Author Archives: John Paul Minda

About John Paul Minda

Professor of Psychology at Western University. I mostly write about cognitive psychology, science, & higher ed. Sometimes running, food, & cats.

2022 Wrap Up!

As 2022 draws to a close, I was inspired by all other 2022 wrap ups I’ve been seeing—like Spotify’s wrapped, Strava, Apple music—and so I decided to write a Minda Lab 2022 Wrap Up. We had a fantastic and productive year. Everyone in the lab (undergraduates, MSc / PhD students, postdocs, and me) has things that we’ve accomplished and can be proud of. Let’s take a look!

2022-2023 lab photo (left) and the 2021-2022 (right) lab photo

Major milestones in 2022

This was a big category, with many students defending, completing, graduating, and beginning new things. If you want to read a thesis or dissertation, just follow the link.

Research in 2022

We published the following papers in 2022:

  • Ruiz Pardo, A. C., & Minda, J. P. (2022). Reexamining the “Brain Drain” Effect: Does Ward et al. (2022) Replicate? Acta Psychologica, 230, 103717.
  • Pavlović, T., Azevedo, F., De, K., RiañoMoreno, J. C., Minda, J. P., … Van Bavel, J. J. (2022). Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning. PNAS Nexus, 1(3), gac093.
  • Qiu, T. T. & Minda, J. P. (2022). Psychedelic experiences and mindfulness are associated with improved wellbeing, The Journal of Psychoactive Drugs,
  • Van Bavel, J.J., Cichocka, A., Capraro, V. Minda, J. P., et al. (2022) National identity predicts public health support during a global pandemic. Nature Communications 13, 517.
  • Azevedo, F., Pavlović, T., Rêgo, G. G. d., Ay, F. C., Gjoneska, B., Etienne, T., Minda, J. P., … Sampaio, W. M. (2022). Social and moral psychology of COVID-19 across 69 countries. Nature Scientific Data, in press

Priya Kalra (postdoc) published the following:

A paper from Julia Ignaszewski’s time as an undergraduate was published this year:

And a second publication of my book!

My book “How To Think: Understanding the Way We Decide, Remember and Make Sense of the World” which had been published in the UK in 2021 was published in North America in 2022 and started appearing in bookstores and libraries everywhere (still time to order for xmas!)


Minda lab members presented at several conferences in 2022 (Toronto, Halifax, Boston). In fact, think this year was the first time we had been to conferences in person since 2019!

  • Kalra, P. B. (2022). Perceptual Similarity Affects Relational Judgements. Proceedings of the Annual Meeting of the Cognitive Science Society, 44(44).
  • Khemani, N., Ruiz Pardo, A. C, & Minda, J. P (2022). Culture and Category Learning: The Relationship between Analytic and Holistic Thinking Styles. Poster presented at the 44th Annual Conference of the Cognitive Science Society. Toronto, ON.
  • Cruz, A. & Minda, J. P (2022) Sorry, I Can’t Talk Right Now: Category Learning on the Go. Talk presented at the 63rd. Annual Meeting of the Psychonomic Society, Boston, MA.
  • Kalra, P. & Minda, J. P (2022) Implicitly Learned Information Affects Explicitly Learned Category Judgements. Talk presented at the 63rd. Annual Meeting of the Psychonomic Society, Boston, MA.
  • Minda, J. P. (July 2022). Six Different Ways to Carve Nature at its Joints, Talk presented at the 32nd Annual Meeting of the Canadian Society for Brain, Behaviour, and Cognitive Sciences, Halifax, NS.

In addition, we have some exciting new research that has progressed significantly in 2022.

  • Bailey Brashears is moving ahead with our investigation into cognitive psychology in Minecraft.
  • Julia Ignaszewski is looking at mood induction with virtual reality.
  • Tim Qiu has just shown us some exciting new data with fNIRS and category learning.


Some of our member’s have won awards to be rightly proud of.

