(please see the publications page for a complete list of papers)
One of our major lines of research deals with the recruitment of various cognitive processes in category learning and how these different modes of category learning can be dissociated. For example, adults, children, and monkeys show some surprising similarities in their ability to learn novel categories of perceptual objects. All three groups are able to easily learn categories that are defined by a single attribute (e.g. red things) or those that are defined by overall family resemblance (e.g. things are usually red, but not always). However, adults can also learn disjunctive categories easily. Monkeys cannot, and younger children are impaired relative to adults. We are currently conducting new research into the nature of these effects and working to refine our model of multiple systems of category learning.
Rabi R. R. & Minda J. P. (2014) Rule-Based Category Learning in Children: The Role of Age and Executive Functioning. PLoS ONE 9(1): e85316. doi:10.1371/journal.pone.0085316
Minda, J. P & Miles, S. J. (2010). The Inﬂuence of Verbal and Nonverbal Processing on Category Learning. In B.H. Ross (Ed.).The Psychology Of Learning and Motivation, Vol 52, pp. 117-162
Minda, J. P. Desroches, A, & Church, B. A. (2008). Learning rule-defined and non rule-defined categories: A comparison of children and adults. Journal of Experimental Psychology:Learning Memory & Cognition. 34,1518-1533.
Smith, J. D., Minda, J. P., & Washburn, D. A. (2004). Category learning in rhesus monkeys: A study of the Shepard, Hovland, and Jenkins tasks. Journal of Experimental Psychology: General, 133, 398-414.
Category Learning: Direct, Indirect, and the effects of motivation and mood
Humans typically acquire information about categories through a variety of means. In the lab this usually means classification training. But in real-world settings, categories are acquired through interacting with the objects, using them, and predicting things about the them. For example, when learning categories explicitly (classification) people may look for simple rules. But this tendency to look for rules is diminished when subjects learn categories indirectly. In addition, people learn about categories in a variety of different cognitive states. Accordingly, we have been examinind how mood and motivation affect the learning of categories.
Nadler, R. T., Rabi, R. R. & Minda J. P. (2010). Better mood and better performance: Learning rule-described categories is enhanced by positive mood. Psychological Science.
Minda, J. P., & Ross, B. H. (2004) Learning categories by making predictions: Comparing direct and indirect processes of category learning, Memory & Cognition, 32, 1355-1368.
Thinking and Reasoning in Medicine
Medical doctors are called upon to make many kinds of decisions about their patients. For example, a doctor might need to make a complicated differential diagnosis and may also need to decide what kinds of tests are required to rule out one or more of those diagnoses. This diagnostic reasoning process has received substantial attention in the literature but diagnosis is only one aspect of the typical doctor-patient relationship. A project with Dr. Minda and Dr.’s Goldzsmidt from Schulich School of Medicine & Dentistry investigates how doctors make decisions about patient management.
Goldszmidt, M., Minda, J.P., & Bordage, G (2013). Developing a unified list of physicians’ reasoning tasks during clinical encounters. Academic Medicine, 88, 1-3.
Devantier, S. L., Minda, J. P., Goldszmidt, M. & Haddara, W. (2009). Categorizing Patients in a forced-choice triad task: The integration of context in patient management, PLoS ONE 4, e5881.
Our lab investigates and evaluates several different computational models of category learning. For example, prototype models operate on the assumption that categories are represented in the mind as a single, abstract representations. Exemplar models assume that categories are represented by collections of similar memory traces.
Minda, J. P. & Smith, J. D. (2011). Prototype models of categorization: basic formulation, predictions, and limitations. In E. Pothos & A. Wills (Eds.) Formal Approaches in Categorization. Cambridge University Press: Cambridge, UK.
Harris, H. D. & Minda, J. P. (2006). An attention-based model of learning a function and a category in parallel. In The Proceedings of the 28th Annual Meeting of the Cognitive Science Society, Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
Minda, J. P. & Smith, J. D. (2001). Prototypes in category learning: The effects of category size, category structure, and stimulus complexity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 775–799.