Current Lab Members

• 
  Daniel R. Little PhD

  Head of Lab

  Melbourne School of Psychological Sciences

  +61 3 8344 3684
  daniel.little@unimelb.edu.au
• 
  Anthea Blunden

  PhD Candidate

  Melbourne School of Psychological Sciences

  +61 3 9035 4339
  a.blunden@student.unimelb.edu.au
• 
  Xue Jun Cheng

  PhD Candidate

  Melbourne School of Psychological Sciences

  +61 3 9035 4339
  xjcheng@student.unimelb.edu.au
• 
  Deborah Lin

  PhD Candidate

  Melbourne School of Psychological Sciences

  +61 3 9035 4339
  djlin@student.unimelb.edu.au
• 
  Sarah Moneer

  PhD Candidate

  Melbourne School of Psychological Sciences

  +61 3 9035 4339
  s.moneer@student.unimelb.edu.au
• 
  Daryl Chen

  Honours Student

  Melbourne School of Psychological Sciences

  +61 3 9035 4339
  darylc2@student.unimelb.edu.au

Associated / Visiting Academics & Students

Prof Cheng-Ta Yang (National Cheng Kung University, Taiwan)
A/Prof Ami Eidels (University of Newcastle)
Prof James Townsend (Indiana University)
Dr Mario Fific (Grand Valley State University)
A/Prof Joseph Houpt (Wright Valley State University)
A/Prof Chris Donkin (University of New South Wales)
Dr Peter Shepherdson (post-doc, University of Zurich)
Paul Garrett (visiting PhD student, University of Newcastle)

Former Lab Members

Post-docs/PhD Students

Dr Tony S. L. Wang (post-doctoral researcher)
Dr Shi Xian Liew (PhD student; now University of Wisconsin, USA)
Dr Robert De Lisle (PhD student)

Honours Students

2018     Daryl Chen
2017     Cameron Boyle
2016     Dylan Hammond
2016     Deborah Lin
2016     Aspen Zhou
2015     Ariel Goh
2015     Marcellin Martinie
2014     Xue Jun Cheng
2014     Tammy Dennis
2014     Callum McCarthy
2014     Sarah Moneer
2013     Kaye Mullins (Jeff Pressing Prize – Best Honours Thesis)
2013     Camille Dean
2013     Amitoze Nandha
2013     Siok Yee Natalie See
2012     Anthea Blunden (Jeff Pressing Prize – Best Honours Thesis)
2012     Shi Xian Liew
2012     Margaret Webb
2011     Nicole Christie
2011     Nicole Le Roux

Research Assistants/Interns

Danièle Martinie
Deborah Lin
Grace Killmer
Sarah Moneer
Xue Jun Cheng
Franco Scalzo
Anthea Blunden
Charlotte Hudson
Robert De Lisle

BECOME A TEST PARTICIPANT

Our experiments are conducted on the University of Melbourne campus in Parkville (in the Redmond Barry Building). Most of experiments are conducted over multiple 1-hour sessions. Participants are compensated for their time.

If you would like to take part in one of our experiments or if you would like to find out more about our experiments, please contact us via email (pdpsylab@gmail.com).

Books 

• Little, D.R., Altieri, N., Fific, M. & Yang, C-T. (2017). Systems Factorial Technology: A Theory Driven Methodology for the Identification of Perceptual and Cognitive Mechanisms. Academic Press.

Peer-reviewed Journal Articles (Lab members' names in bold)