Other fun things

There were a few other things going on too:

Or check out the video below:

  • Several Minda Lab members (Priya, Ana, Chelsea) along with their partners and another lab on campus participated in the FoamFest 5k as the “Big Brain Energy” team. This was organized by Priya and the team had a great time at Boler Mountain!
  • Julia organized the Brain and Mind Art Social which has now become a regular event with students and trainees from many of the labs in the cognitive neuroscience group participating.

So that’s a wrap. I may have forgotten something (lord knows I forget more things in a day than I can even remember) but I wanted to give you all a huge shoutout for another year of great work, great science, and great times!

Good News Updates from our lab

Like just about every other institution, our research institute, the Brain and Mind Institute at Western has been on “stay at home” orders. We’ve all been working at home since March 14th, teaching, doing research, and trying to stay well.

All the labs in the institute have been sending around brief, “good news” email newsletters to the larger group of professors, PIs, postdocs, and grad students, with an emphasis on the fun things we’re doing. We may not see each other in person, but it’s a great way to stay in touch. This week was our lab’s turn and we decided to expand the updates and share them on our blog.

So what have we been up to?

Lab Work

As a lab, we’ve been meeting up several times a week on Zoom for writing support. These are really informal meetups, usually in the mornings. We talk about what we’re working on, then we turn off the mics and sometimes the cameras, and we just write or work on code. After about 90 minutes, we check in and talk about what we wrote, ideas, we had, any writing challenges, etc. Mostly, Zoom is just there in the background while we keep each other company and write. We also do more structured lab meetings and we’re going to star up our journal club too. It’s been a good way to stay cohesive when we’re sheltering in place

OK, Here are some individual updates from our lab:

Emily Nielsen

Emily is the senior PhD student in the lab. She has been keeping busy finishing up her dissertation, with a plan to defend this summer. She’s also been spending some time exploring the city and taking care of her plants. The neighbour’s cats have been stopping by periodically to check in on her and she’s had some nice visits with some other furry friends as well. Last week, Emily cooked meat for the first time ever and, not only did everyone survive(!), but it was actually quite tasty. She also recently learned that a couple she’s been friends with from undergrad is expecting their first baby, which she’s very excited about!

Picture from Emily, including her indoor plants, outdoor walks, and animal friends

Josh Hatherley

Josh is a third year PhD Student in psychology and Josh is keeping busy spending a lot of his time looking out his window with his cat Sim and Sim’s friend Olive. He can be found gardening in the backyard, jogging around the neighborhood, blacksmithing in his shed, or contributing to the internet’s ongoing baking renaissance with his collection of sourdough bread pictures on Instagram.

Josh and Kate and their cats, breads, and gardens

Ana Ruiz Pardo

Ana a third year PhD student in Psychology, has been joining in on the #COVIDbaking and #COVIDcooking trend. Ana and her boyfriend Rex have been making something baked about once a week and Ana tries to have a steady supply of homemade dumplings for when they get a craving. Ana has also started painting again to help her fill weekends with something outside of work or binge watching TV. Ana is helping two former honours students (Kira Forman-Tran and Elyse Cater) apply for The Undergraduate Awards, which is an international competition that Western has competed in for the past few years.

Ana’s baking, dumplings, and paintings

Toka Zhu

Toka is a second year PhD Student. Toka has been working on a manuscript revision to CJEP and working on completing her comprehensive exams. She’s been following Yoga with Tim 30 day challenge and is now on day 25. Toka misses the gym (and Zumba) greatly and is planning to write a travel memoir to cheer herself up with the memories she had in Sydney, Australia last summer. 

Bailey Brashears

Bailey is a first year PhD student in the lab. In addition to working on studies to be run online, Bailey has been spending time baking with her partner, Victoria, and keeping up with friends through Animal Crossing and Splatoon. She is thankful for the company of their two cats, Potato and Punchy, and their blue-tongue skink, Radish. Bailey also just found out that her submission to the Cognitive Science Society Conference was accepted as a talk. Cog Sci would have been in Toronto this year, but it’s going online, so we’ll be working on that this summer. We’ll post a preprint of the paper soon.

