to Pokémon figures in the ventral temporal lobe near face-recognition areas. These data—as a proof of principle—indicate that digital media use can lead to a unique functional and long-lasting representation of digital figures and objects even decades later. Surprisingly, all Pokémon players showed the same functional topography 


in the ventral visual stream for Pokémon figures. Also, here it is not clear whether these data simply show the tremendous plasticity of the brain to add new representations for novel classes of objects to the higher visual areas or whether object representation from intensive digital media use might have negative consequences for face recognition and processing as a consequence of competition for cortical space. In this respect, it is noteworthy that in empathy studies in young adults, a correlation between time spent with digital media and a lower cognitive empathy with other humans has been reported. 20 , 21 Whether due to lack of insight into what other people might think (theory of mind) or to problems with facial recognition or lack of exposure to peers (due to excessive online time) is not currently clear. It should be emphasized that some studies reported no correlation between online time and empathy (for reviews, see refs 22 and 23).


Another area of interest is whether the development of processes related to language (semantics and grammar) is by any means affected by intensive digital media use. It is in this respect worrisome that early extensive screen use in preschoolers can have dramatic influences on language networks, as shown by sophisticated diffusion tensor MRI 24 , 25 ( Figure 1 ). This method provides estimates of white-matter integrity in the brain. In addition, cognitive tasks were tested in preschool children. This was measured in a standardized way by using a 15-item screening tool for observers (ScreenQ), which reflects the screen-based media recommendations of the American Academy of Pediatrics (AAP). ScreenQ scores were then statistically correlated with the diffusion tensor MRI measurement and with cognitive test scores, controlling for age, gender, and household income. Overall, a clear correlation was observed between intensive early childhood digital amedia use and poorer microstructural integrity of white-matter tracts, especially between the Broca and Wernicke areas in the brain ( Figure 1 ). Language comprehension and capacity are highly correlated with the development of these fiber tracts, as reviewed in Grossee et al 26 and Skeide and Friederici. 27 In addition, lower executive functions and lower literacy abilities were observed, even when age and the average household income were matched. Also, digital media use correlated with significantly lower scores in behavioral measures for executive functions. The authors conclude 25 : “Given that screen-based media use is ubiquitous and increasing in children in home, childcare, and school settings, these findings suggest the need for further study to identify the implications for the developing brain, particularly during stages of dynamic brain growth in early childhood.” This study indicates that reading skills might be compromised if fiber tracts between the language areas are not developed to their full extent. Considering that reading ability in children is an excellent predictor of school success, it would also be beneficial to study if ScreenQ scores correlate to school success or to how traditional reading in books compares with reading on screens, in e-books, and on web pages.


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Besides the development of language areas, reading habits might change with the use of electronic media. This change might have implications for new readers and for individuals with reading disabilities. Indeed, this has been explored recently. 28 Here, fMRI was used when children listened to three similar stories in audio, illustrated, or animated format, followed by a test of factual recall. Within- and between-network functional connectivity was compared across formats involving the following: visual perception, visual imagery, language, default mode network (DMN), and cerebellar association. For illustration relative to audio, functional connectivity was decreased within the language network and increased between visual, DMN, and cerebellar networks, suggesting decreased strain on the language network afforded by pictures and visual imagery. Between-network connectivity was decreased for all networks for animation relative to the other formats, particularly illustration, suggesting a bias toward visual perception at the expense of network integration. These findings suggest substantial differences in functional brain network connectivity for animated and more traditional story formats in preschool-age children, reinforcing the appeal of illustrated storybooks at this age to provide efficient scaffolding for language. In addition, deep reading can be influenced by digital media. 29 This shift in reading pattern may threaten the development of deep reading skills in young adults.


A particularly important time for brain development is adolescence, a period when brain areas involved in emotional and social aspects are undergoing intensive changes. Social media might have a profound effect on the adolescent brain due to the fact they allow adolescents to interact with many peers at once without meeting them directly. And indeed, published data indicate a different mode of processing emotions in adolescents, which is highly correlated to the intensity of social media use. This has been shown in the gray matter volume of the amygdala, which processes emotions ( Figure 2 ). 30 , 31 This suggests an important interplay between actual social experiences in online social networks and brain development. 32 Emotion precedence, peer conformity, or acceptance sensitivity might make teenagers in particular vulnerable to fake or shocking news, as well as unlikely self-expectations, or vulnerable as regards regulation of emotions due to unfavorable use of digital media. 33 What is missing here are longitudinal studies to elucidate whether the adolescent brain is differently shaped by social network size online instead of direct personal interaction.


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As a side note, the evidence that violent games do have a profound effect on human behavior is better defined. 34 A meta-analysis of current papers shows that exposure to violent video games is a highly significant risk factor for increased aggressive behavior and for a decrease in empathy and lower levels of prosocial behavior. 34 


Synaptic plasticity


Principally, the study described above supports the notion of high brain plasticity induced by intensive use of digital media. In detail, the effects observed are amazing, but overall, it has been previously shown that the brain changes its functional and structural connectivity with usage, in other words, due to learning, habits, and experience. 35 , 36 To judge this effect on the quality of human cognition and health, the question is more whether our brains—by using digital media extensively—are working in a certain cognitive mode, perhaps at the expense of others that are important. The effects of the brain’s potential to adjust its functional and structural connectivity has been demonstrated in many neuroimaging studies with humans 37 ; for a review, see ref 38. Other studies, including one by Maguire 39 in London taxi drivers, and studies in pianists (as mentioned above) 14 and jugglers 40 show that intensive usage can stimulate the growth of new synaptic connections (“use it”) while at the same time eliminating neuronal synaptic connections that are used less often (“lose it”). 41 , 42 