• Blunden, A. G., Howe, P. D. L. & Little, D. R. (2020). Evidence that within-dimension features are processed coactively. Attention, Perception, & Psychophysics, 82, 193-227.
• Houpt, J. W., Eidels, A. & Little, D. R. (2019). Developments in Systems Factorial Technology: Theory and Applications. Journal of Mathematical Psychology, 92, 1-3.
• Lilburn, S. D., Little, D. R., Osth, A. F. & Smith, P. L. (2019). Cultural problems cannot be solved with technical solutions alone. Computational Brain & Behavior, 2, 170-175.
• Little, D. R., Eidels, A., Houpt, J. W., Garrett, P. M. & Griffiths, D. W. (2019). Systems Factorial Technology analysis of mixtures of processing architectures. Journal of Mathematical Psychology, 92.
• Webb, M. E., Cropper, S. & Little, D. R. (2019). Aha is best when preceded by a "huh?" Presentation of a solution enhances aha experience. Thinking & Reasoning, 25, 324-364.
• Webb, M. E., Laukkonen, R. E., Cropper, S. J. & Little, D. R. (2019). Moment of (Perceived) Truth: Exploring Accuracy of Aha! Experiences. Journal of Creative Behavior. [Accepted 3-Dec-2019]
• Yang, C-T., Wang, C-H., Chang, T-Y., Yu, J-C. & Little, D. R. (2019). Cue-driven changes in detection strategies reflect trade-offs in strategic efficiency. Computational Brain & Behavior, 2, 109-127.
• Baribault, B., Donkin, C., Little, D. R., Trueblood, J. S., Orzevcz, Z., van Ravenzwaaij, D., White, C. N., De Boeck, P. & vanderkerckhove, J. (2018). Metastudies for robust tests of theory Proceedings of the National Academy of Sciences, 115, 2607-2612.
• Cheng, X. J., Mccarthy, C., Wang, T., Palmeri, T. J. & Little, D.R. (2018). Composite faces are not (necessarily) processed coactively: A test using Systems Factorial Technology and Logical-Rule Models. Journal of Experimental Psychology: Learning, Memory & Cognition, 44, 833-862.
• Little, D. R., Eidels, A., Fific, M., & Wang, T. S. L. (2018). How do information processing systems deal with conflicting information? Differential predictions for serial, parallel and coactive processing models.Computational Brain & Behavior, 1, 1-21.
• Little, D. R. & Smith, P. L. (2018). Commentary on Zwaan et al. - Replication is already mainstream: Lessons from Small-N designs. Behavioral and Brain Sciences, 41, e141.[Accepted 31-Jan-18].
• Smith, P. L. & Little, D. R. (2018). Small is beautiful: In defense of the small-N design. Psychonomic Bulletin & Review, 25, 2083-2101.
• Webb, M. E., Little, D. R. & Cropper, S. (2018). Once more with feeling: Preliminary norming data for the aha experience in insight and non-insight problems. Behavior Research Methods, 50, 2035-2056. [Supplement].
• Yang, C-T., Altieri, N. & Little, D.R. (2018). An examination of parallel versus coactive processing accounts of redundant-target audiovisual signal processing. Journal of Mathematical Psychology, 82, 138-158.
• Yang, C-T., Fific, M., Chang, T-Y. & Little, D.R. (2018). Systems factorial technology provides new insights on the other-race effect. Psychonomic Bulletin & Review, 25, 596-604.
• Little, D. R., Eidels, A., Houpt, J. W. & Yang, C-T. (2017). Set size slope still does not distinguish parallel from serial search. Behavioral Brain Science, 40, e145.
• Webb, M. E., Little, D. R., Cropper, S. & Roze, K. (2017). The contributions of convergent thinking, divergent thinking, and schizotypy to solving insight and non-insight problems. Thinking & Reasoning, 23, 235-258.
• Chang, T-Y., Little, D. R. & Yang, C-T. (2016). Selective attention modulates the effect of target location probability on redundant signal processing. Attention, Perception & Psychophysics, 78, 1603-1624.
• Houpt, J. W. & Little, D. R.  (2016). Statistical analysis of the resilience function. Behavior Research Methods. [Accepted 12-Jul-2016].
• Liew, S. X., Howe, P. D. & Little, D. R. (2016). The appropriacy of averaging in the study of context effects. Psychonomic Bulletin & Review, 23, 1639–1646.  Media Coverage: http://tinyurl.com/jzo3bv9
• Little, D. R., Wang, T. & Nosofsky, R. (2016). Sequence-sensitive exemplar and decision-bound accounts of speeded-classification performance in a modified Garner-tasks paradigm. Cognitive Psychology, 89, 1-38.
• Moneer, S., Wang, T. & Little, D. R. (2016). The Processing Architectures of Whole-Object Features: A Logical Rules Approach. Journal of Experimental Psychology: Human Perception &Performance, 42, 1443-1465. [Supplement]
• Wang, T., Christie, N., Howe, P. D. & Little, D. R. (2016). Global cue inconsistency diminishes learning of cue validity. Frontiers in Psychology, 7, 1743, 1-10. [Supplement].
• Webb, M. E., Little, D. R. & Cropper, S. (2016).  Insight is not in the problem: Investigating insight in problem solving across task types. Frontiers in Psychology, 7, 1424.
• Blunden, A. G., Wang, T., Griffiths, D. & Little, D. R. (2015). Logical-rules and the classification of integral dimensions: individual differences in the processing of arbitrary dimensions. Frontiers in Psychology, 5, 1531.
• Howe, P. D. & Little, D. R. (2015). Searching for the highest number. Attention, Perception & Psychophysics, 77, 423-440.
• Little, D. R., Eidels, A., Fific, M. & Wang, T. (2015). Understanding the influence of distractors on workload capacity. Journal of Mathematical Psychology, 69, 25-36.
• Donkin, C, Little, D. R. & Houpt, J. W.  (2014). Assessing the speed-accuracy trade-off effect on the capacity of information processing. Journal of Experimental Psychology: Human Perception & Performance, 40, 1183-1202.
• Little, D. R., Lewandowsky, S. & Craig, S. (2014), Working memory capacity and fluid abilities: The more difficult the item, the more more is better. Frontiers in Psychology, 5, 239.
• Yang, C-T, Little, D. R. & Hsu, C-C. (2014). The influence of cueing on attentional focus in perceptual decision making. Attention, Perception & Psychophysics, 76, 2256-2275.
• 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., 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.
• 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. [Link to Software].
• Howe, P. D., Incledon, N. C. & 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,e41491.
• Hudson, C., Howe, P. D. & Little, D. R. (2012). Hemifield effects in multiple identity tracking. PLoS-ONE, 7,e43796.
• Little, D. R. (2012). Numerical predictions for serial, parallel, and coactive logical rule-based models of categorization response times. Behavior Research Methods, 44, 1148-1156. [Supplement, Software].
• 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. Figure 4 - Corrected Y Axis.
• 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.
• Sewell, D. K., Little, D. R. & Lewandowsky, S. (2011). Bayesian computation and mechanism: Theoretical plurality drives scientific emergence. Behavioral & Brain Sciences, 34, 212-213.
• Fific, M., Little, D. R. & Nosofsky, R. M. (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. M. & Little, D. R. (2010). 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., Lewandowsky, S. & Heit, E. (2006). Ad hoc category restructuring. Memory & Cognition, 34, 1398-1431.

