similarities between hypothesis and thesis

The Real Differences Between Thesis and Hypothesis (With table)

A thesis and a hypothesis are two very different things, but they are often confused with one another. In this blog post, we will explain the differences between these two terms, and help you understand when to use which one in a research project.

As a whole, the main difference between a thesis and a hypothesis is that a thesis is an assertion that can be proven or disproven, while a hypothesis is a statement that can be tested by scientific research. 

We probably need to expand a bit on this topic to make things clearer for you, let’s start with definitions and examples.

Definitions

As always, let’s start with the definition of each term before going further.

A thesis is a statement or theory that is put forward as a premise to be maintained or proved. A thesis statement is usually one sentence, and it states your position on the topic at hand.

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The best way to understand the slight difference between those terms, is to give you an example for each of them.

If you are writing a paper about the effects of climate change on the environment, your thesis might be “Climate change is causing irreparable damage to our planet, and we must take action to prevent further damage”.

If your hypothesis is correct, then further research should be able to confirm it. However, if your hypothesis is incorrect, research will disprove it. Either way, a hypothesis is an important part of the scientific process.

The word “hypothesis” comes from the Greek words “hupo,” meaning “under”, and “thesis” that we just explained.

Argumentation vs idea

A thesis is usually the result of extensive research and contemplation, and seeks to prove a point or theory.

A hypothesis is only a statement that need to be tested by observation or experimentation.

5 mains differences between thesis and hypothesis

Thesis and hypothesis are different in several ways, here are the 5 keys differences between those terms:

So, in short, a thesis is an argument, while a hypothesis is a prediction. A thesis is more detailed and longer than a hypothesis, and it is based on research. Finally, a thesis must be proven, while a hypothesis does not need to be proven.

Is there a difference between a thesis and a claim?

Is a hypothesis a prediction.

No, a hypothesis is not a prediction. A prediction is a statement about what you think will happen in the future, whereas a hypothesis is a statement about what you think is causing a particular phenomenon.

What’s the difference between thesis and dissertation?

A thesis is usually shorter and more focused than a dissertation, and it is typically achieved in order to earn a bachelor’s degree. A dissertation is usually longer and more comprehensive, and it is typically completed in order to earn a master’s or doctorate degree.

What is a good thesis statement?

I am very curious and I love to learn about all types of subjects. Thanks to my experience on the web, I share my discoveries with you on this site :)

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Thesis Vs Hypothesis: Understanding The Basis And The Key Differences

Hypothesis vs. thesis: They sound similar and seem to discuss the same thing. However, these terms have vastly different meanings and purposes. You may have encountered these concepts in school or research, but understanding them is key to executing quality work. 

In this article, I’ll discuss hypothesis vs. thesis, break down their differences, and show you how to apply this knowledge to create quality written works. Let’s get to it!

Thesis vs. Hypothesis: Understanding the Basis

The power of a thesis.

A thesis statement is typically found at the end of the introduction in an essay or research paper, succinctly summarizing the overarching theme.

Crafting a strong thesis

Hypothesis: the scientific proposition.

In contrast, a hypothesis is a tentative proposition or educated guess. It is the initial step in the scientific method, where researchers formulate a hunch to test their assumptions and theories. 

Formulating a hypothesis

Key differences between thesis vs. hypothesis, 1. nature of statement, 3. testability, 4. research stage, 6. examples.

These differences highlight the distinct roles that the thesis and hypothesis play in academic writing and scientific research, with one providing a point of argumentation and the other guiding the scientific inquiry process.

Can a hypothesis become a thesis?

Do all research papers require a thesis, can a thesis be proven wrong.

Yes. The purpose of a thesis is not only to prove but also to encourage critical analysis. It can be proven wrong with compelling counterarguments and evidence.

How long should a thesis statement be?

Is a hypothesis only used in scientific research.

Although hypotheses are typically linked to scientific research, they can also be used to verify assumptions and theories in other areas.

Can a hypothesis be vague?

No. When creating a hypothesis, it’s important to make it clear and able to be tested. Developing experiments and making conclusions based on the results can be difficult if the hypothesis needs clarification.

Final Thoughts

In conclusion, understanding the differences between a hypothesis and a thesis is vital to crafting successful research projects and academic papers. While they may seem interchangeable at first glance, these two concepts serve distinct purposes in the research process. 

So, the next time you embark on a research project, take the time to ensure that you understand the fundamental difference between a hypothesis and a thesis. Doing so can lead to more focused, meaningful research that advances knowledge and understanding in your field.

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While Sandel argues that pursuing perfection through genetic engineering would decrease our sense of humility, he claims that the sense of solidarity we would lose is also important.

This thesis summarizes several points in Sandel’s argument, but it does not make a claim about how we should understand his argument. A reader who read Sandel’s argument would not also need to read an essay based on this descriptive thesis.  

Broad thesis (arguable, but difficult to support with evidence) 

Michael Sandel’s arguments about genetic engineering do not take into consideration all the relevant issues.

This is an arguable claim because it would be possible to argue against it by saying that Michael Sandel’s arguments do take all of the relevant issues into consideration. But the claim is too broad. Because the thesis does not specify which “issues” it is focused on—or why it matters if they are considered—readers won’t know what the rest of the essay will argue, and the writer won’t know what to focus on. If there is a particular issue that Sandel does not address, then a more specific version of the thesis would include that issue—hand an explanation of why it is important.  

Arguable thesis with analytical claim 

While Sandel argues persuasively that our instinct to “remake” (54) ourselves into something ever more perfect is a problem, his belief that we can always draw a line between what is medically necessary and what makes us simply “better than well” (51) is less convincing.

This is an arguable analytical claim. To argue for this claim, the essay writer will need to show how evidence from the article itself points to this interpretation. It’s also a reasonable scope for a thesis because it can be supported with evidence available in the text and is neither too broad nor too narrow.  

Arguable thesis with normative claim 

Given Sandel’s argument against genetic enhancement, we should not allow parents to decide on using Human Growth Hormone for their children.

This thesis tells us what we should do about a particular issue discussed in Sandel’s article, but it does not tell us how we should understand Sandel’s argument.  

Questions to ask about your thesis 

• Is the thesis truly arguable? Does it speak to a genuine dilemma in the source, or would most readers automatically agree with it?  
• Is the thesis too obvious? Again, would most or all readers agree with it without needing to see your argument?  
• Is the thesis complex enough to require a whole essay's worth of argument?  
• Is the thesis supportable with evidence from the text rather than with generalizations or outside research?  
• Would anyone want to read a paper in which this thesis was developed? That is, can you explain what this paper is adding to our understanding of a problem, question, or topic?
• picture_as_pdf Thesis

Home » Education » Difference Between Thesis and Hypothesis

Difference Between Thesis and Hypothesis

Main difference –  thesis vs hypothesis                           .

Thesis and hypothesis are two common terms that are often found in research studies. Hypothesis is a logical proposition that is based on existing knowledge that serves as the starting point of an investigation. A thesis is a statement that is put forward as a premise to be maintained or proved. The main difference between thesis and hypothesis is that thesis is found in all research studies whereas a hypothesis is mainly found in experimental quantitative research studies.

This article explains,

1. What is a Thesis?      – Definition, Features, Function

2. What is a Hypothesis?      – Definition, Features, Function

What is a Thesis

The word thesis has two meanings in a research study. Thesis can either refer to a dissertation or a thesis statement. Thesis or dissertation is the long essay or document that consists of the research study.  Thesis can also refer to a theory or statement that is used as a premise to be maintained or proved.

The thesis statement in a research article is a sentence found at the beginning of the paper that presents the main argument of the paper. The rest of the document will gather, organize and present evidence to support this argument. The thesis statement will basically present the topic of the paper and indicate what position the researcher is going to take in relation to this topic. A thesis statement can generally be found at the end of the first paragraph (introductory paragraph) of the paper.

What is a Hypothesis

A hypothesis is a logical assumption based on available evidence. Hypothesis is defined as “a supposition or proposed explanation made on the basis of limited evidence as a starting point for further investigation” in the Oxford dictionary and as “an idea or theory that is not proven but that leads to further study or discussion” in the Merriam-Webster dictionary. In simple words, it is an educated guess that is not proven with concrete scientific evidence. Once it is scientifically tested and proven, it becomes a theory. However, it is important to note that a hypothesis can be accurate or inaccurate.

Hypotheses are mostly used in experiments and research studies. However, hypotheses are not used in every research study. They are mostly used in quantitative research studies  that deal with experiments. Hypotheses are often used to test a specific model or theory . They can be used only when the researcher has sufficient knowledge about the subject since hypothesis are always based on the existing knowledge. Once the hypothesis is built, the researcher can find and analyze data and use them to prove or disprove the hypothesis.

Thesis: A thesis is a “statement or theory that is put forward as a premise to be maintained or proved” or a “long essay or dissertation involving personal research, written by a candidate for a university degree” (Oxford dictionary).

Hypothesis: A hypothesis is “a supposition or proposed explanation made on the basis of limited evidence as a starting point for further investigation” (Oxford dictionary).

Thesis: Thesis statement can be found in all research papers.

Hypothesis: Hypotheses are usually found in experimental quantitative research studies.

Thesis: Thesis statement may explain the hypothesis and how the researcher intends to support it.

Hypothesis: Hypothesis is an educated guess based on the existing knowledge.

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Home > Blog > Tips for Online Students > Dissertation vs Thesis: The Differences that Matter

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Dissertation vs Thesis: The Differences that Matter

Updated: June 19, 2024

Published: April 26, 2020

As a graduate student, you will have many different types of challenging coursework and assignments. However, the biggest project that you’ll work on when earning your master’s or doctoral degree will be your thesis or dissertation . The differences between a dissertation vs thesis are plenty. That’s because each of these pieces of writing happen at different times in one’s educational journey.

Let’s break down what a dissertation and thesis are so that you have a strong handle on what’s expected. For both a thesis and a dissertation, there is an obvious fluency and understanding of the subject one studies.

Let’s take a look at their similarities and differences.

Photo by  Glenn Carstens-Peters  on  Unsplash

What is a dissertation.

When you enter a doctoral program to earn a PhD, you will learn a lot about how to conduct your own research. At the culmination of your degree program, you’ll produce a dissertation.

A dissertation is a lengthy piece of written work that includes original research or expanded research on a new or existing topic. As the doctoral student, you get to choose what you want to explore and write about within your field of study.

What is a Thesis?

A thesis is also a scholarly piece of writing, but it is for those who are graduating from a master’s program. A thesis allows students to showcase their knowledge and expertise within the subject matter they have been studying.

Main Differences Between a Thesis vs. Dissertation

The biggest difference between a thesis and a dissertation is that a thesis is based on existing research.

On the other hand, a dissertation will more than likely require the doctoral student to conduct their own research and then perform analysis. The other big difference is that a thesis is for master’s students and the dissertation is for PhD students.

Structural Differences Between a Thesis and a Dissertation

Structurally, the two pieces of written analysis have many differences.

• A thesis is at least 100 pages in length
• A dissertation is 2-3x that in length
• A thesis expands upon and analyzes existing research
• A dissertation’s content is mostly attributed to the student as the author

Research Content and Oral Presentation

Once completed, some programs require students to orally present their thesis and dissertation to a panel of faculty members.

Typically, a dissertation oral presentation can take several hours. On the other hand, a thesis only takes about an hour to present and answer questions.

Let’s look at how the two scholarly works are similar and different:

Similarities:

• Each is considered a final project and required to graduate
• Both require immense understanding of the material
• Written skills are key to complete both
• Neither can be plagiarized
• Both are used to defend an argument
• Both require analytical skills
• You will have to draft, rewrite, and edit both pieces of writing
• For both, it is useful to have another person look over before submission
• Both papers are given deadlines

Differences:

• A dissertation is longer than a thesis
• A dissertation requires new research
• A dissertation requires a hypothesis that is then proven
• A thesis chooses a stance on an existing idea and defends it with analysis
• A dissertation has a longer oral presentation component

The Differences in Context: Location Matters

The united states.

In the US, everything that was previously listed is how schools differentiate between a thesis and a dissertation. A thesis is performed by master’s students, and a dissertation is written by PhD candidates.

In Europe, the distinction between a thesis and dissertation becomes a little more cloudy. That’s because PhD programs may require a doctoral thesis to graduate. Then, as a part of a broader post-graduate research project, students may complete a dissertation.

Photo by  Russ Ward  on  Unsplash

The purpose behind written research.

Each piece of writing is an opportunity for a student to demonstrate his or her ability to think critically, express their opinions in writing, and present their findings in front of their department.

Graduate degrees take a lot of time, energy, and hard work to complete. When it comes to writing such lengthy and informative pieces, there is a lot of time management that is involved. The purpose of both a thesis and a dissertation are written proof that you understand and have mastered the subject matter of your degree.

Degree Types

A doctoral degree, or PhD, is the highest degree that one can earn. In most cases, students follow the following path to achieve this level of education: Earn a bachelor’s degree, then a master’s, and then a PhD. While not every job title requires this deep educational knowledge, the salaries that come along with each level of higher education increase accordingly.

Earning Your Degree

Whether you are currently a prospective student considering earning your higher education degree or a student enrolled in a master’s or doctoral program, you know the benefits of education.

However, for some, earning a traditional degree on-campus doesn’t make sense. This could be because of the financial challenges, familial obligations, accessibility, or any other number of reasons.

For students who are seeking their higher education degrees but need a flexible, affordable, and quality alternative to traditional college, take a look at the programs that the University of the People has to offer.

University of the People is an entirely online, US accredited and tuition-free institution dedicated to higher education. You can earn your Master’s in Business Administration or your Master’s in Education . Not to mention, there are a handful of associate’s and bachelor’s degree programs to choose from as well.

If you want to learn more, get in touch with us !

The Bottom Line

Regardless of where and when you earn your master’s or doctoral degree, you will likely have to complete a thesis or dissertation. The main difference between a thesis and dissertation is the level at which you complete them. A thesis is for a master’s degree, and a dissertation is for a doctoral degree.

Don’t be overwhelmed by the prospect of having to research and write so much. Your educational journey has prepared you with the right time management skills and writing skills to make this feat achievable!

In this article

At UoPeople, our blog writers are thinkers, researchers, and experts dedicated to curating articles relevant to our mission: making higher education accessible to everyone. Read More

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Both the hypothesis statement and the thesis statement answer a research question. 

• A hypothesis is a statement that can be proved or disproved. It is typically used in quantitative research and predicts the relationship between variables.  
• A thesis statement is a short, direct sentence that summarizes the main point or claim of an essay or research paper. It is seen in quantitative, qualitative, and mixed methods research. A thesis statement is developed, supported, and explained in the body of the essay or research report by means of examples and evidence.

Every research study should contain a concise and well-written thesis statement. If the intent of the study is to prove/disprove something, that research report will also contain a hypothesis statement.

NOTE: In some disciplines, the hypothesis is referred to as a thesis statement! This is not accurate but within those disciplines it is understood that "a short, direct sentence that summarizes the main point" will be included.

For more information, see The Research Question and Hypothesis (PDF file from the English Language Support, Department of Student Services, Ryerson University).

How do I write a good thesis statement?

How do I write a good hypothesis statement?

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Dissertation vs Thesis: Your 2024 Guide

If you’ve been thinking about going to graduate school, you may be familiar with the application requirements, rigorous academic schedule, and thesis or dissertation you’ll be expected to complete. So, what exactly is the difference between a thesis and a dissertation? While there are similarities, there’s a clear difference between the two. In our guide, we compare dissertation vs thesis. Discover more about both – and what you can expect during your graduate program. Let’s get started!

• Table of Contents

What Is a Thesis?

A thesis is an academic paper or project that’s completed towards the end of a master’s degree program . It is typically completed as the capstone project , meaning it’s the final project required for a student to graduate.

Students need to select a narrow, specific topic within – or relating to – their field of study. Once they’ve selected a topic, students must conduct an in-depth review of existing research on their chosen subjects. The next step is to formulate an academic argument, an assertion they’ll need to support or prove with said research.

Therefore, a thesis is akin to an in-depth research paper. It’s comprised of research that essentially proves what a student has learned during their program.

What Is a Typical Thesis Structure?

A thesis generally follows a rigid structure that’s decided by the program, department, or university. Here is an example of a thesis structure:

• The Title Page
• Summary of Thesis Abstract
• Table of Maps and Figures
• The Thesis Body (Sometimes divided into chapters)
• The Results or Conclusion

Who Needs to Complete a Thesis?

Most master’s degree programs require students to complete a thesis. While some undergraduate programs may also require a thesis, these are generally shorter and narrower in scope.

Some programs will also require a master’s student to defend their thesis in front of a panel or committee.

What Is a Dissertation?

What is “the PhD paper” called? Some people refer to it as a PhD thesis, but it’s most commonly known as a dissertation in the US. Dissertations are the capstone project required at the tail end of a PhD program . It is almost always required, except for a select few one-year PhD programs .

Much like a thesis, dissertations are also academic papers that aim to prove a student’s expertise – while adding to the current body of knowledge – in their field. Thus, a student must look at existing research and conduct their own research .

Basically, it’s the magnum opus of a doctoral journey in the United States. A dissertation isn’t just a long research paper; it’s a beast of a project. It demands extensive research, originality, and the ability to make a meaningful contribution to your chosen field. Think of it as a research odyssey guided by a seasoned mentor. Once you’ve conquered this scholarly quest and defended your findings, you’ll proudly emerge with your hard-earned doctoral degree, a testament to your dedication and scholarly prowess.

A dissertation typically comes after a PhD student completes their required courses and passes their qualifying exams. In some programs, the dissertation process is embedded into the coursework. In such cases, students receive a jump start on their work, allowing them to potentially finish their program earlier.

What Does a Dissertation Do?

PhD candidates must present a new theory or hypothesis. Alternatively, they must present their research to question (or disprove) the existing accepted theory on their chosen subject. Students may choose to tackle their topic from a new angle or take their research in a different direction.

Most programs will require students to defend their dissertations. During the defense, candidates must be able to justify the methodology of their research and the results and interpretation of their findings. Defenses are typically oral presentations in front of a dissertation committee , where the students are asked questions or presented with challenges.

