This was originally posted on my personal blog, I’m reblogging here as well, since this is basic cognitive psych
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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We’re working to document and share our computational modelling code on Open Science Framework. One of the most widely used model 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 1984, Minda & 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.
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.
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.
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.
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.