On the cellular level, this phenomenon has been named synaptic plasticity, reviewed by Korte and Schmitz. 35 It is by now widely accepted that neurons in human cortex and hippocampus, as well as in subcortical areas, are highly plastic, meaning that changes in neuronal activity patterns, for example, generated by intensive training, change synaptic function as well as synaptic structure. Activity-dependent synaptic plasticity alters the efficacy of synaptic transmission (functional plasticity) and modifies the structure and number of synaptic connections (structural plasticity). 35 , 43 , 44 Synaptic plasticity builds the foundation for adjusting the postnatal brain in response to experience and is the cellular implementation for learning and memory processes, as suggested in 1949 from Donald O. Hebb. He proposed that changes in neuronal activity due to usage, training, habit, or learning are stored in assemblies of neurons and not in single nerve cells. 41 Plasticity by this means happens at the network level by altering the synapses between neurons and is therefore called activity-dependent synaptic plasticity. Hebb’s postulate also includes an important rule, predicting that synaptic strength changes when the pre- and postsynaptic neurons show coincident activity (associativity), and this changes the input/output characteristic of neuronal assemblies. Only if these are activated together again can they be remembered. Important is that the synaptic response to a certain brain activity of a given intensity is enhanced; for further details see Magee and Grienberger. 45 This implies that all human activity performed on a regular basis— including use of digital media, social networks, or simply the internet—will have an imprint on the brain, whether for the good, the bad, or the ugly side of human cognitive function depends on the activity itself, or whether it occurs at the expense of other activities. In this respect, linking multitasking mode with cellular synaptic plasticity, Sajikumar et al 46 showed that activation of three inputs impinging on the same neuronal population within a narrow time window (as is the case of humans trying to multitask) leads to the arbitrary strengthening of inputs, and not necessarily the strongest. This means the storage of relevant facts may be compromised if the input to a neuronal network in a particular brain area exceeds its limit of processing power.


Digital media impact on the aging brain


The effects and possible negative or positive aspects of digital media use, culture, and interaction might not only depend on total consumption time and the cognitive domain involved; it might also depend on age. Thus, the negative effects on preschoolers, as reported by Hutton et al, 25 might be quite different from those seen with usage in adults (like addiction) or to the effects observed in the elderly. Therefore, training of the aged brain with digital media might have different consequences than screen time for preschoolers or permanent distraction in adults.


Aging is not only genetically determined, but also dependent on lifestyle and on how the brain is used and trained; for example, see ref 47. One successful attempt involving digital media resulted in an increased attention span in elderly subjects through training response inhibition via computer games. 48 Here, the training was done on a tablet for just 2 months, and significant cognitive effects on lateral inhibition were observed in comparison with a control group. These results correlated with growth processes, seen as greater cortical thickness in the right inferior frontal gyrus (rIFG) triangularis, a brain area associated with lateral inhibition. 49 These effects, probably mediated via processes of structural plasticity depend on time spent performing the training task: the results became better in linear correlation with training time. Overall, it can be summarized that game-based digital training programs might foster cognition in the elderly and is in line with other studies showing that attention training is mediated via increasing the activity in the frontal lobe. 50 Other studies have supported these results by showing that computer training is a possible means to train the brain in older people (>65 years of age), and brain training programs can assist in promoting healthy cognitive aging 51 , 52 (see also ref 53). It will be exciting to probe whether digital media in the future can be used in the elderly to preserve or even increase cognitive capacities, such as attention, that suffer after intensive digital media/multitasking use at younger ages.


Mechanism of addiction and digital media use


In addition to classical substance-use disorders, behavioral addictions are also classified as addictive behavior. 54 The WHO now includes internet-use disorder (IUD) or internet gaming disorder/internet addiction (IGD) in the International Classification of Diseases 11th Revision (ICD-11) , which might in the future also include “smartphone-use disorder” as a behavioral addiction (https://icd.who.int/browse11/l-m/en). Addiction is characterized as a chronic relapsing disorder, depicted by compulsion to seek and use either a substance or a behavior, like gambling. In addition, it includes loss of control in limiting certain behaviors or drug intake, and mostly is associated with the emergence of negative emotions (eg, anxiety, irritability, or dysphoria,) in situations where the drug or behavior is not attainable. Neurologically, addiction is characterized by overall network changes in frontostriatal and frontocingulate circuits. These are also the hallmarks for IGD/IUD addiction. 54 Adolescents in particular might be at risk. 55 For a systematic and more detailed meta-analysis of functional and structural brain changes related to IGD, see the following reviews by Yao et al 56 and D’Hondt et al. 57 


It is also noteworthy that some studies found a correlation between brain anatomy alterations and social networking site (SNS) addiction. 58 It specifically shows that intensive interactions with social media can be correlated to gray-matter alteration of brain areas involved in addictive behavior. Also, other studies reported that intense use of social media can lead to a profound effect on neuronal structures in the human brain, as reviewed in ref 32. Overall, the implications of these data are that neuroscience and psychology research should turn more attention toward the understanding and prevention of online addiction disorders or other maladaptive behaviors related to gaming and social network use.