Although the defense may seem daunting, PhD students work closely with their advisors to prepare for their dissertations. Students receive feedback and advice to guide their dissertations in their chosen direction.

What Is the Typical Dissertation Structure? 

Dissertations follow a rigid structure typically set by the program, department, or university. Here is an example format:

• The Acknowledgments Page
• The Abstract
• Introduction
• The Literature Review & Theoretical Framework
• The Methodology
• Findings/Results
• Discussions of the Findings, including analysis, interpretation, and applications
• The Conclusion
• List of References
• Any Appendices

What Is a Doctoral Thesis?

A doctoral thesis is a substantial piece of scholarly work that marks the pinnacle of a doctoral degree program, such as a PhD. Think of it as the academic grand finale. Its primary mission? To showcase the candidate’s mastery in their chosen field and their knack for delving deep into research.

In a nutshell, a doctoral thesis is a mammoth project that calls for originality. You’ve got to dig, investigate, gather data, crunch numbers, and present real data-supported findings. All this hard work usually happens under the watchful eye of a knowledgeable mentor. Once you’ve conquered this scholarly mountain and defended your thesis successfully, you’ll be proudly awarded your well-deserved doctoral degree. It’s the hallmark of your expertise and contribution to your field.

And how does a doctoral thesis differ from a dissertation? That’s mainly a geographic explanation. While they’re largely similar in scope and purpose, when comparing a doctoral thesis vs. a dissertation:

• A dissertation is the PhD capstone requirement in the US .
• A doctoral thesis is the PhD capstone requirement in Europe .

Related Reading: The Easiest PhDs

Dissertation vs. Thesis: The Similarities

In the master’s thesis vs dissertation discussion, there are plenty of similarities. Both are lengthy academic papers that require intense research and original writing. They’re also capstone projects which are completed at the tail end of their respective programs.

Students must work closely with their respective committees (e.g., faculty members, advisors, professionals) who provide feedback and guidance on their research, writing, and academic arguments. Both thesis and dissertation committees have a committee chair with whom the students work closely.

In some ways, the requirements for theses and dissertations are quite similar. They require a skillful defense of a student’s academic arguments. What’s more, both papers require critical thinking and good analytical reasoning, as well as in-depth expertise in the chosen field of study.

Students must also invest a significant amount of time into both projects while also being able to accept and action feedback on their work.

Dissertation vs. Thesis: The Differences

What are the differences between a PhD dissertation vs. thesis? The first and most distinct difference is the degree program requiring a PhD dissertation or thesis. A dissertation is typically the capstone project for a doctorate, while a thesis is the capstone project for a master’s degree program (or undergraduate program).

Candidates will have to defend their dissertation during an oral presentation in front of their committee. Only some master’s theses require this.

During a thesis, students typically conduct research by reviewing existing literature and knowledge on their chosen subject. During a dissertation, students must do their own research and prove their theory, concept, or hypothesis. They should also expect to develop a unique concept and defend it based on the practical and theoretical results achieved from their rigorous research.

Theses are also typically shorter (around 40 to 80 pages). Dissertations, however, are much longer (between 100 and 300 pages). Of course, the actual length of the paper may depend on the topic, program, department, or university.

Related Reading : PhD Candidate vs Student: What’s the Difference? 

Dissertations and Theses: US vs. Europe

Whether you’re in the US or Europe, dissertations and theses are similar. However, European requirements and conventions differ slightly:

Doctoral Thesis

To ensure your PhD graduation, a dissertation is generally required. Doctoral theses in Europe are much like a PhD dissertation in the US : You must complete your own research and add to the existing body of knowledge in your field.

Master’s Dissertation

It may seem odd to require a dissertation for master’s degree programs, but in Europe, this is exactly what you’ll need. A master’s dissertation is a broader post-graduate program research project , though it’s most typically required for master’s programs.

Frequently Asked Questions

Here are a few of the most common questions we hear about the meaning of thesis vs. dissertation.

Is a Thesis and a Dissertation the Same?

Yes and no. In some ways, a dissertation and a thesis are the same. For example, both require original writing, critical skills, analytical thinking, plenty of research, and lots of academic effort. However, a thesis is more commonly reserved for master’s – and some undergraduate – programs. Dissertations are generally required by PhD programs in the United States.

Additionally, a thesis typically calls for heavy research and compilation of existing knowledge and literature on a subject. A dissertation requires candidates to conduct their own research to prove their own theory, concept, or hypothesis – adding to the existing body of knowledge in their chosen field of study.

How Long Is a Thesis vs. a Dissertation?

One of the primary differences between thesis and dissertation papers is their length. While a thesis might be anywhere from 40 to 80 pages long, a dissertation can easily run from 100 to 300. It’s important to note that these numbers depend on the specific program and university.

Does a PhD Require a Thesis or a Dissertation?

It all depends on where you are! While a US-based PhD requires you to complete a dissertation, a thesis (or “doctoral thesis”) is more commonly required for PhD candidates in Europe. In the US, a thesis is more commonly reserved for master’s degree programs and occasionally undergraduate programs. In Europe, a “master’s dissertation” is typically required for the completion of a master’s degree.

So, there you have it: an in-depth comparison of the dissertation vs. thesis academic requirements. Now that you know the primary similarities and differences between the two, it might become easier to decide your academic path. Just remember, you may be able to find a master’s program without a thesis or a doctorate without a dissertation requirement if you prefer. Good luck!

Are you ready to jump into your doctorate? Find out if you need a master’s degree to get a PhD .

Chriselle Sy

Chriselle has been a passionate professional content writer for over 10 years. She writes educational content for The Grad Cafe, Productivity Spot, The College Monk, and other digital publications.  When she isn't busy writing, she spends her time streaming video games and learning new skills.

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Dissertation vs Thesis: Difference and Comparison

September 8, 2023 by Chukwuemeka Gabriel Leave a Comment

Earning a degree at a college or university involves different types of writing assignments and for those who seek to earn a master’s or doctoral degree, the biggest project they have to work on is a thesis or dissertation.

The academic world involves a lot of writing, most importantly for graduate students pursuing a master’s or doctoral degree. For graduate students to complete their degree, they need to present a major research and writing project.

It’s a dissertation for those pursuing a doctoral degree and a thesis for a master’s degree program. It’s sometimes confusing to understand the difference between these two projects.

What are the differences between dissertation vs thesis?

While these two projects share a few similarities, there are key differences between dissertation and thesis. We will be discussing the major differences and similarities between these two projects and more in this article.

What Is a Thesis?

According to the University of the People , a thesis is a scholarly piece of writing, an academic paper completed by a graduate student near the end of their course of study for a master’s degree program.

Generally, most master’s degrees require students to complete a thesis before graduation. At the undergraduate level, there are some bachelor’s degree programs that require students to commit and write an undergraduate thesis.

An undergraduate thesis is usually shorter and less in-depth compared to a master’s degree thesis.

A thesis provides an opportunity for students to show their knowledge within the subject matter they have learned.

Graduate students choose a specific topic of interest in their field in other to write a master’s thesis.

According to Grand Canyon University , a student pursuing a degree in nutrition science may have to examine the effects of varying compositions of pregame meals on athletic performance.

The student will need to conduct an in-depth review of existing research on that specific topic. The next step is to formulate an academic argument i.e. high crab pregame meals are being of advantage for endurance athletes.

With the existing research, the student can easily prove their assertion.

Also Read: AKA and Delta: Difference and Comparison

What Is a Dissertation?

Unlike a thesis, a dissertation is a piece of written work that incorporates original research. Doctoral student working on a dissertation usually conduct their own expanded research on a new or existing topic.

The aim of writing a dissertation is not just to prove the student’s knowledge, but to contribute to the existing knowledge in their field.

Doctoral student working on a dissertation may present a new theory or hypothesis in their own field. The students may also present research to disapprove a previously presented theory.

Doctoral students are given the opportunity to choose what they want to write about in their own field. The requirement for a researcher to earn a doctoral degree includes submitting and defending a dissertation.

Dissertation vs Thesis: Difference between Dissertation and Thesis

A dissertation is a lengthy piece of scholarly writing required from doctoral students pursuing a PhD, while a thesis is an academic paper completed by a master’s student near the end of their course of study.

The key difference between a dissertation and a thesis is the academic degree programs that need these two projects.

A thesis is a scholarly writing required from students to complete their master’s degree program. In contrast, doctoral students are required to submit and defend their dissertations to earn their Ph.D. degree.

Another difference between a dissertation and a thesis is the need for an oral defence. Doctoral students are required to submit and defend their dissertation in order to earn a PhD, whereas not all master’s degree programs require students to orally present their thesis.

Doctoral student are required to submit their completed dissertation to their dissertation committee. After that, they will schedule dates for the oral presentation of their dissertation.

The students will be questioned by the dissertation committee about their work. They must be prepared to justify the interpretation and methodology of their work.

A dissertation is a lengthy scholarly written work compared to a master’s thesis. Generally, the average dissertation is between 100 to 300 pages of written work compared to a master’s thesis which is somewhere around 100 pages depending on the degree.

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Dissertation vs Thesis: Comparison Chart

Dissertation vs Thesis: Similarities between Dissertation and Thesis

Dissertation and thesis are two distinct projects for master’s and PhD students. Although these two projects differ, dissertation and thesis share some similarities.

Dissertation and thesis are scholarly writings that require intensive research as well as the completion of a paper made up of original writing.

These two projects at the graduate level require the following.

• Critical thinking and analytical reasoning.
• The willingness to rewrite and edit the written work after a review from professors, committee members, or fellow students.
• In-depth knowledge of the subject area.
• Investing more time, dedicating years for dissertations as well as months for theses.
• Both dissertation and thesis require analytical skills for students to support their findings.
• Plagiarism is not tolerated in these two projects.
• A dissertation and a thesis are considered final projects and all graduate students must complete them in their respective degree programs.
• In these two final project committees, the committee chair is the main point of contact for all students.
• Both master’s students and Ph.D. students work with a committee that consists of faculty members, advisors, and similar professionals.

Also Read: Family Medicine vs Internal Medicine: Difference and Comparison

Dissertation vs Thesis: More Differences

Frequently Asked Questions

Here are a few frequently asked questions about the difference between a dissertation vs thesis.

What is a dissertation?

A dissertation is a piece of written work that incorporates original research. Doctoral student working on a dissertation usually conduct their own expanded research on a new or existing topic.

What is a thesis?

A thesis is a scholarly piece of writing, an academic paper completed by a graduate student near the end of their course of study for a master’s degree program.

Also Read: Seminar vs Workshop: Difference and Comparison

What is the difference between dissertation vs thesis?

While these two projects share a few similarities, there are key differences between a dissertation and a thesis.

A dissertation is a lengthy piece of scholarly writing required from doctoral students pursuing a PhD, while a thesis is an academic paper completed by master’s students.

How long is a thesis?

Depending on the master’s degree program and other factors such as research top, institution, and country, a thesis should be at least 100 pages.

How long is a dissertation?

A dissertation is a lengthy scholarly written work that is usually two to three times lengthier than a master’s thesis. On average, a dissertation should have 100 to 300 pages.

A dissertation is a lengthy piece of scholarly writing required from doctoral students pursuing a PhD, while a thesis is an academic paper completed by a master’s student near the end of their course of study for a master’s degree program.

Although these two projects differ, dissertation and thesis share some similarities. Both dissertation and thesis require analytical skills for students to support their findings.

Master’s students and PhD students work with a committee that consists of faculty members, advisors, and similar professionals.

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Thesis vs. Dissertation: Understanding the Differences

A thesis and a dissertation are often used interchangeably, causing confusion among students and academics alike. While they share some similarities, they are distinct in purpose, scope, and requirements. Understanding these differences is crucial for graduate students embarking on their academic journey. In this article, we delve into the world of thesis and dissertation, shedding light on what sets them apart and helping you navigate these essential components of advanced academic research.

Thesis: The Master's Magnum Opus

In the realm of academia, a thesis is often referred to as the master's magnum opus, representing the pinnacle of a student's academic journey at the master's level. It is a significant undertaking that requires dedication, research acumen, and a commitment to scholarly excellence. Here, we explore the key characteristics and elements that define a thesis:

Academic Level: A thesis is a hallmark of master's degree programs. It serves as the culmination of a student's graduate studies, demonstrating their ability to engage critically with their field of study and contribute meaningfully to it. Unlike undergraduate projects, a thesis delves deeper into the subject matter and requires a higher level of analysis and synthesis.

Purpose: The primary purpose of a thesis is to contribute to the existing body of knowledge within a specific field or discipline. It goes beyond mere regurgitation of facts and concepts; it involves original research or a novel approach to addressing a research question or hypothesis. In essence, a thesis is a scholarly endeavor that aims to add value to the academic conversation.

Scope: Theses typically have a narrower scope compared to their doctoral counterparts, dissertations. While they explore complex topics, they often focus on a specific aspect or dimension within a broader field. This focused approach allows students to delve deeply into their chosen subject matter and demonstrate expertise in that area.

Length: Theses vary in length depending on the institution, program, and field of study. However, they are generally shorter than dissertations. A typical master's thesis can range from 50 to 100 pages. This concise format necessitates precision and clarity in presenting research findings and arguments.

Committee Evaluation: A key aspect of the thesis process is the evaluation by a committee of professors or advisors. This committee plays a pivotal role in guiding the student's research, offering feedback, and assessing the final product. Their expertise ensures that the thesis meets academic standards and contributes meaningfully to the field.

Research and Methodology: Research is at the heart of any thesis. Students engage in systematic inquiry, which may involve data collection, experimentation, surveys, literature analysis, or a combination of research methods. The methodology chosen should align with the research question and contribute to the overall quality of the thesis.

Scholarly Writing: A thesis demands scholarly writing of the highest caliber. Students are expected to adhere to academic writing conventions, including proper citation, referencing, and adherence to a specific style guide (e.g., APA, MLA). Clarity, coherence, and a formal tone are essential elements of thesis writing.

Originality: Perhaps the most crucial aspect of a thesis is its contribution to the field's body of knowledge. It must offer fresh insights, perspectives, or solutions to existing challenges. Originality is the hallmark of a well-executed thesis, distinguishing it from undergraduate assignments.

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Dissertation: the doctoral opus.

In the realm of academia, a dissertation is the pinnacle of scholarly achievement within the context of doctoral programs. It represents the culmination of extensive academic preparation and research and is often referred to as the doctoral opus. Here, we explore the defining characteristics and elements that set a dissertation apart in the academic world:

Academic Level: A dissertation is synonymous with doctoral programs, such as a Doctor of Philosophy (Ph.D.) or other terminal degrees. It marks the highest level of academic achievement and signifies a student's readiness to make a significant contribution to their field.

Purpose: The primary purpose of a dissertation is to make a substantial and original contribution to the existing body of knowledge within a specific field or discipline. Unlike a thesis, which often focuses on a more specific aspect, a dissertation tackles complex research questions that have broader implications for the field.

Scope: Dissertations are renowned for their expansive scope. They typically explore multifaceted research questions that encompass multiple variables, perspectives, and dimensions. Doctoral candidates are expected to demonstrate a comprehensive understanding of their field and engage in in-depth analysis.

Length: Dissertations are considerably longer than theses. The length can vary depending on the field of study and the nature of the research, but they often exceed 100 pages and can extend to several hundred pages. This extended format allows for a thorough exploration of the research topic.

Committee Evaluation: A dissertation is rigorously evaluated by a committee of experts in the field. This committee plays a crucial role in guiding the research, offering feedback, and ultimately assessing the quality and significance of the dissertation. Their expertise ensures that the dissertation meets the highest academic standards.

Research and Methodology: Research in a dissertation is not only extensive but also often involves original data collection, experimentation, surveys, case studies, or longitudinal studies. The choice of research methodology is critical and should align with the research questions and objectives.

Scholarly Writing: As with theses, scholarly writing is paramount in dissertations. Doctoral candidates are expected to adhere to the most stringent academic writing conventions. This includes precise citation and referencing, adherence to a specific style guide, and the use of formal and rigorous language.

Originality: The hallmark of a successful dissertation is its originality. It must offer new insights, perspectives, or solutions to complex problems within the field. Doctoral candidates are expected to demonstrate their ability to contribute significantly to the advancement of knowledge.

Defending the Dissertation: A distinctive feature of the dissertation process is the formal defense. Candidates present their research findings and defend their methodology, conclusions, and contributions before their committee and often a public audience. This oral defense is a culmination of the rigorous research journey.

Navigating the Process

Embarking on the journey of crafting a thesis or dissertation is a significant undertaking that requires careful planning, dedication, and resilience. Navigating this process effectively is key to producing a scholarly work that reflects your expertise and contributes meaningfully to your field. Here, we provide an overview of the essential steps and strategies to guide you through this academic odyssey.

1. Define Your Research Question: The first and foremost step in both thesis and dissertation projects is defining a clear and research-worthy question or problem. Your question should be specific, relevant, and capable of generating new insights or solutions. Take the time to conduct a thorough literature review to understand the existing body of knowledge in your chosen area.

2. Assemble Your Committee: Your academic committee will play a vital role in guiding your research and evaluating your final work. Choose committee members who are experts in your field and who can provide valuable insights and feedback throughout the process. Effective communication with your committee is essential.

3. Develop a Research Proposal: Crafting a well-structured research proposal is crucial. It should outline the scope of your project, research methodology, timeline, and expected outcomes. Your proposal will serve as a roadmap for your research journey and will require approval from your committee.

4. Research and Data Collection: The heart of your thesis or dissertation lies in the research phase. Depending on your field and research question, this may involve conducting experiments, surveys, interviews, or extensive data analysis. Ensure that your data collection methods align with your research objectives.

5. Writing and Organization: Writing your thesis or dissertation is an ongoing process that requires discipline and organization. Create a detailed outline or structure to guide your writing. Pay careful attention to proper citation and referencing to avoid plagiarism. Seek feedback from your committee at various stages of writing.

6. Revision and Proofreading: Revising and proofreading are integral steps in producing a polished document. Review your work for clarity, coherence, and logical flow. Check for grammatical errors and ensure that your writing adheres to academic conventions.

