Graduate school advice

Since it is the season for graduate school recruitment interviews, I thought I would share some of my thoughts. This is also partly prompted by two recent articles in the journal Neuron. If you're unfamiliar with it, Neuron is a very high-profile neuroscience journal, so the advice is aimed at graduate students in neuroscience, though I think the advice broadly applies to students in the cognitive sciences (and perhaps other sciences as well). The first of these articles deals with what makes a good graduate mentor and how to pick a graduate advisor; the second article has some good advice on how to be a good graduate advisee.

I broadly agree with the advice in those articles and here are a few things I would add:

Language in developmental and acquired disorders

As I mentioned in an earlier post, last June I had the great pleasure and honor of participating in a discussion meeting on Language in Developmental and Acquired Disorders hosted by the Royal Society and organized by Dorothy Bishop, Kate Nation, and Karalyn Patterson. Among the many wonderful things about this meeting was that it brought together people who study similar kinds of language deficit issues but in very different populations -- children with developmental language deficits such as dyslexia and older adults with acquired language deficits such as aphasia. Today, the special issue of Philosophical Transactions of the Royal Society B: Biological Sciences containing articles written by the meeting's speakers was published online (Table of Contents).

Does Malcolm Gladwell write science or science fiction?

Malcolm Gladwell is great at writing anecdotes, but he dangerously masquerades these as science. Case studies can be incredibly informative -- they form the historical foundation of cognitive neuroscience and continue to be an important part of cutting-edge research. But there is an important distinction between science, which relies on structured data collection and analysis, and anecdotes, which rely on an entertaining narrative structure. His claim that dyslexia might be a "desireable difficulty" is maybe the most egregious example of this. Mark Seidenberg, who is a leading scientist studying dyslexia and an active advocate, has written an excellent commentary about Gladwell's misrepresentation of dyslexia. The short version is that dyslexia is a serious problem that, for the vast vast majority of people, leads to various negative outcomes. The existence of a few super-successful self-identified dyslexics may be encouraging, maybe even inspirational, but it absolutely cannot be taken to mean that dyslexia might be good for you.

In various responses to his critics, Gladwell has basically said that people who know enough about the topic to recognize that (some of) his conclusions are wrong, shouldn't be reading his books ("If my books appear to a reader to be oversimplified, then you shouldn't read them: you're not the audience!"). This is extremely dangerous: readers who don't know about dyslexia, about its prevalence or about its outcomes, would be led to the false conclusion that dyslexia is good for you. The problem is not that his books are oversimplified; the problem is that his conclusions are (sometimes) wrong because they are based on a few convenient anecdotes that do not represent the general pattern.

Another line of defense is that Gladwell's books are only meant to raise interesting ideas and stimulate new ways of thinking in a wide audience, not to be a scholarly summary of the research. Writing about science in a broadly accessible way is a perfectly good goal -- my own interest in cognitive neuroscience was partly inspired by the popular science writing of people like Oliver Sacks and V.S. Ramachandran. The problem is when the author rejects scientific accuracy in favor of just talking about "interesting ideas". Neal Stephenson once said that what makes a book "science fiction" is that it is fundamentally about ideas. It is great to propose new ideas and explore what they might mean. But if we follow that logic, then Malcolm Gladwell is not a science writer, he is a science fiction writer.

The mind is not a (digital) computer

The "mind as computer" has been a dominant and powerful metaphor in cognitive science at least since the middle of the 20th century. Throughout this time, many of us have chafed against this metaphor because it has a tendency to be taken too literally. Framing mental and neural processes in terms of computation or information processing can be extremely useful, but this approach can turn into the extremely misleading notion that our minds work kind of like our desktop or laptop computers. There are two particular notions that have continued to hold sway despite mountains of evidence against them and I think their perseverance might be, at least in part, due to the computer analogy.

The first is modularity or autonomy: the idea that the mind/brain is made up of (semi-)independent components. Decades of research on interactive processing (including my own) and emergence have shown that this is not the case (e.g., McClelland, Mirman, & Holt, 2006; McClelland, 2010; Dixon, Holden, Mirman, & Stephen, 2012), but components remain a key part of the default description of cognitive systems, perhaps with some caveat that these components interact.

