Dr. Daniel Little
B.A. (Hons) (UWA), PhD (UWA)
Lecturer
Contact details:
| email:daniel.little@unimelb.edu.au | |
| telephone: +61 3 8344 3684 |
Lab Webpage: http://www.psych.unimelb.edu.au/research/labs/knowlab
Research Interests:
I am interested in how people form knowledge representations and how these representations influence the perception and interpretation of new events and information. The aim of this research is first to characterize basic information properties and understand the cognitive mechanisms underlying interpretation of new information (what do people do? and how do they do it?), and second, to develop linking hypotheses for how these computations and mechanisms might be carried out or instantiated in the brain.
Professional Associations & Memberships
- The Society for Mathematical Psychology
- Cognitive Science Society
- Association for Psychological Science
- The Psychonomics Society
Awards:
- NIMH Modeling in Cognition Training Grant (Indiana University)
- Australian Postgraduate Award Scholarship (University of Western Australia)
- Jean Rogerson Postgraduate Scholarship (University of Western Australia)
- H.L. Fowler Prize for best Honour's Thesis (University of Western Australia)
Recent Funded Research:
| Project: | Feature processing in categorization |
| Year: | 2012-2014 |
| Funded by: | Australian Research Council Discovery Project Grant |
| Project: | From fluid intelligence to crystallized expertise: An integrative Bayesian approach |
| Year: | 2012-2014 |
| Funded by: | Australian Research Council Discovery Project Grant |
| Project: | Inferring causality from graphs of scientific data |
| Year: | 2011 |
| Funded by: | University of Melbourne Early Career Researcher Grant |
| Project: | Searching for the 'best' option |
| Year: | 2011–2012 |
| Funded by: | Unilever Australia & University of Melbourne Collaboration Grant |
| Project: | Mental models of climate change |
| Year: | 2011 |
| Funded by: | University of Melbourne Interdisciplinary Seed Funding |
| Project: | TELIA: Technology for Endangered Languages in Australasia |
| Year: | 2011 |
| Funded by: | University of Melbourne Interdisciplinary Seed Funding |
Selected Publications:
Refereed journal articles:
Little, D. R., Nosofsky, R. M., & James, T. W. (2012). Brain-activation differences between perceptual categorization and old-new recognition memory are due to parameter changes. Proceedings of the National Academy of Sciences, 109, 333-338.
Sewell, D. K., Little, D. R., & Lewandowsky, S. (2011). Response time tests of logical rule-based models of categorization. Behavioral & Brain Sciences, 34, 212-213.
Craig, S., Lewandowsky, S., & Little, D. R. (2011). Error discounting in probabilistic category learning. Journal of Experimental Psychology: Learning, Memory & Cognition, 37, 673-687.
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.
Nosofsky, R. M., Little, D. R., Donkin, C. & Fific, M. (2011). Short-term memory scanning viewed as exemplar-based categorization. Psychological Review, 118, 280-315.
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.
Nosofsky, R. & Little, D. R. (in press). Classification response times in probabilistic rule-based category structures: Contrasting exemplar-retrieval and decision-bound models. Memory & Cognition, 38, 916-927.
Little, D. R. & Lewandowsky, S. (2009). Better Learning With More Error: Probabilistic feedback increases sensitivity to correlated cues. Journal of Experimental Psychology: Learning, Memory, & Cognition, 35, 1041-1061.
Little, D. R. & Lewandowsky, S. (2009). Beyond non-utilization: Irrelevant cues can gate learning in probabilistic categorization. Journal of Experimental Psychology: Human Perception and Performance, 35, 530-550.
Little, D. R. & Shiffrin, R. M. (2009). Simplicity Bias in the Estimation of Causal Functions. Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society, 1157-1162.
Little, D. R., Lewandowsky, S. & Heit, E. (2006). Ad hoc restructuring. Memory & Cognition, 34 (7), 1398-1431.
Book Chapters:
Little, D. R. & Lewandowsky, S. (2012). Multiple cue probability learning. In N. Seel (Ed.) Encyclopedia of the Sciences of Learning, New York: Springer.
Lewandowsky, S., Little, D. R. & Kalish, M. L. (2007). Knowledge and expertise. In F. T. Durso, R. Nickerson, S. Dumais, S. Lewandowsky, & T. Perfect (Eds.). Handbook of applied cognition, 2nd Ed. (pp. 83 - 110). Chicester: Wiley.
PhD Students under Supervision:
Robert De Lisle (co-supervised with Yoshi Kashima)
David Griffiths (co-supervised with Simon Cropper)
Geoff Saw (co-supervised with Meredith McKague)