Neuroenhancement with electronic devices


So far, we have discussed digital media, but electronic devices in general can also be used to directly stimulate the human brain. The difficulty here is that the human brain is not a simple Turing machine, 59 and the algorithm it uses is less clear. For this reason, it is unlikely that our brains can be reprogrammed by digital technologies and that simple stimulation of certain brain areas will increase cognitive abilities. However, deep-brain stimulation as a treatment option for Parkinson disease, depression, or addiction is a different story. 60 - 62 Additionally, research on so-called brain/machine interfaces (BMIs) has shown that with regard to motor functions and the assimilation of artificial tools, eg, robotic/avatar extremities, incorporation in the somatosensory representation of the brain is possible. 63 This works partly because neurons learn to represent artificial devices via processes of activity-dependent synaptic plasticity. 63 This illustrates that, indeed, our sense of self can be altered by electronic technologies to incorporate external devices. Nicolelis and colleagues have recently demonstrated that such an extension of the sense of body in paralyzed patients trained to use BMI devices could allow them to steer the movements of artificial avatar bodies, leading to a clinically relevant recovery. 64 


This does not mean that the human brain can mimic the binary logic or even the algorism of digital devices, but it highlights how digital machines and digital media could have a huge impact on our mental skills and behavior (discussed in depth by Carr 65 ). This impact is also highlighted by the effect of online cloud storage and search engines on human memory performance. A paradigmatic example is a study in which digital natives were made to believe that facts they had been asked to memorize would be stored in online cloud storage. 66 Under this assumption, they performed more poorly than subjects that expected to have to rely only on their own brain memory function (mainly in the temporal lobe), as fMRI analysis illuminated. 66 These results suggest that subcontracting some simple mental searches to internet cloud storage and relying on search engines instead of memory systems in our own brain reduces our ability to memorize and recall facts in a reliable manner.


Human well-being and multitasking


Addiction and neuroenhancement are particular effects of digital media and electronic devices. More common are the effects of multitasking on attention span, concentration, and the capacity of working memory. 11 Processing multiple and continuous incoming streams of information is certainly a challenge for our brains. A series of experiments addressed whether there are systematic differences in information processing styles between chronically heavy and light media multitaskers (MMTs). 6 , 67 The results indicate that heavy MMTs are more susceptible to interference from what are considered irrelevant external stimuli or representations in their memory systems. This led to the surprising result that heavy MMTs performed worse on a task-switching ability test, probably due to reduced ability to filter out interference from irrelevant stimuli. 6 This demonstrates that multitasking, a rapidly growing behavioral trend, is associated with a distinct approach to fundamental information processing. Uncapher et al 6 summarize the consequences of intense multimedia use as follows: “American youth spend more time with media than any other waking activity: an average of 7.5 hours per day, every day. On average, 29% of that time is spent juggling multiple media streams simultaneously (ie, media multitasking). Given that a large number of MMTs are children and young adults whose brains are still developing, there is great urgency to understand the neurocognitive profiles of MMTs.”


On the other hand, it will obviously be important to understand what information processing is necessary for effective learning within the environment of the 21 st century. A growing body of evidence demonstrates that heavy digital MMTs show poorer memory function, increased impulsivity, less empathy, and a higher amount of anxiety. 5 On the neurological side, they show a reduced volume in the anterior cingulate cortex. In addition, current data indicate that switching quickly between different tasks (multitasking) during digital media use can negatively affect academic outcomes. 6 However, one needs to be careful in the interpretation of these results because, as the direction of causality is not clear, media multitasking behavior might also appear more pronounced in people with reduced prefrontal activity and shorter attention span to start with. Here, longitudinal studies are needed. The overall impact of online social media on our natural social skills (from empathy to theory of other people’s minds) is another realm in which we may experience how and to what extent digital media affects our thinking and sensory processing of social signals. Of many studies, one by Turkle 5 should be highlighted here. Turkle used interviews with teenagers or adults who were heavy users of social media and other kinds of virtual environments. One of the outcomes of this study was that extreme use of social media and virtual reality environments can lead to an increase in risk of anxiety, fewer real social interactions, lack of social skills and human empathy, and difficulties in handling solitude. In addition, the people interviewed reported symptoms related to addiction to internet use and digital social media. This mental routine of being “always connected” to hundreds or even thousands of people might indeed be overburdening our brain areas related to social interaction by dramatically expanding the number of people with whom we can closely communicate. The evolutionary constraint might be a group size limit of approximately 150 individuals. 68 This may be the reason for our increase in cortical volume, eg, chimpanzees interact regularly with 50 individuals, but it may also be the limit of what our brains can achieve. In contrast to this evolutionary constraint, we are more or less in continuous contact with a group of people that by far exceeds our neurobiological limit due to social media. What are the consequences of this cortical overtaxing? Anxiety and deficits in attention, cognition, and even memory? Or can we adapt? So far, we have more questions than answers.


Conclusion


The brain is affected by the way we use it. It is hardly a stretch to expect that intensive digital media use will change human brains due to processes of neuronal plasticity. But it is less clear how these new technologies will change human cognition (language skills, IQ, capacity of working memory) and emotional processing in a social context. One limitation is that many studies thus far did not take into account what humans are doing when they are online, what they are seeing, and what type of cognitive interaction is required during screen time. What is clear is that digital media do have an impact on human psychological well-being and cognitive performance, and this depends on total screen time and what people are actually doing in the digital environment. 69 Over the past decade, more than 250 studies have been published trying to elucidate the impact of digital media use; most of these surveys used self-reporting questionnaires that for the most part did not take into account the vastly different activities people experienced online. However, the pattern of use and the total time spent online will have different effects on a person’s health and behavior. 69 Researchers need a more detailed multidimensional map of digital media use. In other words, what is desirable is a more precise measure of what people do when they are online or looking at a digital screen. Overall, the current situation cannot distinguish in most cases between causal effects and pure correlation. Important studies have been started, 70 , 71 and the Adolescent Brain Cognitive Development Study (ABCD study) should be mentioned. It is orchestrated by the National Institutes of Health (NIH) and aims to explore the effect of environmental, social, genetic, and other biological factors affecting brain and cognitive development. The ABCD study will recruit 10 000 healthy children, ages 9 to 10 across the United States, and follow them into early adulthood; for details, see the website https://abcdstudy.org/. The study will include advanced brain imaging to visualize brain development. It will elucidate how nature and nurture interact and how this relates to developmental outcomes such as physical or mental health, and cognitive capabilities, as well as educational success. The size and scope of the study will allow scientists to identify individual developmental trajectories (eg, brain, cognitive, emotional, and academic) and the factors that can affect them, such as the effect digital media use will have on the developing brain.


