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4-year Undergraduate Program in Computation and Behavioral Sciences

Sun, 20 Sep 2026 20:49:01 +0200

If you have ever discussed Cognitive Science with me for extended periods of time, then you’d know that I have often lamented about the need for a 10 year cognitive science “pre-graduate” program. However true it may be that acquiring relevant knowledge from all the different subdisciplines of Cognitive Science – Psychology, Computer Science, Neuroscience, Philosophy, Linguistics, and Anthropology – takes a long amount of time, such a program would be impractical for at least two main reasons:

Impracticality of long-term planning: To be employable by mid-20s at the latest requires starting by the age of 14 or 16. No sane person in this age group commits to a program so long, that too, with questionable financial or employment utility, especially in the 21st century.
Inclination towards financially-wise choices: It cannot be denied that everyone is trying to find a way or two to earn money by doing activities that aren’t so unpleasurable. In particular, people usually choose undergraduate (or even graduate) programs by the extent of their financial utility they generate. That’s not to say interests have no role. But given two things that look equally enticing in non-monetary terms, one is inclined to choose one that yields them better money.

Time and again, almost all the (senior) researchers I have come across in the field of Cognitive Science over the last 4 years have expressed that mathematical maturity goes a long way in any field of research including Cognitive Science. Interestingly, a related skill concerning programming is also an immensely useful skill in the 21st century in terms of its financial utility. Putting these two together, it seems that it might actually be possible to propose a standard 3- or 4-year undergraduate program that has both utilities (i) financial/employment (ii) research/higher-studies.

As a doctoral student of Cognitive Science, I still lack any background in Linguistics, Neuroscience, or Anthropology. Whatever little I might have gained through occasional exposure is lost through time. So, it’s difficult to judge the below resources by myself. I invite readers to either leave comments below, or on the public post from which you found this page, or email them to me.

By labeling the program as “Computation and Behavioral Sciences”, I have already split the curriculum into two parts. Thus, anyone with a sufficient exposure from these other disciplines of Linguistics, Neuroscience or Anthropology might be able to suggest the appropriate changes to the second part to come up with a corresponding “Computation and XYZ”. In fact, MIT itself has a curriculum focusing on Neuroscience. My own preference for the curriculum comes from my exposure to Cognitive Science at IIT Kanpur and my ongoing PhD at CEU.

Semester	Computation	Behavior
1	Introduction to Algebra	Introduction to Cognitive Psychology
	Introduction to Geometry	Fundamentals of Biology
	Introduction to Programming
2.0	Paradigms of Programming	Introduction to Neuroscience
2.1	Polynomials and Equations
	Basic Statistics
2.2	Coordinate Geometry	Philosophy of Mind
3	Probabilty Theory	Hypothesis Testing and Frequentist Methods
	Single-variable Calculus	Philosophy of Science
	Practical Programming
4	Multivariate Calculus	Basic Experimental Methods
	Linear Algebra	Bayesian Statistics
	Discrete Structures
5	Data Structures	Computational Modeling
	Machine Learning	Embodied, Embedded, Enacted, Extended Cognition
	Databases
6	Non-Classical Logic	Neural Data Analysis
	<Computational Elective 1>	Causal Models and Explanations
		<Behavioral Elective 1>
7	<Computational Elective 2>	<Behavioral Elective 2>
	<Research Project 1>	<Research Project 1>
8	<Research Project 2>	<Research Project 2>
	<General Elective 1>	<General Elective 2>

The nice thing about the above is even if one drops out by year 1 (semester 2) or year 2 (semester 4), they would already be familiar with numerous topics that are part of the computer science curriculum or its prerequisites. As teachyourselfcs.com says: There are 2 types of software engineer: those who understand computer science well enough to do challenging, innovative work, and those who just get by because they’re familiar with a few high level tools. The courses listed above are motivated for the later kinds of programmers.

One objection to this curriculum can be that this is too heavily focused on Computer Science and Mathematics. The reasons are two-fold: Firstly, because many students opting for Behavioral Science due to a spite with Mathematics. Unfortunately, Mathematics comes back to bite people during their Graduate Studies, if not eventually in life. Secondly, an exposure to Data Structures, Discrete Structures, and Databases serves to provide more examples of “structures that the mind might use for computation”, thus helping the student and the eventual researcher keep an “open mind” once out of college.