7. Formal Defense: Doctoral candidates will typically undergo a formal defense of their work. This involves presenting your research findings, methodology, and conclusions to your committee and often a public audience. Prepare thoroughly for this oral defense, as it is a culmination of your research journey.

8. Submission and Publication: Once your thesis or dissertation is complete and approved by your committee, it's time to submit it to your institution's academic office. Depending on your field and aspirations, you may also consider publishing your work in academic journals to contribute to your field's body of knowledge.

9. Celebrate Your Achievement: Completing a thesis or dissertation is a monumental achievement. Take the time to celebrate your hard work and dedication. Share your research findings with peers and colleagues, and consider how your work can continue to impact your field.

10. Continuous Learning: The process of research and scholarship is ongoing. Continue to engage with your field, attend conferences, and seek opportunities for further research and collaboration. Your thesis or dissertation is a significant step in your academic journey, but it is not the end; it is a foundation upon which you can build your future contributions to your field.

Navigating the thesis or dissertation process is a transformative experience that equips you with valuable research, writing, and critical-thinking skills. Approach it with curiosity, dedication, and a commitment to advancing knowledge in your field, and you will emerge from this academic endeavor not only with a well-crafted document but also as a more accomplished scholar.

In the realm of academia, the terms "thesis" and "dissertation" represent distinct milestones in a student's educational journey. Understanding the differences between these two research projects is essential for selecting the appropriate path in your graduate studies. Whether you're aiming for a master's or doctoral degree, both theses and dissertations are opportunities to engage in meaningful research, contribute to your field, and demonstrate your expertise as a scholar.

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This is the Difference Between a Hypothesis and a Theory

What to Know A hypothesis is an assumption made before any research has been done. It is formed so that it can be tested to see if it might be true. A theory is a principle formed to explain the things already shown in data. Because of the rigors of experiment and control, it is much more likely that a theory will be true than a hypothesis.

As anyone who has worked in a laboratory or out in the field can tell you, science is about process: that of observing, making inferences about those observations, and then performing tests to see if the truth value of those inferences holds up. The scientific method is designed to be a rigorous procedure for acquiring knowledge about the world around us.

In scientific reasoning, a hypothesis is constructed before any applicable research has been done. A theory, on the other hand, is supported by evidence: it's a principle formed as an attempt to explain things that have already been substantiated by data.

Toward that end, science employs a particular vocabulary for describing how ideas are proposed, tested, and supported or disproven. And that's where we see the difference between a hypothesis and a theory .

A hypothesis is an assumption, something proposed for the sake of argument so that it can be tested to see if it might be true.

In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.

What is a Hypothesis?

A hypothesis is usually tentative, an assumption or suggestion made strictly for the objective of being tested.

When a character which has been lost in a breed, reappears after a great number of generations, the most probable hypothesis is, not that the offspring suddenly takes after an ancestor some hundred generations distant, but that in each successive generation there has been a tendency to reproduce the character in question, which at last, under unknown favourable conditions, gains an ascendancy. Charles Darwin, On the Origin of Species , 1859 According to one widely reported hypothesis , cell-phone transmissions were disrupting the bees' navigational abilities. (Few experts took the cell-phone conjecture seriously; as one scientist said to me, "If that were the case, Dave Hackenberg's hives would have been dead a long time ago.") Elizabeth Kolbert, The New Yorker , 6 Aug. 2007

What is a Theory?

A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, its likelihood as truth is much higher than that of a hypothesis.

It is evident, on our theory , that coasts merely fringed by reefs cannot have subsided to any perceptible amount; and therefore they must, since the growth of their corals, either have remained stationary or have been upheaved. Now, it is remarkable how generally it can be shown, by the presence of upraised organic remains, that the fringed islands have been elevated: and so far, this is indirect evidence in favour of our theory . Charles Darwin, The Voyage of the Beagle , 1839 An example of a fundamental principle in physics, first proposed by Galileo in 1632 and extended by Einstein in 1905, is the following: All observers traveling at constant velocity relative to one another, should witness identical laws of nature. From this principle, Einstein derived his theory of special relativity. Alan Lightman, Harper's , December 2011

Non-Scientific Use

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch (though theory is more common in this regard):

The theory of the teacher with all these immigrant kids was that if you spoke English loudly enough they would eventually understand. E. L. Doctorow, Loon Lake , 1979 Chicago is famous for asking questions for which there can be no boilerplate answers. Example: given the probability that the federal tax code, nondairy creamer, Dennis Rodman and the art of mime all came from outer space, name something else that has extraterrestrial origins and defend your hypothesis . John McCormick, Newsweek , 5 Apr. 1999 In his mind's eye, Miller saw his case suddenly taking form: Richard Bailey had Helen Brach killed because she was threatening to sue him over the horses she had purchased. It was, he realized, only a theory , but it was one he felt certain he could, in time, prove. Full of urgency, a man with a mission now that he had a hypothesis to guide him, he issued new orders to his troops: Find out everything you can about Richard Bailey and his crowd. Howard Blum, Vanity Fair , January 1995

And sometimes one term is used as a genus, or a means for defining the other:

Laplace's popular version of his astronomy, the Système du monde , was famous for introducing what came to be known as the nebular hypothesis , the theory that the solar system was formed by the condensation, through gradual cooling, of the gaseous atmosphere (the nebulae) surrounding the sun. Louis Menand, The Metaphysical Club , 2001 Researchers use this information to support the gateway drug theory — the hypothesis that using one intoxicating substance leads to future use of another. Jordy Byrd, The Pacific Northwest Inlander , 6 May 2015 Fox, the business and economics columnist for Time magazine, tells the story of the professors who enabled those abuses under the banner of the financial theory known as the efficient market hypothesis . Paul Krugman, The New York Times Book Review , 9 Aug. 2009

Incorrect Interpretations of "Theory"

Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.

The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)

This mistake is one of projection: since we use theory in general use to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.

The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."

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“Theory” vs. “Hypothesis”: What Is The Difference?

Chances are you’ve heard of the TV show The Big Bang Theory . Lots of people love this lighthearted sitcom for its quirky characters and their relationships, but others haven’t even given the series a chance for one reason: they don’t like science and assume the show is boring.

However, it only takes a few seconds with Sheldon and Penny to disprove this assumption and realize that this theory ab0ut The Big Bang Theory is wrong—it isn’t a scientific snoozefest.

But wait: is it a theory or a  hypothesis about the show that leads people astray? And would the actual big bang theory— the one that refers to the beginning of the universe—mean the same thing as a big bang hypothesis ?

Let’s take a closer look at theory and hypothesis to nail down what they mean.

What does theory mean?

As a noun, a theory is a group of tested general propositions “commonly regarded as correct, that can be used as principles of explanation and prediction for a class of phenomena .” This is what is known as a scientific   theory , which by definition is “an understanding that is based on already tested data or results .” Einstein’s theory of relativity and the  theory of evolution are both examples of such tested propositions .

Theory is also defined as a proposed explanation you might make about your own life and observations, and it’s one “whose status is still conjectural and subject to experimentation .” For example:  I’ve got my own theories about why he’s missing his deadlines all the time.  This example refers to an idea that has not yet been proven.

There are other uses of the word theory as well.

• In this example,  theory is “a body of principles or theorems belonging to one subject.” It can be a branch of science or art that deals with its principles or methods .
• For example: when she started to follow a new parenting theory based on a trendy book, it caused a conflict with her mother, who kept offering differing opinions .

First recorded in 1590–1600, theory originates from the Late Latin theōria , which stems from the Greek theōría. Synonyms for theory include approach , assumption , doctrine , ideology , method , philosophy , speculation , thesis , and understanding .

What does hypothesis mean?

Hypothesis is a noun that means “a proposition , or set of propositions, set forth as an explanation” that describe “some specified group of phenomena.” Sounds familiar to theory , no?

But, unlike a theory , a scientific  hypothesis is made before testing is done and isn’t based on results. Instead, it is the basis for further investigation . For example: her working hypothesis is that this new drug also has an unintended effect on the heart, and she is curious what the clinical trials  will show .

Hypothesis also refers to “a proposition assumed as a premise in an argument,” or “mere assumption or guess.” For example:

• She decided to drink more water for a week to test out her hypothesis that dehydration was causing her terrible headaches.
• After a night of her spouse’s maddening snoring, she came up with the hypothesis that sleeping on his back was exacerbating the problem.

Hypothesis was first recorded around 1590–1600 and originates from the Greek word hypóthesis (“basis, supposition”). Synonyms for hypothesis include: assumption , conclusion , conjecture , guess , inference , premise , theorem , and thesis .

How to use each

Although theory in terms of science is used to express something based on extensive research and experimentation, typically in everyday life, theory is used more casually to express an educated guess.

So in casual language,  theory and hypothesis are more likely to be used interchangeably to express an idea or speculation .

In most everyday uses, theory and hypothesis convey the same meaning. For example:

• Her opinion is just a theory , of course. She’s just guessing.
• Her opinion is just a hypothesis , of course. She’s just guessing.

It’s important to remember that a scientific   theory is different. It is based on tested results that support or substantiate it, whereas a hypothesis is formed before the research.

For example:

• His  hypothesis  for the class science project is that this brand of plant food is better than the rest for helping grass grow.
• After testing his hypothesis , he developed a new theory based on the experiment results: plant food B is actually more effective than plant food A in helping grass grow.

In these examples, theory “doesn’t mean a hunch or a guess,” according to Kenneth R. Miller, a cell biologist at Brown University. “A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

So if you have a concept that is based on substantiated research, it’s a theory .

But if you’re working off of an assumption that you still need to test, it’s a hypothesis .

So remember, first comes a hypothesis , then comes theory . Now who’s ready for a  Big Bang Theory marathon?

Now that you’ve theorized and hypothesized through this whole article … keep testing your judgment (Or is it judgement?). Find out the correct spelling here!

Or find out the difference between these two common issues below!

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Thesis Vs Dissertation: What Are The Differences Between Them?

by  Antony W

June 28, 2024

Many people use these terms thesis vs dissertation interchangeably to mean the same thing.

There are even education departments and academic institutions that use these terms differently, making them all the more confusing and therefore difficult to understand.

In this guide, you’ll learn about thesis vs dissertation so you know exactly what they mean when someone in your academic institution mentions the terms.

We’ll look at: 

• What a dissertation is
• What a thesis is
• The similarities between a thesis and a dissertation and
• The differences between a thesis and a dissertation

The primary difference between a dissertation and a thesis is the level at which a learner completes them. You’ll write a thesis if you enroll in a master’s degree courses and work on a dissertation to earn a doctoral degree.

You’ll have to do a lot of research and writing in both cases. And although the task can seem overwhelming, especially given the scope of the research and the length of the assignment, starting early can go a long way to help you finish a thesis or dissertation within the respective deadline.

What is a Dissertation in Academic Writing?

A dissertation is a written assignment that features original or expanded research by a PhD student on a new or existing topic. The doctoral student chooses the topic they would like to explore within their field of study, conducts their own original research, and then presents their results (findings) in writing.

There are two reasons why writing a dissertation is worth it . First, it proves the knowledge and research skills of a doctoral student. Second, it presents an opportunity for the student to add to the existing body of knowledge in their respective field.

The time to start working on a dissertation varies from institution to institution.

In some schools, students have to complete a doctoral course, sit through a series of examinations, and then spend at least 24 weeks writing the dissertation . Other schools integrate dissertation writing into the curriculum, allowing the doctoral to start their research early.

In dissertation writing, a student has the opportunity to present a hypothesis or a new theory in their field of research, which they can use to either disapprove or support a previously presented theory in existing research.

They also have the option to add to the existing research, in which case they have to consider a unique angle that can establish their research in a unique direction.

Perhaps the most intimidating part of working on a dissertation is the defense stage because you can’t be certain of the outcome.

However, you can work closely with the assigned committee throughout the writing process to point your research and writing in the right direction.

What is a Thesis in Academic Writing?

A thesis is an academic piece of writing done by students who wish to graduate from a Master’s degree program.

The purpose of writing a thesis is to examine whether students can express their knowledge within the subject they’ve been studying in their Master’s degree program.

Some universities require Master’s students to write undergraduate theses, but these tend to be shorter with less depth compared to the Master’s degree theses.

As with dissertation, thesis writing requires that a student choose a topic of their interest in their field of study.

The minor difference is that the topic has to be narrow. For example, if you’re taking a course in computer science, you might focus on a topic such as the effect of internet of things in textile industry.

Next, look into existing research on the topic and then use the knowledge gathered to construct an academic argument. You will finalize the process by using already existing research to prove your argument. 

Thesis vs Dissertation: What are the Similarities?

In dissertation and thesis writing, PhD and Master’s students have to choose topics to explore. We recommend that you choose a topic that interests you so you can have an easy time researching, writing, presenting, and defending your findings.

Thesis and dissertation writing requires a student to demonstrate their ability to think and express their view in writing.

Both assignments requires defense, in which case the selected committee asses a student’s ability to present their academic findings in their field of study.

Both assignments have specific deadlines that students have to observe. Start working on your thesis or dissertation early so you don’t end up making unnecessary excuses when it’s almost time to submit your work.

Thesis vs Dissertation: What Are The Differences?

The following table explains the difference between a thesis and a dissertation:

Final Thoughts on Dissertation vs Thesis

As you can see, the main difference between a thesis vs dissertation is the academic level at which students have to complete them. In other words, you will work on a thesis to earn a master’s degree and write a dissertation to earn a doctorate degree at the end of your academic year. 

Writing a thesis or dissertation shouldn’t be difficult either. That’s because your academic journey equips you with the knowledge necessary to write an authentic and comprehensive assignment.

The most important rule when it comes to doing such assignments is to start early. Never wait until it’s too late to start writing your thesis or dissertation. Last minute rush may work well for an essay, but it won’t be quite as effective for thesis or dissertation writing. 

We hope this guide clears the confusion on thesis vs dissertation. 

About the author 

Antony W is a professional writer and coach at Help for Assessment. He spends countless hours every day researching and writing great content filled with expert advice on how to write engaging essays, research papers, and assignments.

Assumption vs. Hypothesis

What's the difference.

Assumption and hypothesis are both concepts used in research and reasoning, but they differ in their nature and purpose. An assumption is a belief or statement that is taken for granted or accepted as true without any evidence or proof. It is often used as a starting point or a premise in an argument or analysis. On the other hand, a hypothesis is a tentative explanation or prediction that is based on limited evidence or prior knowledge. It is formulated to be tested and verified through empirical research or experimentation. While assumptions are often subjective and can be biased, hypotheses are more objective and aim to provide a basis for scientific investigation.

Further Detail

Introduction.

Assumptions and hypotheses are fundamental concepts in the fields of logic, science, and research. While they share some similarities, they also have distinct attributes that set them apart. In this article, we will explore the characteristics of assumptions and hypotheses, their roles in different contexts, and how they contribute to the process of knowledge acquisition and problem-solving.

Assumptions

An assumption is a belief or statement that is taken for granted or accepted as true without any proof or evidence. It serves as a starting point for reasoning or argumentation. Assumptions can be based on personal experiences, cultural norms, or generalizations. They are often used to fill in gaps in knowledge or to simplify complex situations.

One key attribute of assumptions is that they are not necessarily true or proven. They are subjective and can vary from person to person. Assumptions can be implicit, meaning they are not explicitly stated, or explicit, where they are clearly expressed. They can also be conscious or unconscious, depending on whether we are aware of them or not.

Assumptions play a crucial role in everyday life, decision-making, and problem-solving. They help us make sense of the world and navigate through uncertain situations. However, it is important to recognize that assumptions can introduce biases and limit our understanding if they are not critically examined or challenged.

A hypothesis, on the other hand, is a tentative explanation or prediction that is based on limited evidence or prior knowledge. It is formulated as a testable statement that can be supported or refuted through empirical observation or experimentation. Hypotheses are commonly used in scientific research to guide investigations and generate new knowledge.

Unlike assumptions, hypotheses are grounded in evidence and are subject to verification. They are formulated based on existing theories, observations, or logical reasoning. Hypotheses are often stated in the form of "if-then" statements, where the independent variable (the "if" part) is manipulated or observed to determine its effect on the dependent variable (the "then" part).

Hypotheses are essential in the scientific method, as they provide a framework for conducting experiments and gathering data. They allow researchers to make predictions and draw conclusions based on empirical evidence. If a hypothesis is supported by the data, it can lead to the development of theories or further research. If it is refuted, it may prompt the formulation of new hypotheses or the revision of existing ones.

Comparison of Attributes

While assumptions and hypotheses share the commonality of being statements or beliefs, they differ in several key attributes:

Assumptions are often based on personal beliefs, experiences, or cultural norms. They can be influenced by subjective factors and may not have a solid foundation in evidence or logic. In contrast, hypotheses are grounded in existing knowledge, theories, or observations. They are formulated based on logical reasoning and are subject to empirical testing.

2. Verifiability

Assumptions are not easily verifiable since they are often subjective or based on incomplete information. They are accepted as true without rigorous testing or evidence. On the other hand, hypotheses are formulated to be testable and verifiable. They can be supported or refuted through empirical observation or experimentation.

Assumptions are primarily used to simplify complex situations, fill in gaps in knowledge, or provide a starting point for reasoning. They are often employed in everyday life, decision-making, and problem-solving. Hypotheses, on the other hand, serve the purpose of generating new knowledge, guiding scientific research, and making predictions about the relationship between variables.

4. Role in Knowledge Acquisition

Assumptions can limit knowledge acquisition if they are not critically examined or challenged. They can introduce biases and prevent us from exploring alternative explanations or perspectives. Hypotheses, on the other hand, contribute to knowledge acquisition by providing a structured approach to testing and refining ideas. They encourage critical thinking, data collection, and analysis.

5. Testability

Assumptions are often difficult to test since they are not formulated as specific statements or predictions. They are more subjective in nature and may not lend themselves to empirical verification. Hypotheses, on the other hand, are designed to be testable. They are formulated as specific statements that can be supported or refuted through observation or experimentation.

Assumptions and hypotheses are both important concepts in reasoning, problem-solving, and scientific research. While assumptions provide a starting point for reasoning and decision-making, hypotheses offer a structured approach to generating new knowledge and making predictions. Understanding the attributes and differences between assumptions and hypotheses is crucial for critical thinking, avoiding biases, and advancing our understanding of the world.

