Our work is focused on understanding how we develop knowledge through experience and learning, how our knowledge representations influence how we perceive and interpret new information, how our interpretations and perceptions influence our behaviour, and what our behaviour tells us about our knowledge. With these aims in mind, we use a range of empirical and computational techniques to define and test our theoretical ideas.
Specific research areas include:
Logical rule-based models of categorization response time
A classic idea in the study of concept learning is that people learn and represent certain kinds of categories by forming simple, logical rules based on using logical connectives (AND, OR, NOT) to combine separate independent decisions. This project focuses on using a set of logical-rule models that are capable of explaining the time course of categorization by combining sequential sampling and mental-architecture models of RT within an integrated framework.
2012-2014 ARC Discovery Project Grant: Dr. Daniel R. Little. Feature processing in perceptual categorization.
Blunden, A. G., Wang, T., Griffiths, D. & Little, D. R. (2014). Logical-rules and the classification of integral dimensions: Arbitrary dimensions are not necessarily processed coactively. Frontiers in Psychology, 5, 1531. [pdf]
Little, D. R., Nosofsky, R. M., Donkin, C. & Denton, S. E. (2013). Logical-rules and the classification of integral dimensioned stimuli. Journal of Experimental Psychology: Learning, Memory & Cognition, 39, 801-820. [pdf]
Little, D. R., Nosofsky, R. M., & Denton, S. (2011). Response time tests of logical rule-based models of categorization.Journal of Experimental Psychology: Learning, Memory & Cognition, 37, 1-27. [pdf]
Fific, M., Little, D. R. & Nosofsky, R. (2010). Logical-rule models of classification response times: A synthesis of mental-architecture, random-walk, and decision-bound approaches. Psychological Review, 117, 309-348. [pdf]