With that said, let me now list some resources that may be used for the courses themselves. This is still a work in progress, and is the most time and effort consuming part of this page. Reader discretion is advised for improved suggestions. The audience in mind is a class 12th graduate who may not be good with mathematics; or who may be good with mathematics at the expense of other topics:

No.	Course & Prerequisites	Resources
1	Introduction to Algebra
2	Introduction to Geometry
3	Introduction to Programming
4	Introduction to Cognitive Psychology	Cognitive Psychology: A Student’s Handbook by Michael W. Eysenck, Mark T. Keane
5	Fundamentals of Biology	Campbell Biology by Lisa A. Urry, Peter V. Minorsky, Kerry L. Hull, Rebecca B. Orr
6	Paradigms of Programming
	Strong: Introduction to Programming
7	Polynomials and Equations
	Strong: Introduction to Algebra
	Weak: Introduction to Geometry
8	Basic Statistics
	Strong: Introduction to Algebra
9	Introduction to Neuroscience	Neuroscience: Exploring the Brain by Mark Bear, Barry Connors, Michael A. Paradiso
	Strong: Fundamentals of Biology
	Weak: Introduction to Cognitive Psychology
10	Coordinate Geometry
	Strong: Introduction to Geometry
	Strong: Polynomials and Equations
	Weak: Introduction to Algebra
11	Philosophy of Mind
	Strong: Introduction to Cognitive Psychology
	Strong: Introduction to Neuroscience
12	Probability Theory
	Strong: Basic Statistics
	Strong: Introduction to Algebra
	Weak: Polynomials and Equations
13	Hypothesis Testing and Frequentist Methods
	Strong: Basic Statistics
	Strong: Probability Theory
14	Single-variable Calculus
	Strong: Polynomials and Equations
	Strong: Coordinate Geometry
	Weak: Introduction to Algebra
15	Practical Programming
	Strong: Introduction to Programming
	Strong: Paradigms of Programming
16	Philosophy of Science
	Weak: Introduction to Cognitive Psychology
	Weak: Basic Statistics
17	Multivariate Calculus
	Strong: Single-variable Calculus
	Weak: Coordinate Geometry
	Weak: Linear Algebra
18	Linear Algebra
	Strong: Introduction to Algebra
	Weak: Coordinate Geometry
	Weak: Single-variable Calculus
19	Discrete Structures
	Strong: Introduction to Algebra
	Strong: Introduction to Programming
20	Basic Experimental Methods
	Strong: Hypothesis Testing and Frequentist Methods
	Strong: Introduction to Cognitive Psychology
	Weak: Probability Theory
21	Bayesian Statistics
	Strong: Probability Theory
	Strong: Hypothesis Testing and Frequentist Methods
	Weak: Basic Experimental Methods
22	Data Structures
	Strong: Introduction to Programming
	Strong: Paradigms of Programming
	Weak: Discrete Structures
23	Machine Learning
	Strong: Probability Theory
	Strong: Linear Algebra
	Strong: Practical Programming
	Weak: Single-variable Calculus
	Weak: Data Structures
24	Databases
	Strong: Introduction to Programming
	Strong: Data Structures
	Weak: Practical Programming
25	Computational Modeling
	Strong: Machine Learning
	Strong: Introduction to Cognitive Psychology
	Strong: Introduction to Neuroscience
26	Embodied, Embedded, Enacted, Extended, Cognition	The New Science of the Mind by Mark Rowlands (book)
	Strong: Introduction to Cognitive Psychology
	Strong: Philosophy of Mind
	Weak: Introduction to Neuroscience
27	Neural Data Analysis	Analysis of Neural Data by Robert E. Kass, Uri T. Eden, Emery M. Brown (book); Analyzing Neural Time Series Data: Theory and Practice by Mike X Cohen (book)
	Strong: Introduction to Neuroscience
	Strong: Machine Learning
	Weak: Linear Algebra
	Weak: Machine Learning
	Weak: Computational Modeling
28	Causal Models and Explanations	Making Things Happen by James Woodward (book); Explaining the Brain by Carl F. Craver (book); Pseudo-mechanistic Explanations in Psychology and Cognitive Neuroscience (paper); Mechanistic Explanation in Psychology (paper); Cognitive science as an interface between rational and mechanistic explanation (paper)
	Strong: Philosophy of Science
	Strong: Basic Experimental Methods
	Strong: Computational Modeling
	Strong: Neural Data Analysis
	Strong: Embodied, Extended, Enacted Cognition
29	Non-Classical Logic	An Introduction to Non-Classical Logic by Graham Priest (book); Non-Axiomatic Logic by Pei Wang (book)
	Strong: Introduction to Algebra
	Weak: Philosophy of Science