What remains to be determined is whether the increasing frequency of all users moving toward being knowledge distributors themselves might become a great threat to the acquisition of solid knowledge and the need that each has to develop their own thoughts and to be creative. Or will these new technologies build the perfect bridge to ever more sophisticated forms of cognition and imagination, enabling us to explore new knowledge frontiers that we cannot at the moment even imagine? Will we develop completely different brain circuit arrangements, like we did when humans started to learn to read? Taken together, even if much research is still needed to judge and evaluate possible effects of digital media on human well-being, neuroscience can be of tremendous help to distinguish causal effects from mere correlations.


Acknowledgments

The author declares no potential conflict of interest. I thank Dr Marta Zagrebelsky for critical comments on the manuscript

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  • Published: 16 November 2022

Climate change and human behaviour

Nature Human Behaviour volume  6 ,  pages 1441–1442 ( 2022 ) Cite this article

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Climate change is an immense challenge. Human behaviour is crucial in climate change mitigation, and in tackling the arising consequences. In this joint Focus issue between Nature Climate Change and Nature Human Behaviour , we take a closer look at the role of human behaviour in the climate crisis.

In the late 19th century, the scientist (and suffragette) Eunice Newton Foote published a paper suggesting that a build-up of carbon dioxide in the Earth’s atmosphere could cause increased surface temperatures 1 . In the mid-20th century, the British engineer Guy Callendar was the first to concretize the link between carbon dioxide levels and global warming 2 . Now, a century and a half after Foote’s work, there is overwhelming scientific evidence that human behaviour is the main driver of climatic changes and global warming.

a research paper about human behavior

The negative effects of rising temperatures on the environment, biodiversity and human health are becoming increasingly noticeable. The years 2020 and 2016 were among the hottest since the record keeping of annual surface temperatures began in 1880 (ref. 3 ). Throughout 2022, the globe was plagued by record-breaking heatwaves. Even regions with a naturally warm climate, such as Pakistan or India, experienced some of their hottest days much earlier in the year — very probably a consequence of climate change 4 . According to the National Centers for Environmental Information of the United States, the surface global temperature during the decade leading up to 2020 was +0.82 °C (+1.48 °F) above the 20th-century average 5 . It is clear that we are facing a global crisis that requires urgent action.

During the Climate Change Conference (COP21) of the United Nations in Paris 2015, 196 parties adopted a legally binding treaty with the aim to limit global warming to ideally 1.5 °C and a maximum of 2 °C, compared to pre-industrial levels. A recent report issued by the UN suggests that we are very unlikely to meet the targets of the Paris Agreement. Instead, current policies are likely to cause temperatures to increase up to 2.8 °C this century 6 . The report suggests that to get on track to 2 °C, new pledges would need to be four times higher — and seven times higher to get on track to 1.5 °C. This November, world leaders will meet for the 27th time to coordinate efforts in facing the climate crisis and mitigating the effects during COP27 in Sharm El-Sheikh, Egypt.

This Focus issue

Human behaviour is not only one of the primary drivers of climate change but also is equally crucial for mitigating the impact of the Anthropocene. In 2022, this was also explicitly acknowledged in the report of the Intergovernmental Panel on Climate Change (IPCC). For the first time, the IPCC directly discussed behavioural, social and cultural dynamics in climate change mitigation 7 . This joint Focus highlights some of the aspects of the human factor that are central in the adaptation to and prevention of a warming climate, and the mitigation of negative consequences. It features original pieces, and also includes a curated collection of already published content from across journals in the Nature Portfolio.

Human behaviour is a neglected factor in climate science

In the light of the empirical evidence for the role of human behaviour in climatic changes, it is curious that the ‘human factor’ has not always received much attention in key research areas, such as climate modelling. For a long time, climate models to predict global warming and emissions did not account for it. This oversight meant that predictions made by these models have differed greatly in their projected rise in temperatures 8 , 9 .

Human behaviour is complex and multidimensional, making it difficult — but crucial — to account for it in climate models. In a Review , Brian Beckage and colleagues thus look at existing social climate models and make recommendations for how these models can better embed human behaviour in their forecasting.

The psychology of climate change

The complexity of humans is also reflected in their psychology. Despite an overwhelming scientific consensus on anthropogenic climate change, research suggests that many people underestimate the effects of it, are sceptical of it or deny its existence altogether. In a Review , Matthew Hornsey and Stephan Lewandowsky look at the psychological origins of such beliefs, as well as the roles of think tanks and political affiliation.

Psychologists are not only concerned with understanding and addressing climate scepticism but are also increasingly worried about mental health consequences. Two narrative Reviews address this topic. Neil Adger et al. discuss the direct and indirect pathways by which climate change affects well-being, and Fiona Charlson et al. adopt a clinical perspective in their piece. They review the literature on the clinical implications of climate change and provide practical suggestions for mental health practitioners.

