Last Updated: September 2009

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Daniel Little
Post-doctoral Fellow

Personal Webpage

Current Projects
Research Intrests & Bio

I’m interested in how people develop explanations (or mental models) to account for noisy data. Modern statistical techniques tells us that explanations should function in a way that balances how well they explain the data with complexity. Do human explanations have the same properties? My other research interests can broadly be construed as looking at how people form knowledge representations and how these representations influence the perception and interpretation of events and incoming information. A key component of my research is the use of probabilistic and computational models to explain and predict behavior in theoretically rigorous fashion.

Representative Publications
Little, D. R. & Shiffrin, R. M. (in press). Simplicity Bias in the Estimation of Causal Functions. Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society. Amsterdam, Netherlands.
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., Lewandowsky, S. & Heit, E. (2006). Ad hoc restructuring. Memory & Cognition, 34 (7), 1398-1431.