A World without Climate Change is one that promotes Freedom

Thu, 23 Apr 2026 09:53:54 +0200

What would a life without climate change look like? In this world,

Carbon emissions would be down to the pre-industrial levels,
White reflective Arctic and Greenland ice cover would be intact,
AMOC would persist,
Forest covers would be up to their preindustrial level. The land used for farming would be much less.

As side effects,

Seasons would be predictable,
Loss of crops would be less due to unpredicted weather events,
People would need to endure less heat and less cold and less rain and less storms,
Healthcare system would be less affected by cumulative effects of climate change on health,
Less pressure on societal systems would render insurance easier.

But what would day to day life look like? Without fossil fuels, private yachts, flights, and even large scale ocean and atmospheric modes of commute are difficult to imagine. Even the manufacture of semiconductors depends on fossil fuels. Perhaps, many industries that have been historically dependent on fossil fuels including the printing press might have stagnated.

The question “What would the world look like without X?” can be interpreted in two ways. One, we can go back in time and intervene and then try to imagine what life would have been like. Two, one can imagine an intervention at the current moment and try to imagine what life would be like. The latter feels extremely disruptive until we already have a world with renewable energy powering every aspect of our lives. So to speak, this world where fossil fuels suddenly return to their grave and there is no energy to fuel our day to day activities is unimaginable. But even in the first case, where we go back in time and intervene, it is still unimaginable what life would be like without an equally-early discovery of renewable energy to power the printing, semiconductor and transport industry. In other words, this world too is unthinkable.

Imagining negation is tricky. One does not negate wills and desires and possibilities. The mind does not seem to like negations. Instead, one creates positive ones that serve as alternatives to the ones we do not want. Thus, to imagine what would a world without climate change look like, we should perhaps ask, what is the kind of world we want to look at.

Now, we have already broadened our thinking horizons a bit by travelling back 4-5 centuries in trying to imagine the alternative world where printing press did not take off due to a lack of energy. We can continue further and look into the far past to envision the possibilities for the far future. Is it useful to look this far? Aren’t things always transient and in a constant flux of change? Apparantly, that is not always the case! Rather, it seems that, for many things, there is a period of time in which things are more “flexible”, so to speak. But after a period of time, things become “rigid”. To give a few examples -

Around sixth century BC in China, there were several dozen distinct schools of philosophical and cultural experimentation. But later on, only four schools remained. Of these, only one thrived until the start of the 20th century and still continues to influence East Asian cultures.
Despite twenty (or fifteen) centuries, many cultures remain deeply entrenched in Abrahamic religions. Bible was the most sold book in 2025. And that’s ignoring the even higher number of copies that are simply distributed without being bought. Indeed, from AD 40 to AD 350, Christianity grew by about 43 percent per decade, from a population of about 1,000 to 34,000,000. A similar story holds for Islam and Quran.
Consider the English language itself as another example
Or the use of Javascript in browsers
The prominence of vegeterianism in India was apparantly not before two or three millenia (It’s difficult to find sources for these that are not just verbal anecdotes or chatgpt/claude generated conversations.)
Constitutions of countries, including long lived ones such as the United States

MacAskill, in his What we owe the future, calls this “lock-in”.. Initially, some state of affairs is rather flexible or mutable or easily changeable. But as time passes, these become harder and harder to change. The causal pathways by which this happens seems underdiscussed. (Hint: It’s often not merit that determines what becomes popular or what persists through time.)

With reference to the world we are looking for, the recommendation is this: We must strive against a lock-in of state of affairs that are harmful for the long run. At least until we find the most optimal state of affairs, and perhaps even after we do, we must allow a plurality of approaches. Let time do its weeding. Try to avoid weeding out ideas prematurely. The hard to digest implication of this is the tolerance of intolerance. I myself have a hard time accepting it. But even if you reject tolerating intolerance, the central idea remains: allow plurality and diversity, since they keep a civilization flexible.

