Dr Daniel R Little

Academic profile

Senior Lecturer

BA (Hons. Psych.), PhD (UWA)

Senior Lecturer

Research Laboratory

Knowledge, Information & Learning Laboratory

Visual Cognition Lab

Research area

Cognitive psychology and behavioural neuroscience

Contact Details

E

daniel.little@unimelb.edu.au

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?

Research interests

Specific reserach interests include: using computational models of respones time to characterize rule-based decision making in categorization, differentiating categorization models, relating categorization to other processes such as discrimination and recogntion memory, examining how basic categorization processes scale up to other decision making tasks

Publications

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.

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.

Little, D. R., Oehmen, R., Dunn, J., Hird, K. & Kirsner, K. (2013). Fluency Profiling System: An automated system for analyzing the temporal properties of speech. Behavior Research Methods, 45, 191-202.

Howe, P. D., Incledon, N. & Little, D. R. (2012). Can attention be confined to just part of a moving object? Revisiting target-distractor merging in multiple object tracking. PLoS-ONE, 7,(7), e41491.

Hudson, C., Howe, P. D. L., & Little, D. R. (2012). Hemifield effects in multiple identity tracking. PLoS-ONE, 7, (8), e43796.

Little, D. R. (2012). Numerical predictions for serial, parallel and coactive logical rule-based models of response times. Behavior Research Methods, 44, 1148-1156. Supplement

Little, D. R. & Lewandowsky, S. (2012). Multiple-cue probability learning. In N. Seel (Ed.), Encyclopedia of the Sciences of Learning, New York: Springer.

Little, D. R., Lewandowsky, S. & Griffiths, T. L. (2012). A Bayesian model of Raven's Progressive Matrices. In N. Miyake, D. Peebles, & R. P. Cooper (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society (pp. 1918-1923). Austin, TX: Cognitive Science Society.

Nosofsky, R. M., Little, D. R. & James, T. W. (2012). Activation in the neural network responsible for categorization and recognition reflects parameter changes. Proceedings of the National Academy of Sciences, 109, 333-338. Erratum - Figure 4

Sewell, D. K., Little, D. R., & Lewandowsky, S. (2011). Bayesian computation and mechanism: Theoretical pluralism drives scientific emergence. 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. Supplement

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 approa. Psychological Review, 117, 309-348.

Nosofsky, R. & Little, D. R. (2010). Classification response times in probabilistic rule-based category structures: Contrasting exemplar-retrieval and decision-bound. Memory & Cognition, 38, 916-927. Supplement

Little, D. R. & Lewandowsky, S. (2009). Better learning with more error: Probabilistic feedback increases sensitivity to correlated cues in categorization. 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.

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.

Little, D. R., Lewandowsky, S. & Heit, E. (2006). Ad hoc restructuring. Memory & Cognition, 34 (7), 1398-1431.

Key statistics

http://scholar.google.com/citations?user=wBMdXsgAAAAJ

Teaching into

Cognitive Psychology

Perception, Memory and Cognition

Psychological Science: Theory & Practice

Current Topics in Cognitive Psychology

Models of Psychological Processes

Associations

Society of Mathematical Psychology

Psychonomic Society

Cognitive Science Society

Current grants

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.

Links

http://old.psych.unimelb.edu.au/research/labs/knowlab/

CV

DL_CV.pdf(121.28 KB)