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. 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. Academic Press
• Cheng, X. J., Moneer, S., Christie, N. & Little, D. R. (2016). Categorization, Capacity, and Resilience. 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. Academic Press.
• Fific, M. & Little, D. R. (2016). Stretching mental processes: An overview of and guide for SFT applications. 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.Academic Press.
• 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. 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. Academic Press.
• 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. 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. Academic Press.
• 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

• Dennis, T. M. & Little, D. R. (2017). The role of imagination in exemplar generation: The effects of conflict and explanation. Proceedings of the Thirty-Ninth Annual Conference of the Cognitive Science Society.
• Lin, D. J. & Little, D. R. (2017). Sequential effects in the Garner task. Proceedings of the Thirty-Ninth Annual Conference of the Cognitive Science Society.
• 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.
• 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 Thirty-Fourth Annual Conference of the Cognitive Science Society (pp. 1918-1923). Austin, TX: Cognitive Science Society.
• 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.

Github

Code and data for several projects are available at https://github.com/knowlabUnimelb/

Fluency Profiling System

Python code available at //github.com/greymatter24/FPS

For details see 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.

Matlab Tutorial Videos

I created some resources for introducing Matlab as part of the PSYC40012 Models of Psychological Processes subject. Available here: http://505s-psychweb4.psych.unimelb.edu.au/MatlabTutorial/