Thus, an alternative to the current world with climate change is a world that values freedom.

Enter Amartya Sen’s Development as Freedom.

If the goal of life is to live and thrive, the best way to achieve that is by giving each individual the freedom to experiment and influence others in their own ways. Now, this is a claim at the population level. It makes no claim for individual choices: whether one should choose a life that “keeps their options open” or another that lets them “walk or run in peace with their chosen option even if it closes other options” is not something I have an answer to. The mere claim is we should work towards a society that allows individuals to choose differently at least in principle across different lives. Of course, we only have one life and we only live once, but that we can imagine a different life. If the society you live in permits that, that is a good society. In contrast, if merely imagining a different life seems impossible, that is not the kind of society you (or at least I) want to lock humanity in.

Caveat, I haven’t read Development as Freedom yet. So far, I touched some chapters of What we owe the future - particularly, 1, 2, 4. My own thoughts on the book are that it has some insights, but it seems to get carried away. Particularly, I disagree that a life choice that enhances humanity’s survival by 0.0001% as being better than another life choice that enhances the life of a million or even a few thousand. Because, by enhancing the life of a thousand others, especially in significant ways and endowing them with freedom, you raise the chances that someone (perhaps many) amongst these would go on to make choices that ultimately enhance humanity’s survival by not 0.0001% but by 10% even! And there are other critics.

For the more technical minded there, you cannot make optimal worldly interventions basing them on probability distributions formed out of observational data. For, the act of intervention changes the distribution and you will need to resample again! A potential alternative is to build a causal model of the world, but that seems an open topic for research!

Should I do a PhD?

Thu, 18 Dec 2025 09:08:16 +0530

Am I qualified to answer this question? I don’t know. I’m just a second-year PhD student at the moment. You be the judge.

What is a PhD? My understanding so far is it is a period of time (a few years) you spend under another academic (your supervisor)

learning how to be an academic in that particular field
by doing research work
that is in large parts independent as well as (ideally) collaborative
that other academics too deem worth the time (a few years)

Does PhD requires research? Yes.

Does research require PhD? No. Plenty of people throughout human history have made scientific and philosophical discoveries without doing a PhD. See List of Autodidacts and also Industry Research in your particular field.

Does PhD put you on a guaranteed path to be an academic? No. Consider that, annually, an academic institute might churn out 5 PhD-holders, but might create a new assistant-professor position once every 5 years. That’s 25 PhD-qualified candidates for 1 position. Even if you consider that new academic institutes pop up, the number of new academic positions still remain several times below new PhD-qualified candidates. (Also see this Nature article.) In other words, unless you are damn good and damn lucky even during your PhD, only then can you expect to get a good academic position. And let’s not forget that you have already been damn good and damn lucky just to qualify for your PhD program.

If other academics deem some research worth the time, that research must be good, right? No. What that merely implies is that that research is deemed worth the time (and money and resources) by other academics. Surely, it must at least correlate with good research?

What is good research? Surely, research on how to extract fossil fuels is good. For the purposes of extracting fossil fueis. Research on how to exploit greater productivity from employees is good. For the purpose of productivity-maximization. Research how to convert trees to better quality paper is good. For the purpose of paper-manufacturing. But neither of these seem good for the purpose of moving towards a sustainable equitable future. The point is goodness of research is extra-scientific. There is no scientific method to determine what qualifies as good research. To decide whether some research is good or not, you need to have a purpose in mind. Once you have some purpose, then research can be good or bad towards that purpose.

So far, that’s a pretty bleak picture of PhD. And it seems, one should not do a PhD at all! Not to mention PhD students are often underpaid, stressed, and prone to depression and anxiety more than a matched sample not doing their PhD. Okay, that makes the motivation for PhD even bleaker.

Why do a PhD at all then? Why do we do anything at all? Apparantly, because not doing that something incurs a greater perceived cost than doing it.

