Knowledge, Information & Learning Laboratory

Research Overview

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Our work focuses on understanding how we develop knowledge through experience and learning, how 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.

Staff

Current PhD Students

Anthea Blunden
Xue Jun Cheng
Nicole Christie
Tammy Dennis
David Griffiths
Shi Xian Liew
Sarah Moneer
Geoff Saw
Amanda Shanks
Margaret Webb

Former PhD Students

Robert De Lisle Seeing other people: What sensory perception teaches us about social perception

Honours Students

2017 Cameron Boyle, Stephanie Hicks, Eleanor Smetana
2016 Dylan Hammond, Deborah Lin, Aspen Zhou
2015 Ariel Goh, Marcellin Martinie
2014 Xue Jun Cheng, Tammy Dennis, Callum McCarthy, Sarah Moneer
2013 Kaye Mullins (Jeff Pressing Prize - Best Honours Thesis), Camille Deane, Amitoze Nandha, Siok Yee Natalie See, Jenny Markov
2012 Anthea Blunden (Jeff Pressing Prize - Best Honours Thesis), Shi Xian Liew, Margaret Webb
2011 Nicole Christie, Nicole Le Roux

Research Assistant

Danielle Martinie

Lab Alumni

Dr Tony Wang, Post-doctoral Research Associate (now at Brown University)
Charlotte Hudson
Grace Killmer

Collaborators

Prof. Stephan Lewandowsky (University of Bristol)

Prof. Robert M. Nosofsky (Indiana University)

Dr. Chris Donkin (The University of New South Wales)

Dr. Ami Eidels (The University of Newcastle)

Dr. Mario Fific (Grand Valley State University)

Dr. Joseph Houpt (Wright State University)

Dr. Cheng-Ta Yang (National Cheng Kung University)

Funding

2016-2018 ARC DP160102360: Dr. Daniel R. Little, Dr. Ami Eidels & Prof. James T. Townsend. Learning from our mistakes: How and when complex decisions fail. ($224,565).

2015 Melbourne Research Grant Support Scheme: Dr. Daniel Little. The time course of perceptual learning and expertise. ($41,000).

2012-2014 ARC DP120103120: Dr. Daniel R. Little. Feature processing in categorization ($245,000).

2012-2014 ARC DP120103888: Prof. Stephan Lewandowsky, Dr. Daniel R. Little, Dr. Adam Sanborn, Dr. Tom Griffiths. From fluid intelligence to crystallized expertise: An integrative Bayesian approach ($765,000).

2011 Unilever [Commercial Funding]: Dr. Piers Howe & Dr. Daniel R. Little Searching for the "best" option ($29,972.88). The University of Melbourne. Funded by Unilever and by an University of Melbourne Research Collaboration Grant.

2011-2012 ERC Melbourne: Dr. Daniel R. Little Inferring causality from graphs of scientifi c data ($38,678.76). The University of Melbourne.

2011-2012 Interdisciplinary Seed Funding: Stephen Bird, David Grayden, Steve Howard, Daniel Little, Nick Thieberger, Sally Treloyn, Sarah Cut eld, Mark Libermen TELIA: Technology for Endangered Languages in Australasia ($48,740). The University of Melbourne.

2011-2012 Interdisciplinary Seed Funding: Yoshihisa Kashima, Ailie Gallant, David Karoly, Daniel Little, Angela Paladino, Peter  ayner, David K. Sewell, John Wiseman Climate Knowledge and Sustainable Lifestyle: A Preliminary Examination of Cultural Dynamics of Climate Change ($40,000). The University of Melbourne.

Research Opportunities

This research project is available to PhD students to join as part of their thesis.
Please contact the Research Group Leader to discuss your options.

Research Publications

Books 
Peer-reviewed Journal Articles (Lab members' names in bold)
Thesis & Peer Reviewed Book Chapters
  • Altieri, N., Fific, M., Little, D. R. & Yang, C-T. (2016). Historical foundations and a tutorial introduction to Systems Factorial Technology. To appear in D. R. Little, N. Altieri, M. Fific & C-T. Yang (Eds.). Systems Factorial Technology: A Theory Driven Methodology for the Identification of Perceptual and Cognitive Mechanisms. Elsevier. [Accepted 1-Apr-2016].
  • Cheng, X. J., Moneer, S., Christie, N. & Little, D. R. (2016). Categorization, Capacity, and Resilience. To appear in D. R. Little, N. Altieri, M. Fific & C-T. Yang (Eds.). Systems Factorial Technology: A Theory Driven Methodology for the Identication of Perceptual and Cognitive Mechanisms. Elsevier. [Accepted 22-Apr-2016].
  • Fific, M. & Little, D. R. (2016). Stretching mental processes: An overview of and guide for SFT applications. To appear in D. R. Little, N. Altieri, M. Fific & C-T. Yang (Eds.). Systems Factorial Technology: A Theory Driven Methodology for the Identication of Perceptual and Cognitive Mechanisms. Elsevier. [Accepted 12-Jul-2016].
  • Griffiths, D. W., Blunden, A. G. & Little, D. R. (2016). Logical-rule based models of categorization: Using Systems Factorial Technology to understand feature and dimensional processing. To appear in D. R. Little, N. Altieri, M. Fific & C-T. Yang (Eds.). Systems Factorial Technology: A Theory Driven Methodology for the Identification of Perceptual and Cognitive Mechanisms. Elsevier. [Accepted 6-Apr-2016]
  • Howard, Z. L., Eidels, A., Silbert, N. H. & Little, D. R. (2016). Can confusion data inform SFT-like inference? A comparison of SFT and accuracy-based measures in comparable experiments. To appear in D. R. Little, N. Altieri, M. Fific & C-T. Yang (Eds.). Systems Factorial Technology: A Theory Driven Methodology for the Identification of Perceptual and Cognitive Mechanisms. Elsevier. [Accepted 19-Jul-2016].
  • 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.
  • Little, D. R. (2009). Sensitivity to correlation in probabilistic environments. PhD Thesis, University of Western Australia.
Peer Reviewed Conference Proceedings

Research Projects

This Research Group doesn't currently have any projects



Faculty Research Themes

Neuroscience

School Research Themes

Cognitive Psychology and Behavioural Neuroscience



Key Contact

For further information about this research, please contact Dr Daniel R Little

Department / Centre

Melbourne School of Psychological Sciences

Unit / Centre

Knowlab