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• Key Differences

Know the Differences & Comparisons

Difference Between Thesis and Dissertation

Different countries define the words thesis and dissertation differently, i.e. in some countries they are used interchangeably, while in some countries thesis is related to bachelor’s or master’s degree course and dissertation is used in the context of a doctorate degree, whereas in some countries the reverse is true. In India, PhD scholars have to submit a thesis, while M.Phil students submit a dissertation.

So, the meaning of the two words varies from country to country. Broadly speaking, in a master’s degree course, all the students have to submit their thesis, which is nothing but the final project, to obtain their degree, whereas one needs to submit a dissertation, to acquire doctorate degree.

Come let’s move further to understand the difference between thesis and dissertation.

Content: Thesis Vs Dissertation

Comparison chart, similarities, research process.

Definition of Thesis

The word ‘thesis’ is originated from the Greek language which means “ something put forth “. Thesis implies a research document in written or printed form, prepared after conducting novel research on a particular topic and submitted to the university, for an academic degree.

Basically, it is meant to profess “ what the candidate believes and what they aim to prove .” Thesis prepared by the students should be good enough to indicate the actual thought behind the research, rather than just retelling the existing facts. And to do so, students need to collect a plethora of information and a lot of background reading, to have sufficient knowledge about the subject, to develop questions.

Students who pursue a master’s degree or professional degree course, are required to complete the thesis in their last semester, under the guidance of an Assistant Professor.

While preparing the thesis, first of all, the candidate needs to research the topic, for which he/she formulates a proposition, on the basis of the research work previously performed in the concerned field. The student analyzes this research work and gives his/her opinion thereon, on the information collected, and the way information is associated with the topic of study.

Attributes of an Ideal Thesis

• It should never be in the first person and does not contain vague language.
• It has to be contestable, i.e. putting forward a questionable, or arguable point with which people often disagree.
• It should be provocative.
• It declares the conclusion on the basis of evidence and facts, with certainty.
• It assumes and disproves counter-arguments.
• It should be complete, specific and focused.

Definition of Dissertation

The dissertation is a Latin term which refers to “ discussion “. In common terms, a dissertation is a structured research work, in which the Doctorate in Philosophy (PhD) scholars have to demonstrate their findings with a logical argument, as an answer to the proposition chosen by them.

The dissertation is prepared at the end of the PhD program , under a guide, who teaches, instructs and guides the candidate, regarding the selection of the topic, which is not just interesting but unique, original and contestable.

It is a kind of assessment which checks the researching skills and the knowledge of the students and their ability to defend the argument, which forms the basis for their final grade. It includes abstract, introduction, methodology, literature review, findings, discussion, conclusion and recommendation .

The candidate uses the research of other people, as a guide to arrive at and prove/disprove the own novel hypothesis, theory or concept. It takes years to complete the research work, i.e. to gather information, to compile the information in written form, to edit the material and cite the document.

The dissertation is based on original research, in the sense that the candidates have to decide the topic relating to his/her field of study, on which no primary research has been conducted, and arrive at a hypothesis, to perform original research so as to prove or disprove the hypothesis.

Key Differences Between Thesis and Dissertation

The difference between thesis and dissertation are discussed hereunder:

• Thesis refers to an extraordinary piece of writing prepared after a deep research on a topic as a part of university or degree program, wherein a particular idea or concept is put forward as a statement for further discussion. On the other hand, the dissertation implies a document that compiles research, which is a primary requirement for the doctoral program, to prove one’s findings.
• With the thesis, the student makes additions to the existing research, whereas with dissertation the student contributes to the novel discovery in the specific field of study.
• The thesis is all about claiming a hypothesis, whereas dissertation describes or explains, how the researcher proves or disproves the hypothesis.
• While the thesis is submitted at the end of the Graduate or Master’s degree program, as a final project, the submission of the dissertation is done at the end of the Doctorate program.
• The objective of the thesis is to check the candidate’s ability to critically think the topic and to intellectually discuss the information in-depth. On the contrary, the dissertation aims at showcasing the candidate’s capacity as a research scholar, i.e. the student’s ability in identifying the area of interest, exploring the topic, compiling the research work, developing questions and defending.
• When it comes to the length or size of the document, the dissertation is lengthier than a thesis, as the former is about 400 pages, while the latter extends to 100 pages.

While pursuing a higher degree course, the student needs to submit his/her research work, i.e. thesis or dissertation. Both present the candidate’s research and findings on the specific topic. Further, both are prepared under the guidance of an expert in the concerned field.

• Process of research starts with the preparation of a thesis or dissertation, which helps a student to excel in different areas, if they follow the research process, in a proper way.
• Formulating research proposal to explore a particular research question.
• Ascertaining and accessing resources, needed to perform the research work.
• Observe, analyse and review the existing research work.
• Opting a suitable research methodology.
• Preparing a report on the project, indicating the objective, method, findings, conclusion and recommendation.
• Interpreting the findings and generalizing the results.

The two types of research work, usually end up with an oral defence in front of the panel of examiners, wherein they ask the student, question relating to their study, findings and final paper. Its main aim is to check the student’s ability to defend their research work.

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• Knowledge Base
• Null and Alternative Hypotheses | Definitions & Examples

Null & Alternative Hypotheses | Definitions, Templates & Examples

Published on May 6, 2022 by Shaun Turney . Revised on June 22, 2023.

The null and alternative hypotheses are two competing claims that researchers weigh evidence for and against using a statistical test :

• Null hypothesis ( H 0 ): There’s no effect in the population .
• Alternative hypothesis ( H a or H 1 ) : There’s an effect in the population.

Table of contents

Answering your research question with hypotheses, what is a null hypothesis, what is an alternative hypothesis, similarities and differences between null and alternative hypotheses, how to write null and alternative hypotheses, other interesting articles, frequently asked questions.

The null and alternative hypotheses offer competing answers to your research question . When the research question asks “Does the independent variable affect the dependent variable?”:

• The null hypothesis ( H 0 ) answers “No, there’s no effect in the population.”
• The alternative hypothesis ( H a ) answers “Yes, there is an effect in the population.”

The null and alternative are always claims about the population. That’s because the goal of hypothesis testing is to make inferences about a population based on a sample . Often, we infer whether there’s an effect in the population by looking at differences between groups or relationships between variables in the sample. It’s critical for your research to write strong hypotheses .

You can use a statistical test to decide whether the evidence favors the null or alternative hypothesis. Each type of statistical test comes with a specific way of phrasing the null and alternative hypothesis. However, the hypotheses can also be phrased in a general way that applies to any test.

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The null hypothesis is the claim that there’s no effect in the population.

If the sample provides enough evidence against the claim that there’s no effect in the population ( p ≤ α), then we can reject the null hypothesis . Otherwise, we fail to reject the null hypothesis.

Although “fail to reject” may sound awkward, it’s the only wording that statisticians accept . Be careful not to say you “prove” or “accept” the null hypothesis.

Null hypotheses often include phrases such as “no effect,” “no difference,” or “no relationship.” When written in mathematical terms, they always include an equality (usually =, but sometimes ≥ or ≤).

You can never know with complete certainty whether there is an effect in the population. Some percentage of the time, your inference about the population will be incorrect. When you incorrectly reject the null hypothesis, it’s called a type I error . When you incorrectly fail to reject it, it’s a type II error.

Examples of null hypotheses

The table below gives examples of research questions and null hypotheses. There’s always more than one way to answer a research question, but these null hypotheses can help you get started.

*Note that some researchers prefer to always write the null hypothesis in terms of “no effect” and “=”. It would be fine to say that daily meditation has no effect on the incidence of depression and p 1 = p 2 .

The alternative hypothesis ( H a ) is the other answer to your research question . It claims that there’s an effect in the population.

Often, your alternative hypothesis is the same as your research hypothesis. In other words, it’s the claim that you expect or hope will be true.

The alternative hypothesis is the complement to the null hypothesis. Null and alternative hypotheses are exhaustive, meaning that together they cover every possible outcome. They are also mutually exclusive, meaning that only one can be true at a time.

Alternative hypotheses often include phrases such as “an effect,” “a difference,” or “a relationship.” When alternative hypotheses are written in mathematical terms, they always include an inequality (usually ≠, but sometimes < or >). As with null hypotheses, there are many acceptable ways to phrase an alternative hypothesis.

Examples of alternative hypotheses

The table below gives examples of research questions and alternative hypotheses to help you get started with formulating your own.

Null and alternative hypotheses are similar in some ways:

• They’re both answers to the research question.
• They both make claims about the population.
• They’re both evaluated by statistical tests.

However, there are important differences between the two types of hypotheses, summarized in the following table.

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To help you write your hypotheses, you can use the template sentences below. If you know which statistical test you’re going to use, you can use the test-specific template sentences. Otherwise, you can use the general template sentences.

General template sentences

The only thing you need to know to use these general template sentences are your dependent and independent variables. To write your research question, null hypothesis, and alternative hypothesis, fill in the following sentences with your variables:

Does independent variable affect dependent variable ?

• Null hypothesis ( H 0 ): Independent variable does not affect dependent variable.
• Alternative hypothesis ( H a ): Independent variable affects dependent variable.

Test-specific template sentences

Once you know the statistical test you’ll be using, you can write your hypotheses in a more precise and mathematical way specific to the test you chose. The table below provides template sentences for common statistical tests.

Note: The template sentences above assume that you’re performing one-tailed tests . One-tailed tests are appropriate for most studies.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

The null hypothesis is often abbreviated as H 0 . When the null hypothesis is written using mathematical symbols, it always includes an equality symbol (usually =, but sometimes ≥ or ≤).

The alternative hypothesis is often abbreviated as H a or H 1 . When the alternative hypothesis is written using mathematical symbols, it always includes an inequality symbol (usually ≠, but sometimes < or >).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (“ x affects y because …”).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses . In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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Dissertation vs Thesis: Differences and Similarities

Getting a graduate degree, whether it is a master or a doctoral degree is highly challenging. These degrees are all comprised of advanced courses and expect students to be constantly engaged with the material. Both a master’s and a doctoral program mean that the student is on his or her way to become an expert in the field.

In order to succeed in these advanced degrees, students need to complete all coursework by doing the readings, projects, and the necessary research. Whereas a Bachelor’s Degree only goes over subjects of interest in a more superficial manner, still giving students time to commit to a field, students doing a master’s and doctoral degrees take one field of study and explore it in depth.

Besides the necessary courses that are required for a graduate degree most programs also involve the completion of an advanced piece of work in research, either a thesis or a dissertation. Thesis or dissertations are the final pieces of work that students submit before graduation and they encompass all the skills and knowledge that has been accumulated during the years of study for the degree.

Most people use the words thesis and dissertation interchangeably, meaning that one could substitute the other and they are the same. There are so many academic terms now that it is easy to confuse them. However, there are similarities and differences in both the thesis and the dissertation , and this article will explore them.

• What are a thesis and a dissertation?
• Similarities of thesis and dissertation
• Differences between thesis and dissertation

What Are A Thesis And A Dissertation?

The thesis and dissertation are both academic papers which focus on the field of study of the student. They are both research focused and explore a topic in depth, bringing together all concepts, theories, and practical applications which have been acquired during the years that a student has been in graduate school, whether it is a master’s or a doctoral (PhD) program.

Similarities Of Dissertation and Thesis

The terms of thesis and dissertation are used as if they were the same, and there are quite a few similarities between them. These include the fact that both projects are for graduate students and not for undergraduate students. In addition, they are both lengthy papers , often surpassing hundreds of pages depending on the field of study and whether they are more theoretical or practical.

Completing thesis or dissertation

Also, both projects require some sort of guidance to complete. That is why mentors or advisers are assigned to students to help them along the way and give them advice on how to proceed. The advisers or mentors comment on the student’s research plans, writing, collection of data and so on. At the end, they are mentioned in the final project as main contributors besides the author or student.

Since the thesis and dissertation are required for students to be allowed to graduate, those who undertake to do these projects must get a passing grade . This passing grade is usually considered to be a B or above . Due to the mentorship and effort that students give to complete either a thesis or a dissertation, they usually get good grades.

In case a student does not get a passing grade , many universities have policies on letting students redo or rewrite their thesis or dissertation . However, there are limits on the number of times students are allowed to redo their research projects. These limits depend on the university which is awarding the degree.

Moreover, both the dissertation and the thesis have similar structures that students need to write.

• Introduction
• Literary Review
• Bibliography

Even though these parts have the same names, their contents will be quite different.

Thesis or dissertation defense

After students complete their dissertations or thesis projects they are required to present them . Most universities require oral presentations or as they are called thesis or dissertation defenses. This means that students will present and defend their work and arguments in front of a panel of professors, mentors, industry experts, peers, as well as family members. The audience will ask questions at the end to test whether the student has done good work and to see how valid the project is. At the end, the professors will evaluate the research project and will grade them.

Whether you choose a thesis or a dissertation depends on a few factor s, however through completing one or the other you will be doing the following:

• Developing a sound research proposal
• Exploring a specific research question of your interest in depth
• Identifying the necessary resources for your research
• Analyzing and reviewing relevant literature
• Choosing and applying the appropriate research methodology for your research question
• Analyzing, interpreting, and reporting the data and findings
• Identifying lessons learned or implications of your research for a wider scope

Differences Between Thesis And Dissertation

A thesis and a dissertation despite being used interchangeably, also have many differences in various aspects. Below we will give details on the 5 main differences of a thesis and dissertation.

Country differences in definition

Because different countries or continents in general have different educational systems, the definitions of various academic terms will also not be the same. This stands for the dissertation vs thesis concepts, more specifically the differences are in Europe and the United States .

In Europe, a thesis is a requirement to graduate from a doctoral or PhD program . It is a large body of original research that the student does over several years, which also contains references and relations to existing research that has been done by other people.

The dissertation on the other hand is a smaller research project that is done to complete a Master’s program. It does not necessarily need to have original research, but the student must take a view and back it up with arguments collected from existing research in the form of literature review.

The United States

In the United States, the definitions of a thesis and dissertation are completely opposite. The thesis in this case is the research project necessary for graduating from a master’s degree program , while the dissertation is done for doctoral degrees .

So the thesis will contain the arguments developed mostly from existing research , while the dissertation will be complete when the student picks a topic which has not been explored yet and dedicates years to researching it and prove or disprove a hypothesis .

In this article, we will explore the difference between thesis and dissertation from the U.S perspective and definitions.

Type of Degree

The dissertation vs thesis difference is in what they are used for. The dissertation will be used to get the terminal degree or the highest qualification possible in different fields such as the doctoral or PhD degree .

On the other hand, the thesis is used to mark the end of a master’s degree , so students will get their second cycle qualifications or education after their Bachelor’s Degree by presenting their thesis.

There are also cases when a thesis or dissertation is not required and this depends on the type of program or institution that the student is completing their degree in. This mostly occurs with master’s degrees rather than PhD programs. Some masters programs will give students a choice between doing their thesis or completing an additional course.

It is recommended that if students aim to continue their education and get a PhD qualification that they choose to complete the thesis instead of the course. That is because completing a thesis will add to your research experience and PhD programs will be more likely to accept you if you have already published some form of research.

It is also better to do the thesis if you plan to get a PhD since the dissertation requires more extensive work than the thesis, so you will already have a basic understanding of the effort and work it takes to complete it.

Data Collection

Another difference between thesis and dissertation is the way in which students go through the data collection process.

Since the dissertation is the original research for which students graduate from a PhD, it mostly uses primary data such as:

• Questionnaires or surveys
• Focus Groups
• Any other form of data collected by the student

The thesis which does not necessarily require primary data collection, relies more on secondary data such as:

• Survey reports
• Existing studies
• Government or official statistics, etc.

The dissertation focuses on primary data , but uses secondary sources to back arguments and provide evidence for or against a certain hypothesis. The thesis on the other hand may or may not contain primary data , and it all depends on the student’s choice.

Purpose of study

The purpose of each of the research projects is different.

A thesis is mostly the usage of secondary sources to demonstrate to your professors and peers that you have gained enough information and skills in your field of interest. The thesis starts with a proposition and then the student analyzes and makes a case for their point of view.

The purpose of the dissertation is completely different from the thesis. The dissertation is done in order to contribute to the advancement of your field through discovering new knowledge based on research . Those who do a dissertation try to discover new concepts and theories which have not been researched enough before. They have a hypothesis and use quantitative and qualitative primary data to prove or disprove it.

Length of project

Lastly, another difference between the thesis and dissertation is in their length .

Because the dissertation is original research, it can be as long as 400 pages so it will basically be a book on your topic of interest.

The thesis though relies on secondary sources and does not go as in depth into the subject as the dissertation, so its length is minimum 100 pages , but it is not necessarily a whole book about a subject.

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• Thesis VS Dissertation
• Comparison of Dissertation and Thesis

Similar Things Between the Dissertation and Thesis

Differences between a dissertation and a thesis.

If students graduate from university, they are required to obtain a master/doctoral degree (or get an ABD status in some cases). They have to write a serious paper in the particular field. Needless to say, this is a huge project that requires good ability in writing, deep research, & a lot of work with various information from the academic program.

In this article, we will compare a thesis & a dissertation . We will find a difference between these documents, plus share with readers some good tips related to writing a successful college student resume to get a great job after finishing studying.

Here are similarities between a thesis & dissertation to learn:

• Both these papers have the same purpose: commonly, in many schools, these two terms are used interchangeably.
• A dissertation & a thesis have certain terms, they should be done in time without delays. If a student is failed to defend his / her document, they could try to do it many times.
• Both dissertation and thesis require students to choose a topic for research and create a complex work to demonstrate the level of their skills & knowledge they have got for years of studying various programs on their faculty.
• Both dissertation & thesis have a similar structure & format.
• If students are going to get their master / PhD degree, they should create a master thesis proposal / dissertation thesis proposal before writing a final paper. The main goal of this work is to introduce to readers the main aim of writing the future document.
• A student has to defend both completed thesis & a dissertation to get a certain degree.
• If students are writing a dissertation/thesis, they should avoid copyright infringement : this term means they couldn't copy works from other authors because their rights are reserved. That is why it is important to be careful with every word during creating a thesis/dissertation if a high grade is your biggest dream.