A PhD gets you

several years worth of time to read and think on your own
access to your supervisor, who you think is/was awesome
opportunity to attend conferences with like minded people
personal experience and behind the scenes happenings of academic publishing
an affiliation that often times lets you be taken seriously or opens up access to journals

Further, a good academic or PhD environment also gets you

access to the history of your field
access to not just your supervisor and conference attendees, but an entire department (= 24x7) with like minded people
opportunity to be an academic relative of an awesome family of researchers
a stipend that is less than awesome, but still enough to sustain a comfortable life

And if you think doing good science requires knowing the

history [of the field]
current happenings [of the field]
ways of the minds and limitations of the awesome people [in the field],

the PhD package - especially the one which is good - seem like a pretty good deal.

Indeed, the earlier point that goodness of science needs to be evaluated extra-scientifically still stands. You must have a larger purpose in mind. The like-mindedness and awesome-ness is towards that larger purpose. Assuming you have that larger purpose in mind, you are the best judge whether a particular PhD program is good for your purpose.

And now you are also aware of the limitations of academic research described above.

Beware, a bad or not-so-good PhD (or academic) environment often skips teaching you how to be an academic in a particular field. This is often implicit knowledge, and you ought to be a fair bit perceptive about how people and institutions work to figure it out. There are some books on this topic, but it is by and large knowledge that you will need to figure out in person through real-life interactions. Of course, you can consult your supervisor or other colleagues in the department or people you meet at the conferences. Such knowledge also gets described during late-night parties or social lunches at the labs or departments.

Random note: Scientific research is not a game (RPG) in which you unlock certain powers of the world by doing certain specific actions. There is no fixed set of actions that doing research involves. At best, it is like playing a very rich RPG without a manual, and where quests are unlocked or solved on the span of years, decades and generations of humans. (Such a rich-enough RPG at a smaller scale would be fun! Though real-world is often more fun (= more complex).)

Classrooms, Questions, and Learning in India vs Europe

Sat, 29 Nov 2025 20:44:15 +0100

If you were schooled up in India (or other Asian countries too), you’d have unconsciously caught up the habit of staying silent in class. You might stay silent because you do not know or understand something, or for the fear of being labelled dumb, or for wanting to avoid feeling ashamed. Or you might remain silent because you understand it and do not want to attract attention. Some can be attention seekers. Some grow out of attention seeking. Some start to despise the special treatment and they too grow up to be silent*. And there could be other reasons. A fair few would also be fortunate enough to be exposed to discussion-oriented classes during schooling itself. India has a culture that shares deep respect for teachers or gurus. Unfortunately, the respect is also often replaced by fear.

The median Indian student spends 10-12 years or more fearing teachers, or simply staying silent even without being fearful. Even if the student is now admitted to premier colleges in India, where many professors encourage discussions and class participation, it is hard to not remain silent. Conveniently progressing in such college requires participation. If you do not understand, you are expected to ask. The ones who ask, seek help and communicate, progress further than you. This continues through the rest of the undergraduate years. The ones fortunate enough to attend these premier undergraduate colleges, where professors encourage class participation and question asking, end up in a better position in terms of communication in graduate school life. But these students constitute single digit percentage of the student population.

The median Indian student, however, spends another 3-4 years in an undergraduate program learning to stay silent. Even when the student is admitted to the graduate programs of premier colleges, they end up having to spend half their stay trying to unlearn the stay-silent habit, if that is meaningfully-possible at this stage of life at all.

Over the past year, I have been fortunate to start my doctoral studies in Europe. One stark contrast compared to my graduate studies in India – which itself was at a premier institute – is the ease with which everyone asks questions. Many prefix their questions with “May be this is dumb (or silly)…” but they ask. They ask while the topic is still being discussed. But they ask. Ask they do. It doesn’t matter if you are a first year graduate student. Or a senior professor attending an emeritus’ professor’s lecture. Everyone asks questions. Indeed, people do modulate their questions in terms of whether they are clarifications, minor or serious objections, or tangential, but the question asking and discussion environment is prevalent.

Another factor that discourages question asking in Indian schools includes “homework” – The student is expected to ask questions only after putting in the “required” work. If one asks questions without putting in the “required” work, they may be shunned by the teacher. Even if not shunned upon, there is a serious culture that “mistakes are bad”, “you should never do mistakes”, “asking questions is shameful”. This carried itself into the graduate school environment I experienced in India. It is indeed true that the amount of readings in graduate school keep increasing. In courses by some professors, they are outright impossible. Some expect an exposure, some expect a thorough reading, others expect somewhere in between. However, contrast the lack of question-asking with my experience here: students are shameless about asking questions, they are fearless about admitting they haven’t done their readings, they are truthful in acknowledging their weaknesses. The professors too reply in kind – there’s no shunning of the student, there’s simply an answering that tries to clear the concept or point to the student without placing any judgment on the student.

