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Daniel R. Little
Daniel R. Little
Melbourne School of Psychological Sciences, The University of Melbourne
Verified email at unimelb.edu.au - Homepage
Title
Cited by
Cited by
Year
Small is beautiful: In defense of the small-N design
PL Smith, DR Little
Psychonomic Bulletin & Review 1, 1-19, 2018
4522018
Short-term memory scanning viewed as exemplar-based categorization.
RM Nosofsky, DR Little, C Donkin, M Fific
Psychological review 118 (2), 280, 2011
1822011
Insight is not in the problem: Investigating insight in problem solving across task types
ME Webb, DR Little, SJ Cropper
Frontiers in Psychology 7, 1424, 2016
1802016
Logical-rule models of classification response times: A synthesis of mental-architecture, random-walk, and decision-bound approaches.
M Fific, DR Little, RM Nosofsky
Psychological review 117 (2), 309, 2010
1752010
Metastudies for robust tests of theory
B Baribault, C Donkin, DR Little, JS Trueblood, Z Oravecz, ...
Proceedings of the National Academy of Sciences 115 (11), 2607-2612, 2018
1692018
Activation in the neural network responsible for categorization and recognition reflects parameter changes
RM Nosofsky, DR Little, TW James
Proceedings of the National Academy of Sciences 109 (1), 333-338, 2012
1022012
Once more with feeling: Normative data for the aha experience in insight and noninsight problems
ME Webb, DR Little, SJ Cropper
Behavior research methods 50, 2035-2056, 2018
1002018
Response-time tests of logical-rule models of categorization.
DR Little, RM Nosofsky, SE Denton
Journal of Experimental Psychology: Learning, Memory, and Cognition 37 (1), 1, 2011
1002011
The contributions of convergent thinking, divergent thinking, and schizotypy to solving insight and non-insight problems
ME Webb, DR Little, SJ Cropper, K Roze
Thinking & Reasoning 23, 235-258, 2017
862017
Logical rules and the classification of integral-dimension stimuli.
DR Little, RM Nosofsky, C Donkin, SE Denton
Journal of Experimental Psychology: Learning, Memory, and Cognition 39 (3), 801, 2013
802013
Understanding the influence of distractors on workload capacity
DR Little, A Eidels, M Fific, T Wang
Journal of Mathematical Psychology 68, 25-36, 2015
792015
The acceptability and uptake of smartphone tracking for COVID-19 in Australia
PM Garrett, JP White, S Lewandowsky, Y Kashima, A Perfors, DR Little, ...
PloS one 16 (1), e0244827, 2021
772021
Assessing the speed-accuracy trade-off effect on the capacity of information processing.
C Donkin, DR Little, JW Houpt
Journal of Experimental Psychology: Human Perception and Performance, 2013
762013
The appropriacy of averaging in the study of context effects
SX Liew, PDL Howe, DR Little
Psychonomic bulletin & review 23, 1639-1646, 2016
722016
Knowledge and expertise
S Lewandowsky, D Little, ML Kalish
Handbook of applied cognition, 83-109, 2007
682007
Public acceptance of privacy-encroaching policies to address the COVID-19 pandemic in the United Kingdom
S Lewandowsky, S Dennis, A Perfors, Y Kashima, JP White, P Garrett, ...
Plos one 16 (1), e0245740, 2021
662021
Systems Factorial Technology: A Theory Driven Methodology for the Identification of Perceptual and Cognitive Mechanisms
DR Little, N Altieri, M Fific, CT Yang
Elsevier Academic Press, 2017
652017
Working memory capacity and fluid abilities: The more difficult the item, the more more is better
DR Little, S Lewandowsky, S Craig
Frontiers in psychology 5 (239), 1-13, 2014
632014
Working memory capacity and fluid abilities: The more difficult the item, the more more is better
DR Little, S Lewandowsky, S Craig
Proceedings of the Thirty-Fifth Annual Conference of the Cognitive Science …, 2013
632013
“Aha!” is stronger when preceded by a “huh?”: presentation of a solution affects ratings of aha experience conditional on accuracy
ME Webb, SJ Cropper, DR Little
Thinking & Reasoning 25 (3), 324-364, 2019
392019
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