The second is the idea that the mind engages in symbolic or rule-based computation, much like the if-then procedures that form the core of computer programs. This idea is widely associated with the popular science writing of Steven Pinker and is a central feature of classic models of cognition, such as ACT-R. In a new paper just published in the journal Cognition, Gary Lupyan reports 13 experiments showing just how bad human minds are at executing simple rule-based algorithms (full disclosure: Gary and I are friends and have collaborated on a few projects). In particular, he tested parity judgments (is a number odd or even?), triangle judgments (is a figure a triangle?), and grandmother judgments (is a person a grandmother?). Each of these is a simple, rule-based judgment, and the participants knew the rule (last digit is even; polygon with three sides; has at least one grandchild), but they were nevertheless biased by typicality: numbers with more even digits were judged to be more even, equilateral triangles were judged to be more triangular, and older women with more grandchildren were judged to be more grandmotherly. A variety of control conditions and experiments ruled out various alternative explanations of these results. The bottom line is that, as he puts it, "human algorithms, unlike conventional computer algorithms, only approximate rule-based classification and never fully abstract from the specifics of the input."

It's probably too much to hope that this paper will end the misuse of the computer metaphor, but I think it will be a nice reminder of the limitations of this metaphor.

Dixon JA, Holden JG, Mirman D, & Stephen DG (2012). Multifractal dynamics in the emergence of cognitive structure. Topics in Cognitive Science, 4 (1), 51-62 PMID: 22253177
Lupyan, G. (2013). The difficulties of executing simple algorithms: Why brains make mistakes computers don’t. Cognition, 129(3), 615-636. DOI: 10.1016/j.cognition.2013.08.015
McClelland, J.L. (2010). Emergence in Cognitive Science. Topics in Cognitive Science, 2 (4), 751-770 DOI: 10.1111/j.1756-8765.2010.01116.x
McClelland JL, Mirman D, & Holt LL (2006). Are there interactive processes in speech perception? Trends in Cognitive Sciences, 10 (8), 363-369 PMID: 16843037

Graduate student positions available at Drexel University

The Applied Cognitive and Brain Sciences (ACBS) program at Drexel University invites applications for Ph.D. students to begin in the Fall of 2014. Faculty research interests in the ACBS program span the full range from basic to applied science in Cognitive Psychology, Cognitive Neuroscience, and Cognitive Engineering, with particular faculty expertise in computational modeling and electrophysiology. Accepted students will work closely with their mentor in a research-focused setting, housed in a newly-renovated, state-of-the-art facility featuring spacious graduate student offices and collaborative workspaces. Graduate students will also have the opportunity to collaborate with faculty in Clinical Psychology, the School of Biomedical Engineering and Health Sciences, the College of Computing and Informatics, the College of Engineering, the School of Medicine, and the University's new Expressive and Creative Interaction Technologies (ExCITe) Center.

Specific faculty members seeking graduate students, and possible research topics after the jump.

New version of lme4

If you haven't realized it yet, a new version of lme4 (version 1.0-4) was released recently (Sept. 21). For an end-user like me, there were not many changes, but there were a few:

1. No more using the @ operator. After a very helpful email exchange with Ben Bolker, I came to realize that I shouldn't have been using it in the first place, but I hadn't figured out all the "accessor" methods that are available (you can get a list using methods(class = "merMod")). I had been using it in two main contexts:
1. To get the fixed effect coefficients with their standard errors, etc. from the summary. A better way to do that is to use coef(summary(m))
2. To get model-predicted values. A better way to do that is to use fitted(m), with the added convenience that this returns proportions for logistic models, making the model fits easier (and, I think, more intuitive) to visualize. By the way, a predict() method has now been implemented, which provides an easy way to get model predictions for new data.
2. There have been some changes to the optimization algorithms and some of my models that used to run fine are now giving me some convergence warnings. This seems to happen particularly for linear models with within-subject manipulations. Using the bobyqa optimizer instead of the default Nelder-Mead optimizer seems to fix the problem. This can be done by adding  control=lmerControl(optimizer = "bobyqa") to the call to lmer. A minor related point: the release notes (https://github.com/lme4/lme4/blob/master/misc/notes/release_notes.md) state that the internal computational machinery has changed, so the results will not be numerically identical, though they should be very close for reasonably well-defined fits. I have found this to be true for a reasonably large set of models that I've re-run.
3. When fitting logistic models, if you use lmer(..., family="binomial"), it will call  glmer() as before, but now also warns you that you should probably be using  glmer() directly.

Models are experiments

I spent last week at a two-part meeting on language in developmental and acquired disorders, hosted by the Royal Society. The organizers (Dorothy Bishop, Kate Nation, and Karalyn Patterson) devised a meeting structure that stimulated – and made room for – a lot of discussion and one of the major discussion topics throughout the meeting was computational modeling. A major highlight for me was David Plaut’s aphorism “Models are experiments”. The idea is that models are sometimes taken to be the theory, but they are better thought of as experiments designed to test the theory. In other words, just as a theory predicts some behavioral phenomena, it also predicts that a model implementation of that theory should exhibit those phenomena. This point of view has several important, and I think useful, consequences.