Needless to say, there is a differ between a thesis & dissertation. We have gathered all differences between these papers here:

• With doctorate dissertations & theses, students can get various degrees. Students in the United States are required to make a thesis to get a master degree & to write a dissertation to get a PhD degree.
• These documents have different length. A thesis should have at least 100 pages; dissertation is a longer document than a thesis.
• If you are making a thesis, it's important to conduct the original research; in the dissertation, you should use existing research.
• You have to add a thesis analysis to the existing literature. A dissertation is a part of analysis of the existing literature.
• In the dissertation, you need to do more extensive work to develop your research in the particular area, than in a thesis.
• A thesis and a dissertation have different statements. A thesis statement just states a point to explain to readers how you're going to prove an argument in your study. A dissertation requires a hypothesis. There you need to define results you expect from your written work & describe your expectations. If students are writing a dissertation, they need to use theory to research a particular subject.
• It is hard to compare, but writing a dissertation is more difficult for school students. In fact, they have more questions to create a good work.

If you have got a good understanding of how a successful thesis / dissertation should be written, it could be useful to look at various samples to understand that you're on the right way. Most examples you may find in the Internet, are written using the same format, but you can find some differences. Needless to say, writing a thesis / dissertation is a serious work. It requires a lot of skills in different fields: great abilities in writing, solid knowledge in the topic, practice, and a lot of patience. Plus, you have to organize your time well.

Creating a good dissertation / thesis is not easy; if you feel it's too complicated to make this document, you can order a great dissertation / bright thesis from experienced writers. Writing a thesis / dissertation can be quite complicated and challenging thing. We ensure that you can find online experts that know exactly how to write a well-structured, original, and successful document.

Thesis vs Hypothesis - What's the difference?

Hypothesis is a related term of thesis ., hypothesis is a synonym of thesis ., as nouns the difference between thesis and hypothesis, derived terms, related terms, external links.

A note for better Understanding of Thesis vs Dissertation

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Introduction, materials and methods, acknowledgments, author contributions, code and data availability.

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Spatiotemporal whole-brain activity and functional connectivity of melodies recognition

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Leonardo Bonetti, Elvira Brattico, Francesco Carlomagno, Joana Cabral, Angus Stevner, Gustavo Deco, Peter C Whybrow, Marcus Pearce, Dimitrios Pantazis, Peter Vuust, Morten L Kringelbach, Spatiotemporal whole-brain activity and functional connectivity of melodies recognition, Cerebral Cortex , Volume 34, Issue 8, August 2024, bhae320, https://doi.org/10.1093/cercor/bhae320

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Music is a non-verbal human language, built on logical, hierarchical structures, that offers excellent opportunities to explore how the brain processes complex spatiotemporal auditory sequences. Using the high temporal resolution of magnetoencephalography, we investigated the unfolding brain dynamics of 70 participants during the recognition of previously memorized musical sequences compared to novel sequences matched in terms of entropy and information content. Measures of both whole-brain activity and functional connectivity revealed a widespread brain network underlying the recognition of the memorized auditory sequences, which comprised primary auditory cortex, superior temporal gyrus, insula, frontal operculum, cingulate gyrus, orbitofrontal cortex, basal ganglia, thalamus, and hippocampus. Furthermore, while the auditory cortex responded mainly to the first tones of the sequences, the activity of higher-order brain areas such as the cingulate gyrus, frontal operculum, hippocampus, and orbitofrontal cortex largely increased over time during the recognition of the memorized versus novel musical sequences. In conclusion, using a wide range of analytical techniques spanning from decoding to functional connectivity and building on previous works, our study provided new insights into the spatiotemporal whole-brain mechanisms for conscious recognition of auditory sequences.

Research in neuroscience of music has rapidly grown in the past decades ( Münte et al. 2002 ; Koelsch et al. 2004 ; Koelsch et al. 2019 ; Pando-Naude et al. 2021 ). Indeed, since music is an art that acquires meaning through the combination of its constituent elements extended over time ( Cooke 1959 ; Peretz and Zatorre 2003 ), it provides as an excellent tool for investigating the brain’s temporal dynamics ( Münte et al. 2002 ; Koelsch et al. 2004 ; Koelsch et al. 2019 ; Pando-Naude et al. 2021 ).

Several studies have focused on the processing of sounds and revealed the primary role of the auditory cortex ( Näätänen et al. 1978 ; Warrier et al. 2009 ; Brattico and Pearce 2013 ). These investigations uncovered the early, well-known components of the event-related potential/field (ERP/F) that occurs in response to sounds, such as the N100, mismatch negativity, and P3a ( Näätänen et al. 1978 ; Näätänen et al. 2007 ). Additional studies broadened these investigations by employing more complex musical stimuli and analytical techniques. For instance, it has been widely shown that music processing evokes activity in brain networks connected to emotions ( Koelsch 2014 ). A classic study by Blood and Zatorre (2001) revealed that listening to pleasurable music was associated with a burst of activity in brain areas related to pleasure and reward such as amygdala, orbitofrontal cortex, ventral medial prefrontal cortex, and striatum. In addition to neural regions connected to emotions, it has been shown that music processing recruits motor areas of the brain such as the supplementary motor cortex, basal ganglia, and cerebellum, which are responsible for tracking rhythm and musical beat, as described by Kotz et al. (2018) and Nozaradan et al. (2017) .

Music listening has been investigated not only in terms of brain activity but also considering the associated functional connectivity between brain areas and its relationship with musical expertise. For instance, Alluri et al. (2015) investigated musicians and non-musicians while they were listening to music. They found that musicians had stronger connectivity than non-musicians between supplementary motor area (SMA) and ventromedial and ventrolateral cerebral and cerebellar affective regions. Differently, non-musicians compared to musicians showed stronger connectivity between subcortical regions only. In an electroencephalography (EEG) study, Bhattacharya and Petsche (2005) reported enhanced gamma band–phase synchrony when participants with musical expertise listened to music.

Notably, music has also been employed to investigate brain mechanisms connected to memory. Beyond the strong emotional content evoked, music contains complex logical, hierarchical structures ( Cooke 1959 ), yielding to meaningful messages and information that can be encoded and recognized. Along this line, several studies employed functional magnetic resonance imaging (fMRI) and paradigms involving music memorization and evaluation of specific melodic features.

For instance, Gaab et al. (2003) measured the brain activity while participants were asked to compare different simple melodic sequences. When participants successfully carried out the task, their brain activity was mainly observed in the superior temporal, superior parietal, posterior dorsolateral frontal, and dorsolateral cerebellar regions and supramarginal and left inferior frontal gyri. In another classic study ( Zatorre et al. 1994 ), authors dissociated the perceptual analysis of melodies from the pitch comparison of particular tones. They revealed that the former process was associated with activity in the right superior temporal cortex, while the latter mainly involved the right prefrontal cortex. A more recent study by Kumar et al. (2015) highlighted the crucial role of the primary auditory cortex, inferior frontal gyrus and hippocampus underlying an auditory working memory (WM) task consisting of maintaining a series of single sounds. Remarkably, the authors showed that not only the activity but also the connectivity between these three areas were linked to the successful completion of the task.

Auditory memory has also been studied by employing magnetoencephalography (MEG), which is beneficial to detect the fast-scale brain activity associated with memory tasks. A large corpus of studies investigated fast preattentive neural responses, implying the existence of sensory auditory memory, namely, N100 and MMN ( Bonetti et al. 2021a ; Bonetti, Carlomagno, et al. 2022b ; Bonetti et al. 2018 ; Bonetti et al. 2017 ; Näätänen et al. 2007 ). Additional research has investigated auditory memory by using more complex tasks and experimental designs. In another study, Albouy et al. (2017) investigated the brain activity underlying memory retention. The authors showed that theta oscillations in the dorsal stream of the participants’ brain predicted their abilities to perform an auditory WM task that consisted of maintaining and manipulating sound information.

Recently, we expanded on this research by investigating the brain mechanisms underlying long-term encoding and recognition of musical sequences. First, we studied the activity and connectivity in the healthy brain underlying the encoding of single sounds forming a highly structured musical prelude ( Bonetti et al. 2021b ). Our results showed that the first 220 ms of sound processing were associated with a wide network of functionally connected brain areas. Notably, while the brain activity was mainly observed for the primary and secondary auditory cortex and insula, functional connectivity analysis returned a larger picture of equally central brain areas, including not only the auditory cortex and insula but also the hippocampus, basal ganglia, cingulate gyrus, and frontal operculum. These results showed the importance of conducting fast-scale analysis on both activity and functional connectivity when studying encoding of sounds.

Second, we conducted two studies specifically focused on investigating the brain mechanisms underlying recognition of previously memorized melodies taken from the whole musical piece used in our previous study on sound encoding ( Bonetti et al. 2021b ). In the first of these two studies, we focused on the brain activity filtered in two different frequency bands to reveal that the single sounds forming the melody were connected to local and rapid (2 to 8 Hz) brain processing, while the whole sequence was linked with concurrent global and slower (0.1 to 1 Hz) processing involving a widespread network of brain regions ( Bonetti et al. 2022a ). Importantly, this study compared previously memorized musical sequences versus completely novel sequences and only focused on univariate analysis based on the ERF generated by the stimuli in two specific frequency bands. Thus, no functional connectivity nor broadband multivariate pattern analysis was conducted. In the second study, we utilized the same experimental paradigm but implemented key modifications to the stimuli. Specifically, we altered the musical pace, with each sound lasting 350 ms compared to the 250 ms described in Bonetti et al. (2022a) . Additionally, novel melodies were created by keeping the first sound identical to the previously memorized ones and systematically altering the subsequent sounds (e.g. changing all the sounds from the second tone onward, or from the third, fourth, or only the fifth tone). This approach allowed us to study recognition in a different musical tempo and investigate the brain mechanisms underlying the prediction error generated by the novel stimuli, which presented systematic changes from the original, memorized melodies. In this study, we also employed a comprehensive array of analyses, including broadband multivariate pattern analysis.

Building on our previous research, the current study uses the same data reported in Bonetti et al. (2022a) and aims to both replicate some of our previous results and expand them by investigating novel specific, yet relevant details.

First, we aim to test whether the recognition of previously memorized versus novel melodies is associated to changes only in the whole-brain activity or also in the functional connectivity patterns measured during the task.

Second, we compare the brain networks revealed by the functional connectivity analysis with those reported for sound encoding in Bonetti et al. (2021b) . This comparison is particularly meaningful since, in the current study, we used a different dataset obtained from the same participants as in Bonetti et al. (2021b) .

Third, we use temporal generalization in multivariate pattern analysis to study how brain patterns can be generalized over time. In Bonetti et al. (2024) , we demonstrated that recognizing previously memorized versus systematically varied musical sequences produced stable brain patterns recurring over time for the entire duration of the musical sequence. In fact, in that study, the brain responses to each sound in the memorized and novel sequences were consistently similar, indicating that the brain monitored each sound, confirmed predictions when they matched the memory trace, and detected errors when they did not. In this study, we instead compare previously memorized melodies to completely novel ones (i.e. not varied after some sounds) to assess if the brain patterns of differential activity remain stable over the entire musical sequence as in Bonetti et al. (2024) or diverge.

Finally, based on previous evidence on the relationship between cognitive abilities, musical expertise, and music perception ( Herholz et al. 2008 ; Bonetti and Costa 2016 , 2019 ; Criscuolo et al. 2019 ; Criscuolo et al. 2022 ; Fernández-Rubio et al. 2023 ), in this study, we also assess whether WM and musical expertise modulate the brain activity underlying recognition of previously memorized music.

Overview of the experimental design and of the data analysis pipeline

In this study, we wanted to characterize the fine-grained spatiotemporal dynamics of whole-brain activity and functional connectivity during recognition of previously memorized auditory sequences. In brief, during a session of MEG, 70 participants listened to the full prelude in C minor BWV 847 composed by Bach and tried to memorize it as much as possible. As depicted in Fig. 1a and Figure SF1 , participants were then presented with short musical melodies corresponding to excerpts of Bach’s prelude and carefully matched novel musical sequences and were asked to indicate whether each musical excerpt was extracted from Bach’s prelude or was a novel melodic sequence.

Experimental design and analysis methods. a) Graphical schema of the old/new paradigm. One at a time, several five-tone musical sequences (melodies) were presented. These could belong either to the prelude that participants had previously listened to (memorized musical sequence, “old”) or could be novel musical sequences (“new”). In this figure, we depicted at first an example of a memorized musical sequence (“old”) sequence (left, 2nd square) with the relative response pad that participants used to state whether they recognized the excerpt as “old” or “new” (left, 3rd square). Then, we depicted an example of novel musical sequence (“new,” right, 2nd square). The total number of trials was 80 (40 memorized and 40 novel musical sequences), and their order was randomized. b) We collected, preprocessed, and analyzed MEG sensor data by employing multivariate pattern analysis and MCS on univariate tests. c) We beamformed MEG sensor data into source space, providing time series of activity originating from brain locations. d) We studied the source brain activity underlying the processing of each tone of the musical sequences for both experimental conditions.

The analysis pipeline used in this study is partly illustrated in Fig. 1b and described in detail in the following paragraphs, according to recommendations offered by Gross et al. (2013) and Pernet et al. (2020) . This focused on extracting results using three main measures of brain functioning: (i) MEG sensor space activity, (ii) beamformed source localized activity, (iii) static source localized connectivity.

We computed a vast array of analyses for two reasons: to strengthen the reliability of our results by obtaining converging findings from different analytical approaches (e.g. multivariate pattern analysis and Monte Carlo simulation [MCS] on univariate tests) (i); to integrate our brain activity analysis with functional connectivity investigations. The aim of the second method was to detect the relationship and communication between brain areas and not their mere activity in response to our musical stimuli (ii).

First, we used multivariate pattern analysis and MCSs on univariate tests of MEG sensor data. Second, we were interested in finding the brain sources of the observed differences and therefore we reconstructed the sources of the signal using a beamforming algorithm ( Fig. 1c ) to track the brain activity related to each tone of the musical sequences ( Fig. 1d ). Third, complementing our brain activity results, we computed evoked-responses functional connectivity between brain regions. We calculated the static functional connectivity by computing Pearson’s correlations between the envelopes of each pair of brain areas, focusing especially on whole-brain connectivity and degree centrality of brain regions.

Participants

By computing a vast array of novel analyses, this study expands on our previous works on the brain mechanisms underlying music encoding and recognition ( Bonetti et al. 2022a ; Bonetti et al. 2021b ; Bonetti et al. 2024 ; Bonetti et al. 2024 ; Fernandez-Rubio et al. 2022 ; Fernández-Rubio et al. 2022 ). To ensure full transparency, we provide the following detailed information. With regard to the current study, Bonetti et al. (2024) refer to a different dataset with a distinct set of novel melodies (and musical tempo) obtained from a completely different sample. Bonetti et al. (2021a) utilized a different dataset based on sound encoding in the same participants as the current study. Fernandez-Rubio et al. (2022) and Fernández-Rubio et al. (2022) used different datasets based on music recognition in nearly the same participants as the current study. Finally, Bonetti et al. (2022a) employed the same dataset as the current study but focused on different analyses: univariate tests on brain activity in specific frequency bands. The current study employs instead broadband multivariate pattern analysis, broadband univariate tests, and functional connectivity in selected frequencies.

The sample of this study consisted of 70 volunteers who performed an “old/new” auditory paradigm. All participants came from different Western countries and lived in Denmark at the time of the experiment. Thirty-six of them were males and 34 were females (age range: 18 to 42 years old, mean age: 25.06 ± 4.11 years). Since our experiment involved a musical piece usually played by classical pianists, we recruited 23 classical pianists (13 males and 10 females, age range: 18 to 34 years old, mean age: 24.83 ± 4.10 years old), 24 non-pianist musicians (12 males and 12 females, age range: 19 to 42 years old, mean age: 24.54 ± 4.75), and 23 non-musicians (11 males and 12 females, age range: 21 to 35 years old; mean age: 25.86 ± 3.34). The sample regarding functional connectivity analysis slightly differed (three participants had to be discarded due to technical problems during acquisition) and consisted of 67 participants (34 males and 33 females, age range: 18 to 42 years old, mean age: 25.00 ± 4.18 years). Specifically, 21 were non-pianist musicians (10 males and 11 females, age range: 19 to 42 years old, mean age: 24.29 ± 5.02 years), 23 classical pianists (13 males and 10 females, age range: 18 to 34 years old, mean age: 24.83 ± 4.10 years), and 23 non-musicians (11 males and 12 females, age range, 21 to 35 years old; mean age: 25.86 ± 3.34 years).

In Table 1 , we reported additional information about the musical training received by the non-pianist musicians and by the pianists involved in our study.

Information about musical training received by non-pianist musicians and pianists recruited in the study. We reported the years of formal musical training and the years of daily practice with the musical instrument in four ranges of years. In the last column, “AC” refers to the age of commencement of the formal musical training, which is reported in terms of average ± standard deviation.

Participants had homogeneous socio-economic and educational backgrounds and signed the informed consent before the beginning of the experiment.

All the experimental procedures complied with the Declaration of Helsinki—Ethical Principles for Medical Research and were approved by the Ethics Committee of the Central Denmark Region (De Videnskabsetiske Komitéer for Region Midtjylland) (Ref 1-10-72-411-17).

