Publications

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)

• Lin, D. J. \& Little, D. R. (2023). Futher tests of sequential effects in a modified Garner task using separable dimensions. Journal of Experimental Psychology: General. [Accepted 22-09-2022].
• Osth, A. F., Zhou, A., Lilburn, S. \& Little, D. R. (2023). Novelty rejection in episodic memory. Psychological Review. [Accepted 24-10-2022].
• Blunden, A. G., Hammond, D., Howe, P. D. L. & Little, D. R. (2022). Characterizing the timecourse of decision-making in change detection. Psychological Review, 129, 107-145.
• Garrett, P. M., Bennett, M., Hsieh, Y-T., Howard, Z., Yang, C.-T., Little, D. R. & Eidels, A. (2022). Wheel of fortune: A cross-cultural examination of how expertise shapes the mental representation of familiar and unfamiliar numerals. Computational Brain \& Behavior, 5, 45-59.
• Garrett, P. M., White, J., Dennis, S., Lewandowksy, S., Yang, C-T., Okan, Y. Perfors, A., Little, D. R., Kozyreva, A., Lorenz-Spreen, P., Kusumi, T., \& Kashima, Y. (2022). Papers please: Predictive factors for the uptake of national and international COVID-19 immunity and vaccination passports. Journal of Medical Internet Research, 8, e32969.
• Kemp, C., Hamacher, D. W., Little, D. R., & Cropper, S. J. (2021). Comparing constellations across cultures. Nature Astronomy, 6, 406-409.
• Kemp, C., Hamacher, D. W., Little, D. R., & Cropper, S. J. (2021). Perceptual grouping explains constellations across cultures. Psychological Science, 33, 354-363.
• Little, D. R., Yang, H., Eidels, A. & Townsend, J. T. (2022). Extending systems factorial technology to errors responses. Psychological Review, 129, 484-512.
• Garrett, P. M., White, J., Lewandowksy, S., Kashima, Y., Perfors, A., Little, D. R., Geard, N.,Mitchell, L., & Dennis, S. (2021). The acceptability of smartphone tracking technologies for COVID-19 in Australia. PLOS-One, 16, e0244827.
• Howard, Z. L., Garrett, P., Little, D. R., Townsend, J. T., & Eidels, A.  (2021). A show about nothing: No-signal processes in systems factorial technology. Psychogical Review, 128, 187-201.
• Lewandowsky, S., Dennis, S., Perfors, A., Kashima, A., White, J., Garrett, P., Little, D. R., & Yesilada, M.  (2021). Public acceptance of privacy-encroaching policies to address the COVID-19 pandemic in the United Kindom. PLOS-One, 16..
• Lynch, C., Cheng, X. J. & Little, D. R. (2021). There’s a time and a face: The time course ofcomposite face processing. Journal of Experimental Psychology: Human Perception and Performance.
• Shang, L.,Little, D. R., Webb, M. E., Eidels, A. & Yang, C-T. (2021). The workload capacity of semantic search in convergent thinking. Journal of Experimental Psychology: General.
• Webb, M. E., Cropper, S. & Little, D. R. (2021). Unusual uses and experiences are good for feeling insightful, but not for problem solving: Contributions of schizotypy, divergent thinking, and fluid reasoning, to insight moments. Journal of Cognitive Psychology.
• 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.