Individual- and system-level behaviour change

To limit global warming to a minimum, system-level and individual-level behaviour change is necessary. Several pieces in this Focus discuss how such change can be facilitated.

Many interventions for individual behaviour change and for motivating environmental behaviour have been proposed. In a Review , Anne van Valkengoed and colleagues introduce a classification system that links different interventions to the determinants of individual environmental behaviour. Practitioners can use the system to design targeted interventions for behaviour change.

Ideally, interventions are scalable and result in system-level change. Scalability requires an understanding of public perceptions and behaviours, as Mirjam Jenny and Cornelia Betsch explain in a Comment . They draw on the experiences of the COVID-19 pandemic and discuss crucial structures, such as data observatories, for the collection of reliable large-scale data.

Such knowledge is also key for designing robust climate policies. Three Comments in Nature Climate Change look at how insights from behavioural science can inform policy making in areas such as natural-disaster insurance markets , carbon taxing and the assignment of responsibility for supply chain emissions .

Time to act

To buck the trend of rising temperatures, immediate and significant climate action is needed.

Natural disasters have become more frequent and occur at ever-closer intervals. The changing climate is driving biodiversity loss, and affecting human physical and mental health. Unfortunately, the conversations about climate change mitigation are often dominated by Global North and ‘WEIRD’ (Western, educated, industrialized, rich and democratic) perspectives, neglecting the views of countries in the Global South. In a Correspondence , Charles Ogunbode reminds us that climate justice is social justice in the Global South and that, while being a minor contributor to emissions and global warming, this region has to bear many of the consequences.

The fight against climate change is a collective endeavour and requires large-scale solutions. Collective action, however, usually starts with individuals who raise awareness and drive change. In two Q&As, Nature Human Behaviour entered into conversation with people who recognized the power of individual behaviour and took action.

Licypriya Kangujam is a 10-year-old climate activist based in India. She tells us how she hopes to raise the voices of the children of the world in the fight against climate change and connect individuals who want to take action.

Wolfgang Knorr is a former academic who co-founded Faculty for a Future to help academics to transform their careers and address pressing societal issues. In a Q&A , he describes his motivations to leave academia and offers advice on how academics can create impact.

Mitigation of climate change (as well as adaptation to its existing effects) is not possible without human behaviour change, be it on the individual, collective or policy level. The contents of this Focus shed light on the complexities that human behaviour bears, but also point towards future directions. It is the duty of us all to turn this knowledge into action.

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a research paper about human behavior

ORIGINAL RESEARCH article

Evaluating undesired scratching in domestic cats: a multifactorial approach to understand risk factors.

Yasemin Salgirli Demirbas,

  • 1 Department of Physiology, Faculty of Veterinary Medicine, Ankara University, Ankara, Türkiye
  • 2 Department of Psychology, University of Prince Edward Island, Charlottetown, PE, Canada
  • 3 Egas Moniz Center for Interdisciplinary Research (CiiEM), Egas Moniz School of Health and Science, Almada, Portugal
  • 4 Ceva Santé Animale, Libourne, France

Introduction: Despite being a natural feline behavior, scratching can become undesirable from a human perspective when directed at household items. This complex behavior can stem from various motivations, ranging from individual cat characteristics to environmental factors. This study investigates the factors influencing the increased level of undesirable scratching behavior in domestic cats, considering both cat-related and environmental aspects.

Methods: Data from 1,211 cats were collected for this study. An online questionnaire comprising three sections was utilized. The first section gathered caregiver demographics, while the subsequent section examined aspects of cats’ daily routines, social interactions, environments, behaviours, and temperaments. The final section assessed the frequency and intensity of undesirable scratching behavior in cats. Scratching behavior was evaluated based on a combined scratching index.

Results: The study suggests that the presence of a child may be associated with scratching episodes in the home environment. Additionally, factors such as play duration, playfulness, and nocturnal activity were identified as significant contributors to heightened scratching levels ( p  ≤ 0.05). Aggressiveness and disruptiveness also played significant roles in increased scratching behavior ( p  ≤ 0.05). The location of scratching posts emerged as a significant factor, with posts placed in areas frequented by the cat being more effective in redirecting scratching behavior ( p  ≤ 0.05).

Discussion: This study reveals several significant associations between cat characteristics, nocturnal activity and play, as well as the environment. It underscores the multifaceted nature of undesirable scratching behavior and emphasizes the importance of comprehensively understanding both the individual characteristics of the cat and its environment to effectively address this behavior.

1 Introduction

The domestic cat exhibits sociability as a trait, forming and maintaining social bonds with humans despite not being socially dependent ( 1 – 3 ). As a result of common social history with humans for almost 10,000 years, they can easily adapt to human environments even though most of their behavioural biology still mimics that of their solitary ancestors ( 4 ). Domestic cats have a complex nature, as they are both social animals and possess strong territorial instincts. This complexity often leads people to misinterpret cats’ behavioural and environmental needs, regardless of their level of theoretical knowledge ( 5 , 6 ). Scratching behavior, which is included in the normal behavioural repertoire of cats, is one of the most apparent examples of this misinterpretation, as this behavior is often perceived as a behavioural problem by caregivers ( 7 , 8 ).

Undesired scratching, a behavior characterized by the destructive targeting of household items ( 9 , 10 ), poses a significant threat to feline welfare, often prompting misperceptions and interventions within domestic settings. Caregivers often feel frustrated due to the destructive impact of scratching behavior on their living space ( 11 ). This frustration can heighten when both the cat’s stress levels and scratching behavior increase simultaneously in response to confrontational interventions such as positive punishment. In such cases, scratching behavior may serve as a marking behavior in reaction to heightened social tension ( 10 , 12 ). This frustration may escalate to extreme measures taken by caregivers, such as onychectomy, relinquishment of the cat, or even euthanasia ( 8 , 13 ). Despite widespread opposition from veterinary authorities to onychectomy ( 14 ), it remains a contentious approach for managing undesirable scratching, particularly when the alternatives involve euthanizing the cat ( 15 , 16 ).