Experimental design and stimuli

As mentioned in the paragraph on the overview of the analysis pipeline, to study the brain dynamics of musical sequence recognition, we employed an old/new ( Kayser et al. 2003 ) auditory sequence recognition task during MEG recording ( Fig. 1a ). First, participants were requested to listen to four repetitions of a MIDI version of the right-hand part of the entire prelude in C minor BWV 847 composed by J.S. Bach. The tones had the same duration, which was of approximately 250 ms. The full piece lasted about 2.5 min; thus, the total duration of the learning part was approximately 10 min (2.5 min repeated four times). Participants were asked to focus on the musical prelude and memorize it as much as possible. Second, they were presented with 80 short musical excerpts lasting 1,250 ms each and requested to indicate whether each excerpt belonged to the prelude by Bach (memorized musical sequence, “old,” 40 trials) or was a novel musical sequence (“new,” 40 trials). Subsequent analyses were performed on correctly recognized trials only. Importantly, the two categories of stimuli (memorized and novel musical sequences) were composed to be clearly distinguishable in the recognition task, even if they were matched among several variables, to prevent for potential confounds. Specifically, the two categories were matched for rhythm, volume, timbre, tempo, meter, tonality, information content ( ⁠|$IC$|⁠ ), and entropy ( ⁠|$H$|⁠ ). The memorized melodies consisted of excerpts of Bach’s prelude. We extracted one excerpt per musical bar, corresponding to the first five notes of the bar. These different excerpts were selected because they were representative of the melodic contour and of the general repetitive structure of Bach’s prelude. The novel musical sequences were created by assembling a series of tones with a melodic contour that was completely different from the one of Bach’s prelude excerpts. Importantly, such difference was present for all musical tones. By doing so, we designed a task that was challenging yet feasible, since the two categories of melodies presented several similarities, but were clearly different from one another. The 80 musical sequences are reported in musical notation in Figure SF1 .

The |$IC$| and |$H$| were estimated for each tone of the prelude’s excerpts (mean |$IC$|⁠ : 5.70 ± 1.73, mean |$H$|⁠ : 4.70 ± 0.33) and of the novel melodies (mean |$IC$|⁠ : 5.92 ± 1.81, mean |$H$|⁠ : 4.78 ± 0.35) by using Information Dynamics of Music (IDyOM) ( Pearce 2018 ). This robust method uses machine learning to return a value of |$IC$| for the target note based on a combination of the preceding notes of the musical piece comprising the target note and of a set of rules learned from a large set of prototypical pieces of Western music. Thus, in our study, the |$IC$| of each note of the musical sequences was computed using a model trained on both Bach’s prelude excerpts and the novel melodies (i) and on the large corpus of prototypical pieces of Western music usually employed by IDyOM ( Pearce 2018 ) (ii). In this way, musical sequences of the two categories (memorized and novel sequences) with the same |$IC$| were composed of a series of intervals and melodic contours that were quite similar and equally plausible in light of prototypical Western music.

Formally, the |$IC$| represents the minimum number of bits required to encode |${e}_i$| and is described by equation ( 1 ):

where |$ p\left({e}_i|{e}_{\left(i-n\right)+1}^{i-1}\right) $| is the probability of the event |${e}_i $| given a previous set of |${e}_{\left(i-n\right)+1}^{i-1} $| events.

The entropy gives a measure of the certainty/uncertainty of the upcoming event given the previous set of |${e}_{\left(i-n\right)+1}^{i-1}$| events and is calculated by equation ( 2 ):

Equation ( 2 ) shows that if the probability of a given event |${e}_i$| is 1, the probability of the other events in |$A$| will be 0 and therefore, |$H$| will be equal to 0 (maximum certainty). On the contrary, if all the events are equally likely, |$H$| will be maximum (maximum uncertainty). Therefore, IDyOM returns an estimation of the predictability of each tone and uncertainty with which it can be predicted, coherently with the human perception ( Sears et al. 2018 ).

The entire prelude and the musical excerpts were created by using Finale (MakeMusic, Boulder, CO) and then presented by adopting Presentation software (Neurobehavioural Systems, Berkeley, CA).

We collected structural images for each participant by employing magnetic resonance imaging (MRI), either on the same day as the functional MEG scan or on another day within one month.

Data acquisition

We acquired both MRI and MEG data in two independent sessions. The MEG data were acquired by employing an Elekta Neuromag TRIUX system (Elekta Neuromag, Helsinki, Finland) equipped with 306 channels. The machine was positioned in a magnetically shielded room at Aarhus University Hospital, Denmark. Data were recorded at a sampling rate of 1000 Hz with an analogue filtering of 0.1 to 330 Hz. Prior to the measurements, we accommodated the sound volume at 50 dB above the minimum hearing threshold of each participant. Moreover, by utilizing a 3D digitizer (Polhemus Fastrak, Colchester, VT, USA), we registered the participants’ head shape and the position of four headcoils, with respect to three anatomical landmarks (nasion and left and right preauricular locations). This information was subsequently used to coregister the MEG data with the anatomical structure collected by the MRI scanner. The location of the headcoils was registered during the entire recording by using a continuous head position identification (cHPI), allowing us to track the exact head location within the MEG scanner at each time point. We utilized this data to perform an accurate movement correction at a later stage of data analysis. Finally, eyeblink and heart-beat activities were collected by applying two pairs of electrodes after cleaning the skin of the participants. To detect the eyeblink, one electrode was applied above and one below the right eye. To record the heart-beat activity one electrode was placed on the left last rib and the other one on the right collar bone of the participants.

The recorded MRI data corresponded to structural T1. The acquisition parameters for the scan were: voxel size = 1.0 × 1.0 × 1.0 mm (or 1.0 mm 3 ); reconstructed matrix size 256 × 256; echo time (TE) of 2.96 ms and repetition time (TR) of 5,000 ms and a bandwidth of 240 Hz/Px. Each individual T1-weighted MRI scan was subsequently coregistered to the standard Montreal Neurological Institute (MNI) brain template through an affine transformation and then referenced to the MEG sensors space by using the Polhemus head shape data and the three fiducial points measured during the MEG session.

Data preprocessing

The raw MEG sensor data (204 planar gradiometers and 102 magnetometers) was pre-processed by MaxFilter ( Taulu and Simola 2006 ) for attenuating the interference originated outside the scalp by applying signal space separation (MaxFilter parameters: spatiotemporal signal space separation (SSS), movement compensation using cHPI coils (default step size: 10 ms); correlation limit between inner and outer subspaces used to reject overlapping intersecting inner/outer signals during spatiotemporal SSS: 0.98).

The data were converted into the Statistical Parametric Mapping (SPM) format and further analyzed in Matlab (MathWorks, Natick, Massachusetts, United States of America) using Oxford Centre for Human Brain Activity Software Library (OSL) ( Woolrich et al. 2011 ), a freely available toolbox that combines in-house-built functions ( https://github.com/leonardob92/LBPD-1.0.git ) with existing tools from the FMRIB Software Library (FSL) ( Woolrich et al. 2009 ), SPM ( Penny et al. 2011 ), and Fieldtrip ( Oostenveld et al. 2011 ). We applied a 48 to 52 Hz notch filter to correct for possible interference of the electric current and downsampled the data to 150 Hz. A few segments of the data (less than 0.5% of the whole dataset), contaminated by large artifacts, were removed after visual inspection. Then, to discard the interference of eyeblinks and heart-beat artifacts from the brain data, we performed independent component analysis (ICA) to decompose the original signal in independent components. We subsequently isolated and discarded the components that correlated with the time series recorded by the electrodes used for monitoring the eyeblink and heart-beat activities and rebuilt the signal from the remaining components ( Mantini et al. 2011 ). In most cases, we rejected only two components, one for the eyeblink and one for the heart-beat activity. In a few cases, we rejected up to six components. This happened when more than one component picked up the eyeblink or the heart-beat activity. Finally, the data were low pass–filtered (40 Hz threshold) to improve the performance of the subsequently used decoding algorithm and univariate analyses and epoched in 80 trials (one for each musical excerpt) lasting 3,500 ms each (100 ms of prestimulus time). To be noted, when computing the static functional connectivity analysis we used data that were not low pass–filtered and therefore could be investigated in frequencies higher than 40 Hz. Then, correctly identified trials were analyzed by employing two different methodologies (multivariate pattern analysis and cluster based MCS of independent univariate analyses) to strengthen the reliability of the results as well as broaden the amount of information derived by the data.

In addition, it is worth noting that we replicated the key analyses reported in the main text of this manuscript using only minimal preprocessing steps. Specifically, the analyses included decoding, MCS on MEG sensor data, and MCS on source reconstructed data. The preprocessing steps consisted of applying MaxFilter and ICA to remove eye-blink and heartbeat artifacts. The results of these analyses, which are reported in the Supplementary Materials , show that not performing low-pass and notch filters and using a different sampling rate (250 Hz) did not affect the significance of our original findings (see Figures SF2 and SF3 for a comparison between the results obtained following the two preprocessing pipelines).

Multivariate pattern analysis

We conducted a multivariate pattern analysis to decode different neural activity associated with the recognition of previously memorized versus novel musical sequences. Specifically, we employed support vector machines (SVMs) ( Cichy et al. 2014 ), analyzing each participant independently. MEG data were arranged in a 3D matrix (channels × time points × trials) and submitted to the supervised learning algorithm. To avoid overfitting, we employed a leave-one-out cross-validation approach to train the SVM classifier to decode the two conditions. This procedure consisted of dividing the trials into N different groups (here n  = 8) and, for each time point, assigning N − 1 groups to the training set and the remaining Nth group to the testing set. Then, the performance of the classifier to separate the two conditions was evaluated. This process was carried out 100 times with random reassignment of the data to training and testing sets. Finally, the decoding accuracy time series were averaged together to obtain a final time series reflecting the performance of the classifier for each participant.

To identify the channels that were carrying the highest amount of information required for decoding the two experimental conditions, we followed the procedure described by Haufe and colleagues ( Haufe et al. 2014 ) and computed the decoding sequences from the weights returned by the SVM. Moreover, we computed the confusion matrix for each time point, obtaining the four time series reported in Figure SF4 .

Finally, to assess whether the two experimental conditions were differentiated by neural sequences stable over time, we performed a temporal generalization multivariate analysis. The algorithm was the same as the one described above, with the difference that in this case we used each time point of the training set to predict not only the same time point in the testing set but also all time points ( Cichy et al. 2014 ; King and Dehaene 2014 ).

In both cases, to test whether the decoding results were significantly different from the chance level (50%), we used a sign permutation test against the chance level for each time point and then corrected for multiple comparisons by applying FDR correction (α = 0.05; FDR-adjusted q  < 0.026 for non-temporal generalization results and α = 0.02; FDR-adjusted q  < 0.005 for temporal generalization results).

Univariate tests and Monte Carlo simulations

The multivariate pattern analysis is a powerful tool that requires relatively few preprocessing steps for returning an estimation of the different neural activity associated with two or more experimental conditions. However, this technique does not identify which condition is stronger than the other nor the polarity of the neural signal characterizing the experimental conditions. To answer these questions and strengthen our results, we employed a different approach by calculating several univariate t -tests and then correcting for multiple comparisons by using MCS.

Before computing the t -tests, in accordance with many other MEG and electroencephalography (EEG) task studies ( Gross et al. 2013 ), we averaged over trials in each condition, obtaining two mean trials, one for the memorized and one for the novel musical sequences. Then, we combined each pair of planar gradiometers by sum-root square. Afterward, we computed a t -test for each MEG channel and each time point in the time range 0 to 2.500 s, contrasting the two experimental conditions. Independently for the two MEG sensor categories, we reshaped the matrix for obtaining, for each time point, a 2D approximation of the MEG channels layout that we binarized according to the P -values obtained from the previous t -tests (threshold = 0.01) and the sign of t -values. The resulting 3D matrix ( M , 2D × time) consisted of 0 s when the t -test was not significant and 1 s when it was. Then, to correct for the multiple comparisons happening in these univariate analyses, we identified the clusters of 1 s and assessed their significance by running MCS. Specifically, we made 1000 permutations of the elements of the original binary matrix M , identified the maximum cluster size of 1 s, and built the distribution of the 1,000 maximum cluster sizes. Finally, we considered significant the original clusters that had a size bigger than the 99.9% maximum cluster sizes of the permuted data. The whole MCS procedure was performed for gradiometers and magnetometers (in the significant time-window emerged from gradiometers, see SI Appendix SR1 for details), for memorized versus novel musical sequences and vice versa.

Relationship between neural activity and behavioral measures

We investigated whether the neural activity underlying the musical recognition task was modulated by musical expertise and individual differences along the following four behavioral measures: WM skills (i), esthetical judgment of the musical piece used in the study (ii), previous familiarity with Bach’s prelude (iii), and the Goldsmith Musical Sophistication Index (GOLD-MSI) (iv) ( Müllensiefen et al. 2013 ), which measures the ability of engaging with music. Regarding WM, we employed the widely used Wechsler Adult Intelligence Scale (WAIS-IV) ( Wechsler 1997 ), which returned a reliable measure of individual WM. With regard to esthetical judgment of Bach’s prelude, we utilized a 7-score Likert scale from −3 (very unpleasant) to +3 (very pleasant). Previous familiarity with Bach’s prelude was assessed asking participants to mark the number corresponding to one of the following options: 1) I have never heard it before; 2) I have occasionally heard it; 3) I sometimes listen to it; 4) I usually listen to it; 5) I played it for myself; and 6) I played it in front of a public . Further, in our sample, only six participants declared to have previously played the Bach’s prelude that we used in the study in front of a public and only four of them stated that they still remembered a few bars of it.

Then, we used the averaged brain activity over the significant gradiometer channels returned by our previous analysis. Here, we computed the difference between the neural activity underlying recognition of memorized versus novel musical sequences. Then, we computed independent Pearson’s correlations for each time point of the resulting time series and the four behavioral measures described above [WM skills (i), esthetical judgment of Bach’s prelude used in the study (ii), previous familiarity with Bach’s prelude (iii), and the GOLD-MSI (iv)]. Moreover, we contrasted the brain activity underlying music recognition across pianists, non-pianist musicians, and nonmusicians. For this analysis, we used one analysis of variance (ANOVA) for each time point.

The time series obtained from the correlations and the ANOVA were binarized according to the outcome of the tests [one was assigned to the significant tests ( P  < 0.05), while zero to the nonsignificant ones]. Those values were submitted to a 1D MCS with an α level = 0.001, to correct for multiple comparisons. First, we extracted the clusters of the significant results emerged from the correlations (here, cluster means group of contiguous significant values [ones]). Then, we computed 1,000 permutations and for each of them, we randomized the order of the binarized values and computed the maximum cluster size of the detected clusters of significant values. Finally, we built a reference distribution of the 1,000 maximum cluster sizes (obtained from the 1,000 permutations) and considered significant the original clusters that were larger than the 99.9% of the permuted ones.

Source reconstruction

Beamforming.

The brain activity collected on the scalp by MEG channels was reconstructed in source space. First, we coregistered the individual MRI scan with the corresponding 3D coordinates recorded during the MEG session and transformed the native space into MNI space. In the few cases (three) when the individual MRI scan was not available, we used a brain template (MNI 152 T1 template). Second, using OSL ( Woolrich et al. 2011 ), we applied a local-spheres forward model and a beamformer approach as the inverse method ( Hillebrand and Barnes 2005 ) ( Fig. 1b and c ). We utilized an 8 mm grid and both magnetometers and (noncombined) planar gradiometers. We accounted for the differing signal strength of magnetometers and gradiometers by converting their values in standardized z -scores. The spheres model used as forward model depicted the MNI-coregistered anatomy as a simplified geometric model, fitting a sphere separately for each sensor ( Hillebrand and Barnes 2005 ). The beamforming that we employed as inverse method utilized a diverse set of weights sequentially applied to the source locations for isolating the contribution of each source to the activity recorded by the MEG channels for each time point ( Hillebrand and Barnes 2005 ; Brookes et al. 2007 ). The covariance matrix necessary to compute those weights was calculated on the matrix obtained by concatenating the data of all trials for the two conditions and normalized according to Luckhoo et al. (2014) for counterbalancing the reconstruction bias toward the center of the head.

General linear model

An independent general linear model (GLM) was calculated sequentially for each time point at each dipole location and for each experimental condition ( Hunt et al. 2012 ). At first, reconstructed data were tested against its own baseline to calculate the statistics of neural sources of the two conditions memorized and novel musical sequences. Then, after computing the absolute value of the reconstructed time series to avoid sign ambiguity of the neural signal, first-level analysis was conducted, calculating contrast of parameter estimates (memorized versus novel musical sequences) for each dipole and each time point. Those results were submitted to a second-level analysis, using one-sample t -tests with spatially smoothed variance obtained with a Gaussian kernel (full-width at half-maximum: 50 mm).

Then, to correct for multiple comparisons, a cluster-based permutation test ( Hunt et al. 2012 ) with 5,000 permutations was computed on second-level analysis results, taking into account the significant time range emerged from the MEG sensors MCS significant gradiometer cluster. Therefore, we performed one permutation test on source space, using an α level of 0.05, corresponding to a cluster forming threshold of t  = 1.7.

Brain activity underlying musical sequences development

Then, as depicted in Fig. 1d , we performed an additional analysis considering the brain activity underlying the processing of each tone forming the musical sequences. To do that, we computed a GLM for each time point and source location. Then, we averaged over the time points forming each of the five time windows associated with the duration of the musical tones (0 to 250 ms, 251 to 500 ms, 501 to 750 ms, 751 to 1,000 ms, 1,001 to 1,250 ms). Finally, we corrected for multiple comparisons with a cluster-based permutation test, as described above ( Hunt et al. 2012 ). Here, when computing the significant clusters of brain activation independently for the two experimental conditions (memorized and novel musical sequences), we computed 10 permutation tests on source space, adjusting the α level to 0.005 (0.05/10), corresponding to a cluster forming threshold of t  = 2.7. Regarding memorized versus novel musical sequences, we performed five tests and therefore, the α level became 0.01 (0.05/5), corresponding to a cluster forming threshold of t  = 2.3.

Functional connectivity preprocessing

After reconstructing the data into source space, we constrained the beamforming results into the 90 noncerebellar regions of the automated anatomic labelling (AAL) parcellation, a widely used and freely available template ( Tzourio-Mazoyer et al. 2002 ) in line with previous MEG studies ( Brookes et al. 2016 ) and corrected for source leakage ( Colclough et al. 2015 ). Finally, since we were interested in studying the functional connectivity of evoked responses, according to a large number of MEG and EEG task studies ( Gross et al. 2013 ), we averaged the trials over conditions, obtaining two mean trials, one for memorized and one for novel musical sequences. In order to minimize the probability of analyzing trials that were correctly recognized by chance, here, we only considered the 20 fastest (mean RT: 1,770 ± 352 ms) correctly recognized previously memorized musical sequences (mean RT: 1,717 ± 381 ms) and novel musical sequences (mean RT: 1,822 ± 323 ms) excerpts. The same operation has been carried out for the resting state that served as baseline. Here, we created 80 pseudo-trials with the same length of the real ones, starting at random time points of the recorded resting-state data.

