Dr Daniel R Little
Dr Daniel R Little
BA (Hons. Psych.), PhD (UWA)
Cognitive psychology and behavioural neuroscience
Key research questions
How do we develop knowledge through experience and learning?
How do our knowledge representations influence how we perceive and interpret new information?
How do our interpretations and perceptions influence our behaviour?
What does our behaviour tells us about our knowledge?
Specific research interests include: using computational models of response time to characterize rule-based decision making in categorization, differentiating categorization models, relating categorization to other processes such as discrimination and recognition memory, examining how basic categorization processes scale up to other decision making tasks
Little, D. R., Lewandowsky, S. & Craig, S. (in press). Working memory capacity and fluid abilities: The more difficult the item, the more more is better. Frontiers in Cognitive Science. [Accepted 04/03/2014]
Donkin, C., Little, D. R. & Houpt, J. W. (in press). Assessing the speed-accuracy trade-off effect on the capacity of information processing. Journal of Experimental Psychology: Learning, Memory & Cognition.
Little, D. R., Lewandowsky, S. & Craig, S. (2013). Working memory capacity and fluid abilities: The more difficult the item, the more more is better. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 918-923). Austin, TX: Cognitive Science Society.
Cropper, S. J., Kvansakul, J. G. S. & Little, D. R. (2013). The categorisation of non-categorical colours: A novel paradigm in colour perception. PLOS-One, 8, e59945, 1-21.
Society of Mathematical Psychology
Cognitive Science Society
2012-2014 ARC Discovery Project Grant: Dr. Daniel R. Little. Feature processing in perceptual categorization.
2012-2014 ARC Discovery Project Grant: Dr. Daniel R. Little, Prof. Stephan Lewandowsky, Dr. Adam Sanborn, Dr. Tom Griffiths. From fluid intelligence to crystallized expertise: An integrative Bayesian approach.
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