Several studies have investigated the main causes of undesired scratching by considering various aspects. Some studies examined environmental stressors ( 17 ) and needs of cats ( 10 ), while others focused on management strategies and demographics of cats and their caregivers ( 12 , 18 ). The only proposed link between cat demographics and undesired scratching in these studies was age, i.e., a higher possibility of scratching was observed in younger cats ( 10 , 12 ). The absence of suitable scratching materials in the environment and a lack of understanding of cat ethology are proposed as major contributors to the development of this problem ( 12 ). Positive punishment was further reported to be associated with increased level of scratching ( 12 ). On the other hand, the use of positive reinforcement methods ( 10 ) or feline synthetic pheromones ( 18 – 20 ) are suggested to help reduce undesired scratching.

In cat ethology, scratching serves many purposes such as maintaining claw health, provision of safety by marking and social communication ( 10 , 21 ). Therefore, the underlying motivation of scratching is multifactorial and depends not only on social and physical factors but also on the individual cat. Given that scratching is an essential element of the natural behavior of cats, it is crucial to understand cat related factors that may cause an increase in intensity or frequency of this behavior, to ensure good welfare of cats.

This study aims to comprehensively evaluate undesired scratching by assessing the intensity and frequency of this behavior, as opposed to assessing its presence vs. absence. Furthermore, the study seeks to shed light on the risk factors related to cat behaviours and environmental factors that may be associated with increased scratching levels in the home environment. The hypothesis posits that certain behavioural characteristics of cats could be significantly linked to higher levels of scratching behavior. Thus, this research seeks to offer a deeper understanding of the reasons behind undesired scratching, ultimately providing valuable insights for the development of more effective and tailored interventions to enhance the well-being of cats and strengthen their bonds with their human companions.

2 Materials and methods

2.1 study design.

This study reports the further analysis of data from a blinded, randomized and controlled consumer study which has already been published ( 18 ). The protocol underwent review by the ethics committee, and the collection of personal data adhered to all relevant legal obligations concerning the protection of personal data and privacy. This included compliance with the Law of 1978 and the GDPR, as well as following the recommendations and guidelines of the Commission Nationale Informatique et Libertés (CNIL).

2.2 Questionnaire design

To collect relevant data, a questionnaire which encompassed a total of three main parts was designed. Part I focused on caregiver demographics and Part II explored diverse facets of the daily lives and behavior of cats. This included their daily schedules, social engagements, physical surroundings, behaviours and temperament. Part III centered on the level of the undesired scratching behavior displayed by the cats. This part included questions regarding the frequency and intensity of undesired scratching events. To assess the frequency of undesired scratching, caregivers were instructed to recall any instances they directly witnessed or inferred from new damage noticed over the past week. A semi-quantitative scale ranging from 0 (Never) to 6 (Multiple times per day) was used to measure the frequency of scratching. Additionally, caregivers rated the intensity of scratching on a visual analog scale (VAS) from 1 to 10, considering factors like duration and damage extent. A minimum intensity rating of 1 was mandated since any scratching activity (excluding never) was presumed to possess some degree of intensity. This methodology mitigated bias in the Global Index Score, ensuring that any frequency greater than zero corresponded to a certain level of intensity (Please see the Supplementary Material for the questionnaire).

2.3 Participants

The study was conducted in France, and the participants were recruited from a panel of cat caregivers. Potential participants were contacted via email and had to meet specific inclusion criteria to participate in the study. The inclusion criteria were as follows: (1) being 18 years or older and providing signed consent to participate, (2) owning a cat that presented undesired scratching for at least 1 month, with a minimum frequency of two episodes per week (indoor scratching), (3) having only one cat to avoid potential stress from multi-cat households, and (4) having at least one scratching post, cat tree, or another scratching device that was consistently available to the cat in order to reliably evaluate the extent of scratching on household items.

2.4 Questionnaire distribution

The questionnaire was distributed via a web application to caregivers, who were notified of their availability by email. The participants received the eligibility questionnaire by email with the selection questions and were asked if they would like to participate in the study. Reminders were sent via email and SMS 24 h after questionnaire availability, giving caregivers another 24 h to complete it.

2.5 Ethical considerations

The study was conducted following ethical guidelines, and informed consent was obtained from all participants. The anonymity of respondents was maintained throughout the questionnaire process to ensure confidentiality and encourage honest responses.

2.6 Statistical analysis

SAS 9.4 was utilized as a statistical software program. The collected questionnaire data were analyzed to explore the associations between various factors and undesired scratching behavior in domestic cats.

The scratching behavior levels for frequency, intensity, and index of each cat were categorized. In the assessment of frequency, a scale between 0 and 6 was used, and cat caregivers were asked to mark the appropriate option regarding the frequency of scratching behavior of their cats (6: Every day, more than twice a day; 5: Every day, once or twice a day; 4: Almost every day; 3: Every other day; 2: Twice a week; 1: Once a week; 0: Never). Intensity was assessed using a Visual Analog Scale (VAS), which is a subjective measurement tool. On this scale, participants were asked to rate the intensity of scratching. The scale ranges from 1 to 10, where 1 represents an extremely low intensity, and 10 represents an extremely high intensity of scratching. The scratching index, which is obtained by multiplying the frequency and intensity is used to combine two different aspects of the scratching behavior to create a single value ( 22 ). The Cat Behavior Issues Assessment Scale (CABIAS™) is a validated scale used to assess common problem behaviours in cats, such as urine marking, scratching, fear, and issues related to cohabitation between cats ( 23 ). CABIAS employs an index score as a scoring system that combines aspects of the frequency and intensity of the problem behavior based on the previous week of observation.

