Measuring EEG during a category learning task

Humans learn to classify and categorize things all the time. It’s a fundamental aspect of cognition. Sometimes, categories can be learned in more than one way and there may be some things which seem to be exceptions to the classification rule. How does the brain react to individual stimuli during the learning process and in particular, how does the brain deal with learning these exceptions?

A new paper from our lab, published at Cognitive, Affective, and Behavioral Neuroscience, tries to answer this question. This work was carried out by a former lab member and PhD student, Rachel Rabi (who is now Banting postdoc working with Nicole Anderson and Lyne Hasher at the Rotman Research Institute in Toronto) along with Marc Joanisse, and lab members Toka Zhu and Paul Minda.

Learning a Conjunctive Rule

In the study, participants CJB1_28learned to classify shapes known as Gabor Patches (seen to the right), which are fairly common in visual perception research. Two features varied on these stimuli: the orientation of the alternating light and dark bands and the spatial frequency of the light and dark bands. We created a whole set of these and the figure below shows how these stimuli were arranged according to variance along the Orientation and Frequency dimensions. The optimal categorization strategy for this set was the conjunctive rule that combined frequency and orientation. However, it was also possible to learn this category less than perfectly with one of two sub-optimal, single-dimensional rules that were easier to acquire. In the example below, if a person only used frequency, it would allow them to classify all the B items, along with the items in the A1 and A3 regions, but they would misclassify the items in the A2 region because just using frequency does not distinguish between those two. In this case, participant would make errors on those stimuli until they transitioned to the optimal rule.


Our goal was to understand the cognitive processes behind the transition from a simple, suboptimal rule to a more complex optimal rule. We reasoned that this mirrors many real-world scenarios in which people learn a quick and easy rule that’s usually correct but with time and practice can use a more complicated rule that’s almost always correct.

Measuring EEG

Participants learned to classify the items in the set by seeing a single visual stimulus on a computer screen, making a decision as to whether it belonged in one category or the other (A or B), and then receiving feedback on whether or not they made the right decision. This feedback-driven learning went on for several hundred trials. During the learning phase, we also measured electrical activity in the brain using EEG. The EEG wave forms allowed us to see what was going in the brain at the moment that the participants saw the stimulus, made a decision, and received feedback.

We tested several hypotheses, and one hypothesis was that even after people learned to classify all the items correctly and had learned the conjunctive rule, we should be able to detect a difference between the stimuli that were able to be classified with only the simple rule (“easy” items) and those that could only be classified with the full, conjunctive rule (“difficult” items). We predicted that we’d find a difference in the EEG wave forms between these items, even though the behavioural response was the same.

Processing of “Exceptions”

We used a series of computational models to determine which strategy each person was using (details in the paper) and, as predicted, we found that people transitioned from a single-feature rule to the conjunctive rule. Then we looked at the EEG wave forms for participants who were using that conjunctive rule and making correct decisions. What we found as that even though they classified all of the stimuli correctly, there was a difference in brain activity between the “easy” stimuli and the “hard” stimuli. The difference can be seem below and it shows higher amplitude for the difficult stimuli, roughly 400-600 msec after as image is seen. We interpreted this difference as evidence for the additional memory processing that was happening when participants saw and classified the difficult stimuli.

It seems like even though you can make a correct decision on all of these items, your brain reacts differently to the difficult stimuli. Your brain seems to know that these items were, for a time, exceptions to a simple rule.

Untitled 2

We’re working on some new projects to understand more about how the brain reacts when learning about categories that have exceptional items. In addition, this was our first paper that combined the modelling work (which we’ve done a lot of) with EEG ( which we only done a little of). We plan to keep working with this technique. The modelling tells us what strategy participants most likely used and the EEG tells us how the brain was processed these stimuli. Together, it gives us a more complete picture of something that most of us just take for granted: making a quick classification decisions.

Find out More

You can see more about this work by reading the paper at the journal or the preprint from the preprint server.  If you really want to dig unto what we’be done, you can see all of the stimuli that we used, the individual data from out study and the code for our analyses and computation models at my Open Science Framework Page.


A Curated Reading List

This was originally posted on my personal blog, I’m reblogging here as well, since this is basic cognitive psych

The Modern Scientist

Fact: I do not read enough of the literature any more. I don’t really read anything. I read manuscripts that I am reviewing, but that’s not really sufficient to stay abreast of the field. I assign readings for classes, to grad students, and trainees and we may discuss current trends. This is great for lab, but for me the effect is something like me saying to my lab “read this and tell me what happened”. And I read twitter.

But I always have a list of things I want to read. What better way to work through these papers than to blog about them, right?

So this the first instalment of “Paul’s Curated Reading List”. I’m going to focus on cognitive science approaches to categorization and classification behaviour. That is my primary field, and the one I most want to stay abreast of. In each instalment, I’ll pick a…

View original post 1,262 more words

The Exemplar Model

We’re working to document and share our computational modelling code on Open Science Framework. One of the most widely used models is Rob Nosofsky’s Exemplar model known as the Generalized Context Model. This project can be found here, and is the result of Amanda Lien’s NSERC summer project in 2017. (Fun aside: I programmed a version of this model in 1998 using Turbo Pascal 7.0, later updated to REALBasic/Xojo, then a slow R script with loops, and finally with A. Lien’s help, an R version with matrices that minimizes running time. The underlying math is the same.)

This notebook describes the formulation of an exemplar model of categorization, the Generalized Context Model. Full specification of the model itself can be found elsewhere (Nosofsky 1984Minda & Smith 2001) This model has been used in cognitive psychological research to make predictions about how participants will learn to classify objects and belonging to one or more category. The primary assumption of the exemplar model is that categories are represented in the mind by stored exemplar traces, rather that rules or prototypes.

The document describes the development and use of an R script that reads in a text file of classification probabilities (usually obtained from behavioural testing), a text file that corresponds to the stimuli in the experiment, and a text file that corresponds to the exemplars of each category. The model then uses a hill-climbing algorithm to adjust the parameters and minimize the fitting error. The model will report the best-fitting parameters, the fit index, and the prediction of the model.


Introduction to Mindfulness Meditation

I gave a Lunch and Learn presentation to the staff at Western Research Development Services and Ethics in March 2018. The purpose of the talk was to introduce the concept of mindfulness, to run a brief meditation exercise, and also to talk about research in mindfulness (including my own) and to spend a bit of time talking about how members of Western’s research staff were instrumental in being able to help my lab with funding development. The slides from this talk are available here.

Canadian Scientists Organizing Boycott Against US Conferences

An article posted on Motherboard discusses the boycott of several Canadian scientists against attending US conferences as a form of protest against the Trump administration’s ban on travel from seven Muslim-majority countries. Western’s own neuroscientist Dr. Owen has offered to compensate the cancellation fees of others from Western’s Brain and Mind Institute and pay for international scientists to come to the university to present their research. Dr. Minda and the Categorization Lab are also considering joining the boycott, but a final decision has not yet been made.

Explaining the causal links between illness management and symptom reduction

Exciting new research published in Patient Education and Counseling was led by Karen Zhang, a recent grad from the Categorization Lab. Zhang and her fellow researchers revealed that providing explanation for why illness management is effective for reducing symptomatology can help improve the knowledge and application of health information for younger individuals. In contrast, reducing verbal demands of patient education material may help older adults learn new health information better.