Bailey’s cats and focaccia

Tim Qiu

Tim is an NSERC USRA student has been working from his home in Guelph. Tim was going to study brain activation during mindfulness meditation and mind wandering states using fNIRS, but that’s not possible right now. But we’ve worked out a new project that we can carry out remotely. He’s currently re-evaluating his relationship with technology, spending more time just sitting outdoors like an old man. When he’s not doing that, he is consuming an inordinate amount of tea. 

Chelsea McKenzie

Chelsea is a research assistant in the lab, has completed her third year at Brescia. She’s been keeping busy by staying active (i.e., practicing yoga, walking the dogs, jogging around London, and using the elliptical at home). Chelsea is grateful to be at home with her two dogs, Bella and Jules, and for having more time to spend in the garden. 

Chelsea’s dogs and tomatoes waiting to be planted

John Paul Minda

Finally, from lab director John Paul Minda (that’s me): Since sheltering in place with Beth, my two daughters (one home from university and one doing Grade 10 at home), and my cat Peppermint, I’ve been running more, cooking more, eating more, tweeting more, and writing more. I’ve also learned to sew masks and we’ve been playing more board games. The second edition for my Psychology of Thinking textbook is finished and will be published in the fall, and my non-fiction book with Little, Brown and Co should be completed this summer and will be published in 2021. 

That’s it from our lab for now. We’ll update later in the summer with some research progress and someday we’ll be back in the lab.

Stay well and stay safe!

Cognitive Interference and Category Learning: A Tale of Two Systems

Joshua Hatherley, a PhD student in the Minda Cognitive Science lab, is presenting some research from his dissertation as a poster at the 2019 Psychonomics Meeting in Montreal. The poster presentation is scheduled for Friday, November 15, 2019 from 12:00 PM – 1:30 PM in Room 517B. You can get a copy of the poster link here.

The Experiment

Josh investigated the relationship between working memory, procedural memory, and category learning. The COVIS model assumes that category learning is regulated by an explicit system which uses working memory and an implicit system which uses procedural memory. Research by Minda et al. (2008) used a co-articulation task to disrupt disjunctive-rule category learning and Miles and Minda (2011) used a switching task to disrupt rule-based category learning. However, Minda et al. (2008) failed to find a link between performance on single-dimensional category sets and working memory. Miles and Minda (2011) used a demanding task that interfered with verbal working memory, visual working memory, and executive functions but did not specify if one or all of these processes caused the disruption. The present study addressed these limitations by asking people to learn either a rule-defined (RD) or a non-rule-defined (Information Integration) category set (see Figure 1) composed of 80 Gabor patch  images, 40 in each category, that varied in either the orientation of the image, or the frequency of the lines in the image.

While completing this primary categorization task, participants were asked to also perform one of three concurrent tasks that interfered with either verbal working memory, procedural memory, or none of the processes (see Figure 2).  In the first concurrent task, participants only learned the category set. This was done in order to be able to compare concurrent task performance to some kind of baseline. In the second concurrent task, participants were asked to speak aloud letters as they appear directly underneath the categorization stimuli. As reading and speaking a list of letters as they appear on the screen should have used a verbal working memory system, it was expected that this additional task would divert resources away from working memory and interfere with learning rule-defined category sets. In the third concurrent task, participants were assigned to complete a motor concurrent task. In it, participants were asked to tap the table with their non-dominant hand whenever they see a colon appear on the screen. As motor function is thought to be a process of procedural memory, adding the additional burden of performing a tapping task should slow down participants ability to learn non-rule-defined categories.


Results indicated that both the verbal working memory task and the procedural memory task impaired the learning of rule-defined categories but had no effect on the learning of information integration categories (see Figure 3).

Information integration category learning did not seem to be affected by either the verbal or the tapping concurrent task. Although we did not  predict an effect of the verbal concurrent task, we did predict an effect of the tapping task. We also see that rule-defined category learning appears to be slowed by both the verbal concurrent task and the tapping concurrent task. While we did expect to see an impact from the verbal concurrent task on rule-defined category learning, we did not expect to see it from the tapping concurrent task – which was thought to only impact procedural memory. 