Descriptive analyses were conducted, and comparisons of risk factors and cat characteristics based on scratching behavior levels (low and high) were performed using statistical tests. All qualitative parameters were assessed with a Chi-square test, while the sole quantitative parameter (weight) was analyzed using a Wilcoxon test. Qualitative parameters encompassed questions pertaining to cat characteristics including sociability, disruptiveness, lethargy, vivacity, boisterousness, and tranquility, alongside their daily routines such as play, activity levels, grooming habits, and dietary patterns. Additionally, aspects related to behavior, such as biting history, litter box issues, and preferred scratching locations, were also considered within the qualitative framework. Environmental elements, such as the placement of scratching posts and the presence of children, as well as demographic information including breed, gender, neutering status, and Body Condition Score (BCS), were further explored as qualitative parameters (For detailed questionnaire information, please refer to the Supplementary Material ).

3.1 Study population

This study was a sub-study of a previously published controlled customer study ( 18 ). In this study, exclusive focus was placed on the data collected on Day 0, serving as the initial baseline before any treatment was administered to the cats. The study population consisted of 1,211 cats. Out of the initially recruited 1,415 cats, 204 were excluded due to factors such as changes in residence, missing baseline data, or unavailability of a scratching post.

Cats were divided into three groups, each with a similar number of individuals, based on the distribution of a specified index. The intermediate group ( N  = 356) was excluded from further analysis, resulting in the creation of two distinct categories representing cats with either high or low scratching behavior. This separation was done to enable a more straightforward comparison between cats exhibiting low and high levels of scratching. Such categorization aligns with the study’s goal of examining factors associated with extreme scratching behavior, concentrating the research question on these specific behavioural categories ( Table 1 ).

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Table 1 . Index distribution.

3.2 Cat demographics and characteristics

No significant difference was observed between purebred and mixed-breed cats, gender, neutering status, body condition score, and actual weight concerning the scratching index ( p  ≥ 0.05).

All questions related to the characteristics of the cats were categorized into seven main groups: disruptive, lethargic, apprehensive, vivacious, boisterous, social, and tranquil ( Figure 1 ). Disruptiveness was found to be significantly related to a high scratching level ( p  ≤ 0.01). Moreover, sub-characteristics such as aggressiveness and destructiveness were also found to be significantly associated with a high scratching level ( p  ≤ 0.01). No other main or sub-characteristic had a significant effect on scratching level except for the sub-characteristics active ( p  ≤ 0.05). The main characteristic ‘boisterous’ and the sub-characteristic “playful” showed a trend towards significance ( p  = 0.058). In the high scratching cat profile group, 58% of the cats were characterized as disruptive. In the high scratching cat profile group, 58% of the cats were characterized as disruptive.

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Figure 1 . Cat characteristics according to level of index scratching.

3.3 Daily routines and environment

The analysis of our study revealed significant effects of different factors on scratching behavior among the subjects. The duration of play, nocturnal activity, the presence of children ( p  ≤ 0.05; Figure 2 ), as well as the placement of the cat tree, the availability of scratching posts, and preferences for scratching areas were identified as factors significantly associated with the level of scratching ( p  ≤ 0.05; Table 2 ).

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Figure 2 . Effect of different factors ( A : Child presence at home, B : Cat activity level at night, C : Playfulness of the cat, D : Playing duration) on low and high scratching profiles.

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Table 2 . Answers related to scratching location.

4 Discussion

The present study aimed to investigate the factors influencing the level of undesired scratching behavior in domestic cats, adopting a multifactorial approach that considered various behavioural and environmental aspects. The findings reveal a significant association between specific factors and cats in high scratching profiles.

One noteworthy finding is the influence of the presence of a child at home on the high level of scratching behavior. It appears that the presence of a child in the household could potentially contribute to heightened stress levels, thereby leading to more frequent and intense scratching episodes ( 24 ). This outcome aligns with previous research suggesting that the presence of children, particularly during specific developmental stages, might amplify the likelihood of undesirable scratching incidents within the home ( 25 ). Conversely, it is crucial to highlight that presence of children have been documented as a prominent factor behind the relinquishment or return of adopted pets ( 26 , 27 ). While most studies predominantly concentrate on the well-being and health of the human residents sharing the same household with cats, these findings underscore the significance of evaluating the quality of life for both constituents – humans and pets – to ensure the establishment of a harmonious environment. One additional factor that should be discussed here is that, within the scope of this study, no detailed investigation has been conducted regarding children-cat interaction. Specifically, the presence of either the child or the cat at the beginning, as well as the age of the children interacting with the cat, was not examined. Consequently, further research is required to determine whether the cat’s behavior is influenced by the arrival of a new child as a family member or is generally associated with the intrinsic presence of the child. Additionally, exploring whether the age of the child contributes to scratching behavior is a factor that warrants further investigation.

