Academia on digikar's microblog

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

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.