What do these results mean? For one thing, learning to perform perceptual classifications is difficult. It takes practice. But people can learn to do it fairly well, even for stimuli like these that are hard to describe. But these results also show that some classifications, the rule-defined ones,  are made even more difficult when people are asked to do two things at once. We think that the cognitive resources that are needed to speak the letters or to switch between tasks are also being used for learning these categories. Other classification, the information-integration categories, do not seem to suffer from the same kind of interference. Do these results suggest that there are two cognitive systems that learn new categories? Perhaps. The best way to know for sure is to look more closely at some of the cognitive and neural substrates involved in learning. That’s what our lab is planning to do next. 

This work was supported by NSERC, Western University, and BrainsCAN.

For full analysis, see our rpubs page.

People Are More Likely to Use Classification Rules When Features Are Easy to Describe Verbally

Bailey Brashears, a PhD in the Minda Cognitive Science lab, is presenting some research from her Master’s thesis as a poster at the 2019 Psychonomics Meeting in Montreal. The poster presentation is scheduled for the Friday Nov 15 poster session at noon. You can get a copy of the poster here

The Experiment

Bailey investigated the effects of feature verbalizablity on the acquisition of novel concepts. Participants in the experiment learned a category set that could be acquired by either a perfectly predictive criterial attribute rule or an overall family-resemblance strategy. Half of the participants learned this set with features that were easy to name and describe verbally and the other half learned this set with features that were not easy to name and describe verbally. In this case, easy to name meant features that corresponded to focal colours, nameable shapes, or countable shapes. Features that were not easy to name were equally diagnostic of category membership, but did not correspond to focal colours or nameable shapes.  

Some examples of the stimuli we used. Each pair shown are opposite prototypes


After learning, participants were transferred to a set of new exemplars that included stimuli designed to distinguish between rule strategies and family-resemblance strategies. We found that it took participants about the same number of trials to learn the category sets regardless of the feature type learned. However, participants’ accuracy on previous items in the testing phase was higher for participants who learned the stimulus with easy to verbalize features; while participants were able to learn the categories in either condition, they retained these categories with better accuracy when the features were easy to verbalize. 

The majority of the subjects in the easily verbalizable condition were fit best by the criterial attribute (CA) rule model.

We also analyzed each learner’s performance with a set of computational models. Each model in the set assumed that performance was driven by one (and only one) of the available strategies (a rule, family resemblance, guessing, etc) and we examined which of the model best fit the observed performances. We found that people who learned the categories with easier to name features were more likely to classify new stimuli in accordance with a rule-based strategy. People who learned the categories with difficult to name features showed evidence of both rule use and family resemblance responding and no clear preference for either strategy. The figure above shows the models’ fit (essentially how close the model is to the observed data) for each subject by condition.


What do these results mean? For one thing, these results suggest that when people can rely on language to label features and to name things, they do. The more available the names are, the more likely people are to use rules that correspond to those features. This points to the primacy of language as a means to consolidate information. This language-based rule system might be a default way to acquire new concepts. It also means that there are other ways to learn concepts beyond using language, however. People who learned the categories that had features that were hard to describe verbally often learned the overall family resemblance, suggesting a tendency to learn exemplars. 

Bailey is working on a second study to replicate these results and plans to develop other ways to explore the role of language in acquiring new concepts. As well, she’s working on designing gaming environments that can explore the incidental acquisition of concepts. Stay tuned for more!

This work was supported by NSERC, Western University, and BrainsCAN.

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

John Paul Minda, PhD

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…

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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.

Recent PhD Graduates

Congratulations to two recent PhDs from the Categorization lab. Rachel Rabi defended her dissertation August of 2016 and is now working as a postdoc at the Rotman Institute in Toronto. Rachel’s doctoral work investigated category learning in older adults. You can read some of her work on her Research Gate profile and her doctoral dissertation is available here.

Karen Zhang completed her dissertation in November of 2016 and she is now a clinical intern at St Joseph’s Hospital in Hamilton Ontario. Karen’s work was on patient learning and understanding. You can read her dissertation here, and peer reviewed publications are forthcoming.