Similar to a recent study ( 28 ), this study also underscores the importance of play behavior in preventing undesired problem behaviours exhibited by cats in the household environment. According to the findings of this study, factors such as the duration of play, playfulness, and nocturnal activity were identified as influencing the level of scratching behavior. This suggests that cats characterized by increased playfulness and engaged in prolonged play and activity sessions tend to exhibit heightened scratching activities. A potential explanation for the association between heightened activity/play and increased scratching involves sustained sympathetic arousal, which may be connected to vigilance and stress, thereby amplifying marking-induced scratching behavior ( 29 , 30 ). Play itself holds a crucial role in the well-being of cats, serving as an outlet for their inherent hunting and exploration instincts ( 31 , 32 ). Nevertheless, in the wild, individual play is intertwined with predation, necessitating a heightened arousal level for repetitive yet brief periods ( 33 , 34 ). The observed association between intensified play and heightened nocturnal activity may suggest prolonged or inappropriate play routines for cats, potentially leading to increased stress and irritability. This result may underscore the significance of implementing appropriate play routines and periods for cats as it is stated above ( 32 ).

Inadequate or insufficient play and hunting opportunities may further result in frustration, prompting increased scratching as a stress release mechanism ( 35 , 36 ). Additionally, frustration can serve as a potential underlying factor for disruptive behavior, which is in turn associated with a higher level of scratching behavior. This observation posits that cats with a lower threshold for frustration may exhibit heightened scratching responses as a manifestation of stress, or the environment’s failure to meet basic needs may induce frustration, leading to elevated aggressiveness and disruptiveness ( 37 ). Emphasizing the significance of the five pillars of cat care, including the provision of appropriate play opportunities ( 34 ), is crucial. Promoting regular and brief interactive play sessions, coupled with offering suitable toys, can alleviate stress and consequently reduce undesirable scratching behaviours.

In this study, the location of the scratching post emerged as a significant factor influencing scratching behavior, It was further revealed that the scratching post was situated in the same room where scratching occured for both low and high level scratchers. Given that scratching behavior in cats typically manifests in socially significant areas, it may be inferred that the motivation behind this behavior serves as a means of expressing their underlying emotional state ( 38 , 39 ). Cats may prefer specific locations for scratching that align with their territorial and marking patterns. Providing well-positioned scratching posts in areas frequented by the cat may help redirect scratching to more appropriate surfaces, reducing damage to household items ( 40 ).

While this study provides valuable insights into scratching behavior in domestic cats, it is important to acknowledge several limitations. Firstly, relying on caregiver-reported data introduces potential biases due to subjective interpretation and recall biases inherent in such reports. However, it is worth noting that the study assessed scratching behavior based on observations over the previous 7 days, which may help mitigate bias stemming from caregivers’ memory. Additionally, previous research has demonstrated the reliability of the assessment scale across different caregivers within households, suggesting that memory-related biases may not significantly impact the scores ( 23 ). Moreover, given the significant role of caregiver perspectives in shaping feline welfare, bias resulting from caregiver perception may still offer valuable insights into factors related to undesired scratching. Even if this assessment relies on caregiver reports, it is important to keep in mind that this behavior is never directly assessed by the veterinarian and is always based on caregiver reports or complaints. Another limitation lies in the adoption of a cross-sectional research design. Although this design allows for identifying associations between factors and scratching behavior, longitudinal investigations are crucial for understanding the temporal dynamics and causal pathways underlying undesired scratching behavior in domestic cats. Lastly, the lack of detailed exploration into the nuances of children-cat interaction, including factors such as the age of the child and the timing of their introduction to the cat, limits our understanding of their influence on scratching behavior in domestic cats. Addressing these limitations in future studies is essential for advancing our comprehensive understanding of the multifaceted factors contributing to undesired scratching behavior in the home environment.

In conclusion, this study unveils the intricate and multifaceted nature of undesired scratching behavior in domestic indoor cats. Gaining insights into these contributing factors is paramount for cat caregivers, as it enables the implementation of targeted interventions to encourage appropriate scratching and improve the overall well-being of their feline companions. The pivotal role of addressing both the physical and social needs of cats emerges as a critical strategy in mitigating undesirable behaviours. This holistic approach ensures a comprehensive understanding and effective management of scratching-related issues in domestic cats.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The animal studies were approved by Ceva Santé Animale Committee (ref CFAEC-2022-08). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent was obtained from the owners for the participation of their animals in this study.

Author contributions

YD: Conceptualization, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing. JP: Conceptualization, Methodology, Supervision, Writing – review & editing. XJ: Data curation, Formal analysis, Methodology, Visualization, Writing – review & editing. LM: Data curation, Formal analysis, Software, Visualization, Writing – review & editing. SE: Conceptualization, Methodology, Writing – review & editing. GG: Conceptualization, Data curation, Investigation, Methodology, Supervision, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article.

Acknowledgments

The authors recognize and appreciate the collaboration of the caregivers who took part in this research.

Conflict of interest

XJ, LM, and SE were employed by Ceva Santé Animale.

The remaining 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.

The authors declare that this study received funding from Ceva Santé Animale. The funder had the following involvement in the study: data collection.

Publisher’s note

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.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fvets.2024.1403068/full#supplementary-material

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Keywords: domestic cat, undesired scratching, cat behavior, environmental factors, behavioural characteristics

Citation: Demirbas YS, Pereira JS, De Jaeger X, Meppiel L, Endersby S and da Graça Pereira G (2024) Evaluating undesired scratching in domestic cats: a multifactorial approach to understand risk factors. Front. Vet. Sci . 11:1403068. doi: 10.3389/fvets.2024.1403068

Received: 18 March 2024; Accepted: 24 May 2024; Published: 03 July 2024.

Reviewed by:

Copyright © 2024 Demirbas, Pereira, De Jaeger, Meppiel, Endersby and da Graça Pereira. 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: Yasemin Salgirli Demirbas, [email protected]

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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