There are further implications of this “don’t ask questions” attitude especially when it comes to interdisciplinary collaboration. Interdisciplinary collaboration requires you to collaborate with someone whose background you can only acquire after years if not decades of hard work. This is impractical. The only other way is to admit your ignorance. Be open to learning. Be open to asking. Be open to asking dumb questions. Be open to feeling confused. Similarly, you too need to be open to the other person asking questions that seem nonsensical or dumb to you. Be empathetic towards them. Be understanding towards them. Be answering towards them without shunning.

Acknowledge the situation, assume positive intentions, take a deep breath, and continue communication.

I do not have an easy fix to the problem. Yet this contrast in discussion and question asking is something I have witnessed firsthand. If you happen notice yourself feeling ashamed, fearful, or any other negative emotion in classroom settings, despite having access to humble professors who are ready to answer any question, hopefully, this post will help you become more mindful of it, and be more open to asking questions, admitting your ignorance and learning more.

I have indeed had the chance to witness great question askers and communicators, both in India and Europe. It isn’t beyond anyone. But it definitely requires an attitude shift in most of us. Happy asking! Happy Learning!

*Of course, why a particular student or individual is more silent than others has multiple context-dependent reasons. On a different note, why a class might remain silent also has another set of reasons.

Resume and CV Templates

Fri, 31 Oct 2025 13:00:08 +0100

A while ago, I had run up into an amazing reddit thread on resumes and CV template, suggestions and discussions. Here it is:

https://www.reddit.com/r/jobs/comments/7y8k6p/im_an_exrecruiter_for_some_of_the_top_companies/

It has pretty much kept being updated for the last… 8 years(!)

Advice for (Psychology) Graduate Students

Fri, 24 Oct 2025 08:45:32 +0200

I recently received an email from one of my professors titled Some non-obvious advice for psych graduate students. I wanted to share it publicly, but found no obvious public links in the email that was forwarded from one of Substack’s mailing list. Though, after inspecting the url for Varun Shennoy’s substack varunshenoy.substack.com this morning, the critical part seemed to be obtaining some username, and then the website should be at <username>.substack.com. And there it was paulbloom.substack.com that I could share publicly. Enough Meta!

If you are a psychology graduate student (or perhaps even a postdoctoral researcher or someone in their early career), definitely check it out!

Some non-obvious advice for psych graduate students

Another of Paul Bloom’s post I liked and can suggest reading is

The only interdisciplinary conversations worth having

To be honest, that’s a strong take. I think from an interdisciplinary research perspective, it still makes sense. However, I also think there’s more to interdisciplinary research. I should share an article I had found another day; it’s a tangential topic on philosophy of interdisciplinary research rather than advice for graduate school.

Coming back to graduate school advice, I had come across a book last year. I suspect it was this, but it could be different. It is slighly hefty at 400+ pages, but given that they are plain english rather than academic jargon, it should be an easy read over lunch hours or a weekend or two. Goodreads reviewers rate it quite highly.

A Field Guide to Grad School: Uncovering the Hidden Curriculum

Good luck!

Don't major in Computer Science!

Thu, 23 Oct 2025 23:09:57 +0200

From time to time, I regret majoring in computer science and not something heavier in mathematics. Today was one such day 🥲. It remains one of the biggest regrets of my life. Alas, what is done is done. Grass is always greener on the other side.

This post by Varun Shenoy sums it quite well. If you are young, and have an option, choose anything but computer science. And particularly, don’t go into computer science in order to learn programming. Programming is the bread and butter of 21st century, but this means it is a fundamental skill… like english. You don’t become excited just because you know english. You need other skills, english is just a medium of communication (with other english-speakers). You need other skills, programming is just a means of communication (with the computer).

Don’t also go into computer science in order to pursue higher studies in computer science. The research part of computer science can be fairly heavy in mathematics, so I’m biased to say that an applied-math major would be better. Of course, you can start an undergraduation in computer science and fill it up with mathematics heavy topics, but why not acquire a proper mathematics background in the first place then.