This procedure has been carried out for five different frequency bands (0.1 to 2 Hz, 2 to 8 Hz, 8 to 12 Hz, 12 to 32 Hz, 32 to 75 Hz) ( Lee et al. 2018 ).

Static functional connectivity and degree centrality

We estimated the static functional connectivity (SFC) calculating Pearson’s correlations between the envelope (computed using the Hilbert transform; Liu 2012 ) of each pair of brain areas time courses. This procedure has been carried out for both task and baseline and for each of the five frequency bands considered in the study. Afterward, we averaged the connectivity matrices in order to obtain one global value of connectivity for each participant and each frequency band. These values were submitted to an ANOVA to discover which frequency band yielded the strongest connectivity effects ( Allen et al. 2019 ). A follow up post-hoc analysis was conducted using Tukey’s correction for multiple comparisons. Then, for all frequency bands, we computed Wilcoxon sign-rank tests comparing each pair of brain areas for recognition task versus baseline, aiming to identify the functional connectivity specifically associated with the task. To assess the resulting connectivity matrix B , we identified the degree of each region of interest (ROI) and tested its significance through MCS 59 .

In graph theory the weighted degree of each vertex v (here each brain area) of the graph G (here the matrix B ) is given by the sum of the connection strengths of v with the other vertexes of G , showing the centrality of each v in G ( Rubinov and Sporns 2010 ). We computed the degree of each vertex of B for each musical tone, obtaining a 90 × 1 vector ( ⁠|${s}_t$|⁠ ). Then, through MCS, we assessed whether the vertices of B had a significantly higher degree than the degrees obtained permuting the elements of B . Specifically, we made 1,000 permutations of the elements in the upper triangle of B and we calculated a 90 × 1 vector |${d}_{v,p}$| containing the degree of each vertex v for each permutation p . Combining vectors |${d}_{v,p}$| we obtained the distribution of the degrees calculated for each permutation. Finally, we considered significant the original degrees stored in |${s}_t$| that randomly occurred during the 1,000 permutations less than two times. This threshold was obtained by dividing the α level (0.001) by the five frequency bands considered in the study. The α level was set to 0.001 since this is the threshold that, during simulations with input matrices of uniformly distributed random numbers, provided no false positives. This procedure was carried out for each frequency band and for both experimental conditions.

MEG sensor data

Our first analysis was conducted on MEG sensor data and focused on the brain activity underlying the recognition of the previously memorized versus novel musical sequences. On average, participants correctly identified the 78.15 ± 13.56% of the previously memorized melodies [mean d’ score = 1.70 ± 1.03; mean reaction times (RTs): 1,871 ± 209 ms] and the 81.43 ± 14.12% of the novel melodies (mean d’ score = 2.38 ± 1.63; mean RTs: 1915 ± 135 ms). Subsequent MEG sensor data analyses were conducted on correct trials only.

As depicted in Fig. 2a , we conducted a multivariate pattern analysis using a support vector machine (SVM) classifier (see details in the Materials and Methods section) to decode different neural activity associated with the recognition of previously memorized versus novel musical sequences. This analysis resulted in a decoding time series showing how neural activity differentiated the two experimental conditions. The decoding time series was significantly different from chance level in the time range 0.8 to 2.1 s from the onset of the first tone ( q  < 0.026, false-discovery rate (FDR)–corrected, Fig. 2a , top left). The highest accuracy was reached when predicting the neural activity underlying recognition of novel melodies, while the prediction of previously memorized melodies was less accurate, as shown by confusion matrix computed for each time point ( Figure SF4 ).

Brain activity underlying memorized versus novel musical sequences. a) Multivariate pattern analysis decoding the different neural activity associated with memorized versus novel musical sequences. Decoding time series (left), spatial sequences depicted as topoplot (middle left), temporal generalization decoding accuracy (middle right), and statistical output of significant prediction of training time on testing time (right). b) The left plot shows the amplitude associated with memorized (red) and novel musical sequences (blue). The middle plot illustrates the t -statistics related to the contrast between memorized versus novel musical sequences. The right plot shows the correlation between WM abilities and the neural responses underlying recognition of the memorized versus novel musical sequences. Thinner lines depict standard errors. The plots refer to the average over the gradiometer channels forming the significant cluster outputted by MEG sensor MCS. c) Three couples of topoplots showing brain activity for gradiometers (left of each pair, fT/cm) and magnetometers (right of each pair, fT) within the significant time window emerged from MCS. First couple of topoplots depicts the neural activity underlying the recognition of the previously memorized musical sequences, second couple refers to the novel musical sequences, while the third one represents the statistics ( t -values) contrasting the brain activity underlying recognition of memorized versus novel musical sequences. d) Neural sources for the recognition of memorized sequences (left), novel sequences (middle), and their contrast (right). The values are t -statistics.

To evaluate the persistence of discriminable information over time, we applied a temporal generalization approach by training the SVM classifier at a given time point t , as before, but testing across all other time points. FDR-corrected ( q  < 0.005) results are depicted in Fig. 2a (top right) showing that performance of the classifier was significantly above chance even a few hundreds of milliseconds beyond the diagonal.

In addition, we computed the same analysis after following only minimal preprocessing steps (i.e. MaxFilter and ICA for removing eyeblink and heart-beat artifacts) and using a sampling rate of 250 Hz. The analysis returned very similar results, although overall less strong. Indeed, the decoding time series was significantly different from chance level in several time windows in the time range 0.8 to 2.1 s from the onset of the first tone ( q  < 0.026, FDR-corrected). Moreover, temporal generalization analysis confirmed that the performance of the classifier was significantly above chance even a few hundreds of milliseconds beyond the diagonal ( P  < 0.005). A graphical depiction of the comparison between the two analyses that differed only for the preprocessing steps and for the sampling rate is provided in Figure SF2 . Our procedure clearly shows that the significance of the results of our main analysis pipeline was not affected by computing low-pass and notch filter and by using 150 Hz instead of a different sampling rate such as 250 Hz.

Univariate tests and MCSs

First, we contrasted the previously memorized versus novel musical sequences ( t -test threshold = 0.01, MCS threshold = 0.001, 1000 permutations), considering the positive t -values only (which is when the memorized music was associated with a stronger brain activity than the novel melodies). We performed this analysis in the time range 0 to 2.5 s by using combined planar gradiometers only. This procedure yielded the identification of one main significant cluster (MCS P  < 0.001; time: 0.547–1.180 s, size: 2117), as depicted in Fig. 2b and c and reported in detail in Tables ST1 and ST3 . In addition, we computed the same analysis after following only minimal preprocessing steps (i.e. MaxFilter and ICA for removing eyeblink and heart-beat artifacts) and using a sampling rate of 250 Hz. The results returned one large cluster that was very similar to the one described above (MCS P  < 0.001; time: 0.640 to 1.140 s, size: 2,649). Detailed statistics is reported in Table ST3 , while a graphical depiction of the comparison between the two analyses that differed only for the preprocessing steps and for the sampling rate is provided in Figure SF2 . Our procedure clearly shows that the results of our main analysis pipeline were not affected by computing low-pass and notch filter and by using 150 Hz instead of a different sampling rate such as 250 Hz.

After working with the gradiometers, we computed analyses for the magnetometers. Here, based on the significant cluster appearing, we computed the same algorithm one more time for magnetometers only, within the significant time range emerged for the first MCS (0.547 to 1.180 s, P  < 0.001, Table ST1 ). This two-step procedure was necessitated by the sign ambiguity typical of magnetometer data and returned three significant clusters (positive magnetometers: MCS P  < 0.001; time: 0.627 to 1.180 s, size: 817: negative magnetometers: Cluster I—MCS P  < 0.001; time: 0.727 to 0.880 s, size: 190; Cluster II—MCS P  < 0.001; time: 0.960 to 1.133 s, size: 168).

Then, the same procedure was carried out by considering the results where the brain activity associated with the novel melodies exceeded the one elicited by Bach’s prelude excerpts. This analysis returned eight small significant clusters (size range: 6 to 14, P  < 0.001) shown in Table ST2 .

Relationship between brain activity and behavioral measures

Once we established that the recognition of the memorized and novel musical sequences gave rise to clearly different brain activity, we investigated whether such activity was modulated by individual differences such as WM and musical skills related to the Bach’s prelude used in the study.

We correlated each time point of the brain activity time series with four behavioral measures: WM skill (i), esthetical judgment of the Bach’s prelude (ii), previous familiarity with the Bach’s prelude (iii), and the GOLD-MSI (iv), which measures the ability of engaging with music. We corrected for multiple comparisons by using MCS with significance level α = 0.001. Overall, the results showed that the neural activity was not correlated to such measures. Indeed, detailed analysis revealed only few small significant clusters. Two clusters at the gradiometer level were found for WM (Cluster I: MCS P  < 0.001; time: 1.77 to 1.85, mean r  = 0.32; Cluster II: MCS P  < 0.001; time: 2.48 to 2.53, mean r  = 0.30, Fig. 2b ), meaning that participants with higher WM presented a stronger neural activity underlying musical recognition. Regarding liking, we detected two significant clusters (Cluster I: MCS P  < 0.001; time: 2.40 to 2.45, mean r  = 0.33 ( Figure SF5 ); Cluster II: MCS P  < 0.001; time: 1.11 to 1.14, mean r  = −0.27). The two clusters were small and pointed to different directions of the correlation with the neural data (one was positive and the other negative), suggesting that the esthetical judgment was not a good predictor of the brain activity. Additionally, both familiarity with Bach’s prelude and the GOLD-MSI returned only one small cluster showing a negative correlation: Cluster I: MCS P  < 0.001; time: 1.18 to 1.21, mean r  = −0.28. (familiarity); Cluster I: MCS P  < 0.001; time: 0.10 to 0.13, mean r  = −0.29 (GOLD-MSI) ( Figure SF5 ). Finally, no significant differences were detected when contrasting the brain activity underlying music recognition across pianists, non-pianist musicians, and nonmusicians ( Figure SF5 ).

Source-reconstructed data

To identify the neural sources of the signal, we employed a beamforming approach and computed a GLM for assessing, at each time point, the independent neural activity associated with the two conditions as well as their contrasts.

Main cluster of previously memorized versus novel musical sequences

We identified the neural sources of the gradiometers significant cluster emerging from the MEG sensor data when contrasting memorized versus novel musical sequences. Here, we performed one permutation test in source space, with an α level of 0.05, which, in our case, corresponded to a cluster forming threshold of t  = 1.7. As depicted in Fig. 2d , results showed a strong activity originating in the primary auditory cortex, insula, hippocampus, frontal operculum, cingulate cortex, and basal ganglia. Detailed statistics are provided in Table ST4 . In addition, we computed the same analysis after following only minimal preprocessing steps (i.e. MaxFilter and ICA for removing eyeblink and heart-beat artifacts) and using a sampling rate of 250 Hz. The analyses returned extremely similar results to the ones described above, localizing the significant difference between memorized and novel musical sequences auditory cortex, insula, hippocampus, frontal operculum, cingulate cortex, and basal ganglia. Detailed statistics is reported in Table ST4 , while a graphical depiction of the comparison between the two analyses that differed only for the preprocessing steps and for the sampling rate is provided in Figure SF2 . Our procedure clearly shows that the results of our main analysis pipeline were not affected by computing low-pass and notch filter and by using 150 Hz instead of a different sampling rate such as 250 Hz.

Dynamic brain activity during development of musical sequences

To reveal the specific brain activity dynamics underlying the recognition of the musical sequences, we carried out a further analysis for each musical tone forming the musical sequence. Here, we adopted a stricter cluster forming threshold of t  = 2.7 (see Materials and Methods for details). As depicted in Fig. 3a and b , we found significant activity within primary auditory cortex and insula, especially in the right hemisphere, for both experimental conditions. This activity decreased over time, following the unfolding of the musical sequences. Conversely, the contrast between memorized versus novel music gave rise to a burst of activity for the memorized Bach’s excerpts increasing over time, especially with regard to the last three tones of the musical sequences, as shown in Fig. 3c . This activity was mainly localized within hippocampus, frontal operculum, cingulate cortex, insula, inferior temporal cortex, and basal ganglia. We report detailed clusters statistics in Table ST5 . In addition, we computed the same analysis after following only minimal preprocessing steps (i.e. MaxFilter and ICA for removing eyeblink and heart-beat artifacts) and using a sampling rate of 250 Hz. The analyses returned extremely similar results to the ones described above, localizing the significant difference between memorized and novel musical sequences auditory cortex, insula, hippocampus, frontal operculum, cingulate cortex, and basal ganglia. Moreover, the strongest difference between the two conditions was obtained for the last three tones of the musical sequences. Detailed statistics is reported in Table ST5 , while a graphical depiction of the comparison between the two analyses that differed only for the preprocessing steps and for the sampling rate is provided in Figure SF3 . Our procedure clearly shows that the results of our main analysis pipeline were not affected by computing low-pass and notch filter and by using 150 Hz instead of a different sampling rate such as 250 Hz.

Brain activity over time. a) Brain activity (localized with beamforming) associated with the recognition of previously memorized musical sequences (top row). Such sequences were extracted from the Bach’s prelude that participants attentively listened to before doing the recognition task. The bottom row depicts an example trial for the memorized sequences. Red tones illustrate the dynamics of the musical excerpt. b) Brain activity underlying the detection of the novel musical sequence (top row) and musical representation of one example trial (bottom row). c) Contrast ( t -values) over time between the brain activity underlying memorized versus novel musical sequences.

Static functional connectivity

To obtain a better understanding of the brain dynamics underlying recognition, we complemented our brain activity results with an investigation of the static functional connectivity of the evoked responses.

After constraining the MEG preprocessed data to the 90 noncerebellar parcels of the AAL parcellation, we estimated static functional connectivity by using Pearson’s correlations in five frequency bands: 0.1 to 2 Hz, 2 to 8 Hz, 8 to 12 Hz, 12 to 32 Hz, 32 to 74 Hz. Then, we tested the overall connectivity strengths of the five frequency bands during auditory recognition by employing ANOVA. The test was significant [ F (4,330) = 187.02, P  < 1.0e-07). As depicted in Fig. 4a and b , post-hoc analysis highlighted especially that the 2 to 8 Hz band had a stronger connectivity profile than all other frequency bands ( P  < 1.0e-07).

Static functional connectivity. a) Contrast between recognition task (memorized and novel musical sequences averaged together) and baseline SFC matrices calculated for five frequency bands: 0.1 to 2 Hz, 2 to 8 Hz, 8 to 12 Hz, 12 to 32 Hz, and 32 to 74 Hz. b) Violin-scatter plot showing the average of the SFC matrices over their two dimensions for all participants. c) Averaged MEG gradiometer channels waveform of the brain activity associated with the recognition task. d) Power spectra for the evoked responses associated with the recognition task computed for all MEG channels. The first power spectra matrix reflects the analysis from 1 to 74 Hz in 1 Hz intervals, while the second reflects the analysis from 1 to 30 Hz in 1 Hz intervals.

To detect the significance of each brain region centrality within the whole-brain network for the auditory recognition task, we contrasted the brain connectivity matrices associated with the task versus baseline by performing a Wilcoxon signed-rank test for each pair of brain areas. Then, the resulting z -values matrix was submitted to a degree MCS (see Materials and Methods for details). We computed this analysis independently for the five frequency bands, and therefore, we considered significant the brain regions whose P -value was lower than the α level divided by 5 (2.0e-04). The results for 2 to 8 Hz are depicted in Figure SF6 and reported as follows: left Rolandic operculum ( P  < 1.0e-07), insula ( P  < 1.0e-07), hippocampus ( P  = 5.5e-05), putamen ( P  < 1.0e-07), pallidum ( P  < 1.0e-07), caudate ( P  = 1.1e-05), thalamus ( P  < 1.0e-07), Heschl’s gyrus ( P  < 1.0e-07), superior temporal gyrus ( P  < 1.0e-07), right superior temporal gyrus ( P  = 1.1e-06), Heschl’s gyrus ( P  < 1.0e-07), thalamus ( P  < 1.0e-07), parahippocampal gyrus ( P  = 4.3e-05), pallidum ( P  < 1.0e-07), putamen ( P  < 1.0e-07), amygdala ( P  < 1.0e-07), insula ( P  < 1.0e-07), and Rolandic operculum ( P  < 1.0e-07). Additional results related to the other frequency bands are reported in supporting information (SI) Appendix (SR2) .

Conversely, the degree MCS of the contrasts between memorized versus novel melodies yielded no significant results.

In this study, we detected the spatiotemporal dynamics of the whole-brain activity and functional connectivity during recognition of previously memorized auditory sequences compared to matched novel melodies.

First, by using a broadband multivariate pattern analysis and MCS of massive univariate data, we found converging evidence that the brain activity elicited by the recognition of musical excerpts extracted from Bach’s prelude compared to the novel musical sequences gave rise to significant changes in widespread regions including the primary auditory cortex, superior temporal gyrus, cingulate gyrus, hippocampus, basal ganglia, insula, and frontal operculum. Notably, the neural difference reflecting the recognition of memorized versus novel musical sequences extended from approximately 700 to 2,000 ms after the onset of the first tone of the melodies. This suggests that the brain discriminated the two categories of musical sequences, especially from tone number three of the sequences. Interestingly, this difference in neural activity extended up to 2,000 ms, which corresponded to about 100 ms after participants categorized the musical sequences by using the response pad (the mean reaction time was approximately 1,900 ms for both categories of sequences). This could indicate that the elaborated process of recognition and discrimination of the two categories of musical sequences requires a widespread network of brain areas whose activity was differentiated for more than 1,000 ms.

Second, we inspected this finding further by estimating static functional brain connectivity evolving over time. Here, the recognition of both previously memorized and novel auditory sequences was accompanied by significant centrality within the whole-brain network of several brain regions including the insula, hippocampus, cingulate gyrus, auditory cortex, basal ganglia, frontal operculum, and subgenual and orbitofrontal cortices. This result emerged only for the frequency band: 2 to 8 Hz.

First of all, the results presented in the current study either replicate or closely align to findings reported in our prior research on the long-term encoding and recognition of music ( Bonetti et al. 2021a ; Bonetti et al. 2022a ; Bonetti et al. 2024 ; Fernandez-Rubio et al. 2022 ; Fernández-Rubio et al. 2022 ), as well as with several studies investigating music, auditory perception, and memory processes. For instance, the brain activity observed in our study during the recognition of musical sequences aligns with previous research indicating auditory processes associated with the primary auditory cortex and insula ( Mutschler et al. 2007 ; Brattico and Pearce 2013 ). Moreover, in this study we observed stronger activity underlying the recognition of the memorized sequence in brain areas related to memory recognition such as hippocampus, medial temporal cortices ( Brown and Aggleton 2001 ; Bird 2017 ), and cingulate cortices ( Teixeira et al. 2006 ). Additionally, the recognition of excerpts from Bach’s prelude was associated with a stronger activity of brain regions previously related to evaluative processes ( Bach et al. 2008 ; Stephenson-Jones et al. 2016 ) and pleasure ( Kringelbach 2010 ) such as the cingulate gyrus and subgenual cortices, as well as parts of the basal ganglia. Finally, recognition of memorized music was accompanied by stronger activity in brain regions responsible for fine-grained auditory elaboration and prediction error such as the inferior temporal cortex ( Zatorre et al. 2002 ) and insula ( Limongi et al. 2013 ). Of particular interest is the involvement of the hippocampus and cingulate gyrus, whose role in auditory processing is not completely clear. The hippocampus has been previously reported in relation to music, especially in fMRI studies that investigated memory processing for sounds ( Baumgartner et al. 2006 ; Alluri et al. 2015 ). Nevertheless, less is known about its the fast temporal dynamics in relation to music processing. Interestingly, our results indicated that the hippocampus was mainly relevant for the abstract recognition of the melodic sequence, while its involvement in the auditory processing of single sounds was reduced. The cingulate gyrus is an associative area that has been connected to several cognitive processes involving music imagery, but not memory ( Criscuolo et al. 2019 ; Criscuolo et al. 2022 ). Notably, in this study, we showed that it was strongly involved in music recognition, highlighting its relevance for auditory memory. Future research should further investigate the specific role played by the cingulate gyrus, clarifying, for example, whether it is mainly related to the actual recognition process or if it modulates the attentional resources for the task. Interestingly, several brain regions we identified, such as the orbitofrontal cortex, cingulate gyrus, insula, and thalamus, are part of the limbic system. Despite our stimuli being brief musical excerpts not designed to elicit a wide range of emotional responses, these regions were still activated. Although definitive conclusions cannot yet be drawn, this activation may be due to two factors. First, the identified limbic regions are implicated in diverse functions beyond emotions ( Bach et al. 2008 ; Limongi et al. 2013 ; Stephenson-Jones et al. 2016 ; Criscuolo et al. 2019 ; Criscuolo et al. 2022 ) and, in this context, they may be more engaged in evaluative and memory functions rather than emotional ones. Alternatively, participants may have experienced a sense of recognition and correctness when identifying the musical excerpts as familiar melodies, which could evoke an emotional response, arguably of pleasure, thereby engaging the limbic system. Future research should specifically investigate these possibilities.

After verifying that our results were consistent with and replicated findings from our prior research and other studies in the field, we proceeded to test our novel hypotheses. The first hypothesis was to examine both the commonalities and distinctions in brain activity and functional connectivity during the recognition of musical sequences. Our connectivity analysis revealed both important similarities and differences with the brain activity, coherently with our previous works on the topic ( Bonetti et al. 2022a ; Fernandez-Rubio et al. 2022 ; Fernández-Rubio et al. 2022 ). A key similarity consists of the significant brain areas emerged by conducting activity and functional connectivity analyses. Indeed, both analyses returned a network comprising insula, hippocampus, cingulate gyrus, auditory cortex, basal ganglia, frontal operculum, and subgenual and orbitofrontal cortices. This suggests that processing of auditory sequences does not only require the mere activation of a large network of brain areas but also their communication over time. Interestingly, a relevant difference between activity and functional connectivity was that the primary auditory cortex did not play a crucial role in functional connectivity, while it was central in the brain activity elicited by the presentation of the sounds. Moreover, the main connectivity patterns emerged for brain regions not only related to auditory processing but also to higher sound and linguistic elaborations such as the insula, inferior temporal cortex ( Zatorre et al. 2002 ; Limongi et al. 2013 ), and frontal operculum ( Koelsch et al. 2006 ). Furthermore, we also observed other central brain areas that have been previously related to evaluative (e.g. orbitofrontal and subgenual cortices; Bach et al. 2008 ) and memory recognition (e.g. hippocampus; Squire and Bayley 2007 and basal ganglia; Stephenson-Jones et al. 2016 ) processes. Another relevant difference was that while the activity of the brain areas was strongly diverse in the recognition of the previously memorized versus novel sequences, the functional connectivity analysis did not reveal any difference between the two experimental conditions. This finding suggests that the communication between the large network of brain areas that we revealed may be necessary to process auditory sequences, while the key for discerning previously memorized from novel melodies may be in the differential activity over time of some of the key brain regions comprised in the large network. Lastly, it is important to emphasize that we observed significant connectivity patterns only within the 2 to 8 Hz frequency range. Given that our study presented sounds at a frequency of 4 Hz (with each sound lasting 250 ms), we cannot definitively determine whether the heightened connectivity in the 2 to 8 Hz range reflects a specific brain rhythm or a stimulus-driven brain response. Future studies are needed to explore this observation further. Moreover, future research should also investigate this phenomenon under different experimental conditions, including not only comparing resting state to active listening during memory tasks but also incorporating passive listening conditions. These efforts will help to refine our understanding of the functional connectivity mechanisms involved in music recognition.

The second hypothesis consisted of comparing the brain networks revealed by the functional connectivity analysis with those obtained using the same analysis for sound encoding in Bonetti et al. (2021b) . This comparison holds particular significance because our study utilized a different dataset from the same participants as Bonetti et al. (2021b) , enabling direct and valuable insights into similarities and differences. Remarkably, the network of brain areas emerging for the recognition of musical sequences highly resembled the one reported in our previous work. Indeed, primary and secondary auditory cortex, insula, hippocampus, basal ganglia, cingulate gyrus, and frontal operculum were highly involved both in encoding and recognition of sounds and sequences. Interestingly, while encoding mainly recruited a network of brain areas in the right hemisphere (both in terms of activity and connectivity), recognition of musical sequences was associated with activity and centrality of both hemispheres, with an overall stronger involvement of the left one. Taken together, our two studies reinforce the thesis that for both encoding and recognition of music a large network of functionally connected brain regions typically involved in auditory, memory and evaluative processes is required.

The third hypothesis involved employing temporal generalization in multivariate pattern analysis to investigate how brain patterns generalize over time. In our previous work ( Bonetti et al. 2024 ), we demonstrated that distinguishing between previously memorized and systematically varied musical sequences resulted in consistent brain patterns persisting throughout the entire sequence duration. Specifically, our findings indicated that brain responses to individual sounds within memorized and novel sequences were consistently similar, suggesting ongoing monitoring of each sound, confirmation of predictions aligned with memory traces, and error detection when discrepancies occurred. In this study, we extended our investigation by comparing brain patterns between previously memorized melodies and entirely novel ones (i.e. not systematically varied after specific sounds) to determine whether differential brain activity patterns remained stable across the entire musical sequence or exhibited divergence. The findings of this study revealed no consistent patterns, indicating that the differential activity between corresponding tones of the memorized and novel sequences varied throughout the sequence (e.g. the differential activity at tone three differed from that at tones four or five). This discrepancy likely stems from two potential reasons. First, it may be attributed to the faster pace of each auditory stimulus (250 ms in the current study) compared to our previous investigation (350 ms in Bonetti et al. 2024 ). The increased tempo may necessitate a more holistic processing of the musical sequence by the brain, rather than focusing on individual sounds. Consequently, this could explain why different sounds are processed in distinct ways, thus preventing generalization over time in our decoding analysis. The alternative explanation involves the absence of systematic variations to the original sequences in the novel condition. In Bonetti et al. (2024) , the novel melodies initially mirrored the previously memorized ones before gradually diverging. This divergence induced prediction errors, reflected in stable brain patterns over time, which suggested that the brain monitored the entire sequence in a similar manner. This might be because the brain recognized the beginning of the sequence and attempted to discern whether the change was temporary or marked the start of a varied melody. In the current study, where the novel melodies were entirely different from the memorized originals, the brain appeared not to engage in such continuous monitoring, as indicated by the nongeneralizable results of the multivariate pattern analysis.

Altogether, our findings can be seen in light of the global neuronal workspace hypothesis proposed by Dehaene and Changeux (2005) . They defined the global workspace as a privileged network of brain areas, where conscious information is processed in terms of memory, attention, and valence and subsequently broadcast and made available to the whole-brain ( Dehaene and Changeux 2005 , 2011 ). As predicted by their hypothesis, the recognition of the memorized musical sequences extracted from Bach’s prelude—over and above the novel melodic sequences—led to stronger ignition of putative regions in the global workspace such as the hippocampus, cingulate gyrus, orbitofrontal cortex, and frontal operculum, perhaps reflecting the mechanisms that allow the brain to process, extract a meaningful representation, and recognize previously memorized musical sequences. Remarkably, our research did not only show the brain regions involved in the musical recognition task but also provided the dynamics of the activity of such regions, thus expanding the hypothesis proposed by Changeux. We observed that the brain activity linked to the recognition of memorized versus novel musical sequences significantly differed from the third tone of the sequences. Moreover, such different activity was first observed for the cingulate gyrus (third, fourth, and fifth tone of the melodies) and then for the hippocampal areas, inferior temporal cortex, insula, and frontal operculum (fourth and fifth tone of the melodies). Interestingly, our findings showed that the conscious, effortful recognition of temporal sequences involved several high-order brain areas, while previous studies on automatic recognition and prediction error associated with sudden deviations in auditory sequences (e.g. indexed by MMN and N100) revealed a major contribution of sensorial brain areas such as auditory cortices ( Bonetti et al. 2021a ; Bonetti et al. 2022b ; Bonetti et al. 2018 ; Bonetti et al. 2017 ; Näätänen et al. 2007 ). This provides evidence for the relevance of the global neuronal workspace for conscious over automatic temporal sequence discrimination and recognition.

A further theory in the neuroscientific field that can be related to our results is predictive coding. In this framework, the brain is considered a generator of models of expectations of incoming stimuli. Recently, this theory has been linked to complex cognitive processes, finding a remarkable example in the neuroscience of music ( Koelsch et al. 2019 ). In their work, Koelsch and colleagues suggested that the perception of music is the result of an active listening process where individuals constantly formulate hypothesis about the upcoming development of musical sentences, while those sentences are evolving and unfolding their ambiguities. Our study may be consistent with this perspective with regard to two of our outcomes. On the one hand, there is activity in the primary auditory cortex, responsible for the first sensorial processing of tones and decreasing over time. This may happen since the brain is predicting that a further tone will be presented, and its responses progressively decrease. On the other hand, the activation of brain areas related to memory and evaluative processes is increasing over time and stronger for the recognition of the memorized versus novel musical sequences. This may suggest that the brain has formulated predictions of the upcoming sounds based on the memory trace previously stored during the encoding part of our experimental task. The match between those predictions and the actual sounds presented to participants may lead to the activation of the brain areas that we observed in our experiment. However, further studies are required to provide additional evidence that can properly demonstrate whether the brain is predicting the upcoming tones of the musical melodies. In our research, the novel melodies were completely diverse from the memorized ones. Differently, future investigations may systematically vary the novel melodies, introducing the variations at specific times (e.g. from tone number two, from tone number three). If the brain is predicting the upcoming sound, it will systematically show a prediction error signal when the sequence is varied.

Analyzing the relationship between the brain activity during our recognition task and behavioral measures related to memory and musical skills showed very weak associations in small, isolated clusters. This suggested that having more engagement with music, general musical expertise, or a previous familiarity and higher appreciation for Bach’s prelude does not play a major role in modulating the brain activity during the musical recognition task. However, a mild yet interesting effect was observed for WM. This evidence, coherently with previous research ( Bonetti et al. 2018 ), shows a connection between WM skills and neural data underlying memory tasks, indicating that the brain of individuals with higher WM abilities is characterized by a stronger activity when recognizing temporal sequences such as the excerpts from Bach’s prelude. Our results suggest that memory skills may be more important than musical abilities and expertise when recognizing temporal sequences, even when they consist of musical melodies. Future studies are called to further investigate the relationship between WM and the brain activity underlying recognition of long-term encoded auditory information.

We also investigated our data by focusing on MEG sensor analysis. In this regard, we found that brain activity was reflected by two ERF components: N100 to each sound and a slow negativity following the entire duration of the musical sequences. This negative waveform shares similarities with well-established ERF components such as contingent negative variation (CNV) ( Walter et al. 1964 ; Naatanen et al. 2001 ). However, it also holds novel significance since in the current study it is associated with a conscious auditory recognition task, differently from classic studies on CNV ( Walter et al. 1964 ; Rohrbaugh et al. 1976 ). Thus, in combination with findings reported by Bonetti et al. (2022a) , our results offer new insights also into the dynamics of ERF and MEG sensor analyses. Future research is necessary to determine whether the observed slow negativity is influenced by the tempo of the stimuli or occurs independently of the musical pace.

Finally, it is important to highlight that while a major part of our findings was localized in the cerebral cortex (e.g. primary and secondary auditory cortex, insular cortex, cingulate cortex, frontal operculum), we also observed significant results reconstructed in deeper, subcortical areas such as hippocampus and basal ganglia. Whether MEG source reconstruction algorithms can reliably localize deep sources is part of a long-standing debate in the literature, and it is difficult to make definitive claims. On balance, it must be stated that deep sources are less easily detectable than cortical sources ( Hillebrand and Barnes 2002 ; Goldenholz et al. 2009 ). Therefore, on the one hand, our results related to hippocampus and basal ganglia should be taken cautiously and call for future replications. On the other hand, there is no reason to believe that deep sources cannot be identified at all using MEG, as suggested by the mathematics behind source reconstruction algorithms such as beamforming and by several previous studies on MEG source reconstruction ( Muller et al. 2019 ; Pizzo et al. 2019 ). To summarize, we argue that our subcortical results are reliable although obviously less accurate than our findings concerning cortical areas. Thus, they call for further confirmation by future studies, possibly employing not only MEG but also different machines and techniques such as fMRI and intracranial EEG (iEEG).

In conclusion, we have identified the spatiotemporal unfolding of fast-scale brain activity and functional connectivity associated with the recognition of previously memorized compared to novel musical sequences extended over time. We have shown the brain areas which were active and communicating during the processing of the subsequent items of the melodies, thus offering a first glimpse of the neural processing of temporal sequences. Future studies are called to replicate our results and further investigate the complex topic of encoding and recognition of temporal sequences. For instance, they should vary the experimental design and better disentangling the role of the different brain areas involved in the networks shown in our work.

We thank Giulia Donati, Riccardo Proietti, Giulio Carraturo, Mick Holt, and Holger Friis for their assistance in the neuroscientific experiment. We also thank the psychologist Tina Birgitte Wisbech Carstensen for her help with the administration of psychological tests and questionnaires. The corresponding author L.B. ( [email protected] ) works at the Center for Music in the Brain, Aarhus University, Universitetsbyen 3, Aarhus, Denmark, 8000 and at the Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Stoke Place 7, OX39BX, Oxford, United Kingdom.

L. Bonetti (Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing—original draft, Writing—review & editing), Elvira Brattico (Conceptualization, Funding acquisition, Investigation, Supervision, Writing—review & editing), F. Carlomagno (Formal analysis, Methodology, Software, Visualization, Writing—review & editing), Joana Cabral (Formal analysis, Methodology, Software, Validation, Writing—review & editing), Angus Stevner (Formal analysis, Methodology, Software, Writing—review & editing), Gustavo Deco (Formal analysis, Methodology, Resources, Supervision), P.C. Whybrow (Investigation, Supervision, Writing—review & editing), M. Pearce, (Methodology, Software, Supervision, Validation, Writing—review & editing), Dimitrios Pantazis (Formal analysis, Methodology, Software, Supervision, Validation, Writing—review & editing), P. Vuust (Conceptualization, Funding acquisition, Project administration, Supervision, Validation, Writing—review & editing), and Morten Kringelbach (Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Software, Supervision, Visualization, Writing—review & editing).

The Center for Music in the Brain (MIB) is funded by the Danish National Research Foundation (project number DNRF117).

L.B. is supported by Lundbeck Foundation (Talent Prize 2022), Carlsberg Foundation (CF20-0239), Center for Music in the Brain, Linacre College of the University of Oxford, and Society for Education and Music Psychology (SEMPRE’s 50th Anniversary Awards Scheme).

M.L.K. is supported by Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117), and Centre for Eudaimonia and Human Flourishing funded by the Pettit and Carlsberg Foundations.

G.D. is supported by the Spanish Research Project PSI2016-75688-P (AEI/FEDER, EU), by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreements n. 720270 (HBP SGA1) and n. 785907 (HBP SGA2), and by the Catalan AGAUR Programme 2017 SGR 1545.

J. C. is supported by La Caixa Foundation, Spain (LCF/BQ/PR22/11920014) and the Foundation for Science and Technology, Portugal (UIDB/50026/2020, UIDP/50026/2020). Additionally, we thank the Italian section of Mensa: The International High IQ Society for the economic support provided to the author Francesco Carlomagno and the University of Bologna for the economic support provided to the students Giulia Donati, Riccardo Proietti, and Giulio Carraturo.

Conflict of interest statement : None declared.

The code used for the full analysis pipeline is available at the following link: https://github.com/leonardob92/MelodiesRecognition_LB2017_BroadbandActivity_StaticFunctionalConnectivity.git

Additional code related to the study is available at the following link: https://github.com/leonardob92/LBPD-1.0.git

The multimodal neuroimaging data will be made available upon reasonable request.

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