The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Human Category Learning
Abstract
Much recent evidence suggests some dramatic differences in the way people learn perceptual categories, depending on exactly how the categories were constructed. Four different kinds of category-learning tasks are currently popular—rule-based tasks, information-integration tasks, prototype distortion tasks, and the weather prediction task. The cognitive, neuropsychological, and neuroimaging results obtained using these four tasks are qualitatively different. Success in rule-based (explicit reasoning) tasks depends on frontal-striatal circuits and requires working memory and executive attention. Success in information-integration tasks requires a form of procedural learning and is sensitive to the nature and timing of feedback. Prototype distortion tasks induce perceptual (visual cortical) learning. A variety of different strategies can lead to success in the weather prediction task. Collectively, results from these four tasks provide strong evidence that human category learning is mediated by multiple, qualitatively distinct systems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The attention system of the human brain
Posner, M. I., & Petersen, S. E. (1990) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.13.030190.000325 · Cited by 8,070
Context theory of classification learning
Medin, D. L., & Schaffer, M. M. (1978) · Psychological Review · doi:10.1037/0033-295x.85.3.207 · Cited by 3,061
A neuropsychological theory of positive affect and its influence on cognition.
Ashby, F. G., Isen, A. M., & Turken, A. U. (1999) · Psychological Review · doi:10.1037/0033-295x.106.3.529 · Cited by 2,270
Self-citationRosch, E. (1975) · Cognitive Psychology · doi:10.1016/0010-0285(75)90021-3 · Cited by 1,423
Downes, J. J., Roberts, A. C., Sahakian, B. J., Evenden, J. L., Morris, R. G., & Robbins, T. W. (1989) · Neuropsychologia · doi:10.1016/0028-3932(89)90128-0 · Cited by 527
The utility of the Wisconsin Card Sorting Test in detecting and localizing frontal lobe lesions
Robinson, A. L., Heaton, R. K., Lehman, R. A., & Stilson, D. W. (1980) · Journal of Consulting and Clinical Psychology · doi:10.1037/0022-006x.48.5.605 · Cited by 491
Mechanisms of perceptual learning
Dosher, B. A., & Lu, Z.-L. (1999) · Vision Research · doi:10.1016/s0042-6989(99)00059-0 · Cited by 394
Array models for category learning
Estes, W. K. (1986) · Cognitive Psychology · doi:10.1016/0010-0285(86)90008-3 · Cited by 356
Lombardi, W. J., Andreason, P. J., Sirocco, K. Y., Rio, D. E., Gross, R. E., Umhau, J. C., & Hommer, D. W. (1999) · Journal of Clinical and Experimental Neuropsychology · doi:10.1076/jcen.21.1.2.940 · Cited by 144
Cited by
Chapter 2 Design for a Working Memory
Oberauer, K. (2009) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(09)51002-x · Cited by 636
Experience-dependent representation of visual categories in parietal cortex
Freedman, D. J., & Assad, J. A. (2006) · Nature · doi:10.1038/nature05078 · Cited by 542
Hirshleifer, D. (2015) · Annual Review of Financial Economics · doi:10.1146/annurev-financial-092214-043752 · Cited by 497
Reinforcement Learning in Multidimensional Environments Relies on Attention Mechanisms
Niv, Y., Daniel, R., Geana, A., Gershman, S. J., Leong, Y. C., Radulescu, A., & Wilson, R. C. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.2978-14.2015 · Cited by 362
Redish, A. D., Jensen, S., Johnson, A., & Kurth-Nelson, Z. (2007) · Psychological Review · doi:10.1037/0033-295x.114.3.784 · Cited by 346
Reber, P. J. (2013) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2013.06.019 · Cited by 296
A neurobiological theory of automaticity in perceptual categorization
Ashby, F. G., Ennis, J. M., & Spiering, B. J. (2007) · Psychological Review · doi:10.1037/0033-295x.114.3.632 · Cited by 286
Self-citationTracking the emergence of conceptual knowledge during human decision making
Kumaran, D., Summerfield, J. J., Hassabis, D., & Maguire, E. A. (2009) · Neuron · doi:10.1016/j.neuron.2009.07.030 · Cited by 260
Basal Ganglia Circuits for Reward Value–Guided Behavior
Hikosaka, O., Kim, H. F., Yasuda, M., & Yamamoto, S. (2014) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071013-013924 · Cited by 259
Neural mechanisms for voice recognition
Andics, A., McQueen, J. M., Petersson, K. M., Gál, V., Rudas, G., & Vidnyánszky, Z. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.05.048 · Cited by 222
The basal ganglia in human learning
Seger, C. A. (2006) · The Neuroscientist · doi:10.1177/1073858405285632 · Cited by 182
Cheetham, M. (2011) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2011.00126 · Cited by 176
The role of the basal ganglia in category learning
Ashby, F. G., & Ennis, J. M. (2006) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(06)46001-1 · Cited by 120
Self-citationHandwriting generates variable visual output to facilitate symbol learning.
Li, J. X., & James, K. H. (2016) · Journal of Experimental Psychology: General · doi:10.1037/xge0000134 · Cited by 96
The hazards of explanation: Overgeneralization in the face of exceptions.
Williams, J. J., Lombrozo, T., & Rehder, B. (2013) · Journal of Experimental Psychology: General · doi:10.1037/a0030996 · Cited by 88
Interleaved presentation benefits science category learning.
Eglington, L. G., & Kang, S. H. K. (2017) · Journal of Applied Research in Memory and Cognition · doi:10.1016/j.jarmac.2017.07.005 · Cited by 72
Trial-to-trial, uncertainty-based adjustment of decision boundaries in visual categorization
Qamar, A. T., Cotton, R. J., George, R. G., Beck, J. M., Prezhdo, E., Laudano, A., Tolias, A. S., & Ma, W. J. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1219756110 · Cited by 64
Toward a dual-learning systems model of speech category learning.
Chandrasekaran, B., Koslov, S. R., & Maddox, W. T. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.00825 · Cited by 55
Self-citationSchematic memories develop quickly, but are not expressed unless necessary
Tompary, A., Zhou, W., & Davachi, L. (2020) · Scientific Reports · doi:10.1038/s41598-020-73952-x · Cited by 53
A detailed comparison of optimality and simplicity in perceptual decision making.
Shen, S., & Ma, W. J. (2016) · Psychological Review · doi:10.1037/rev0000028 · Cited by 46
Meta-learned models of cognition
Binz, M., Dasgupta, I., Jagadish, A. K., Botvinick, M., Wang, J. X., & Schulz, E. (2024) · Behavioral and Brain Sciences · doi:10.1017/s0140525x23003266 · Cited by 38
The impact of sleep on novel concept learning
Graveline, Y. M., & Wamsley, E. J. (2017) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2017.03.008 · Cited by 31
Explanation recruits comparison in a category-learning task
Edwards, B. J., Williams, J. J., Gentner, D., & Lombrozo, T. (2019) · Cognition · doi:10.1016/j.cognition.2018.12.011 · Cited by 34
When the poor excel: Poverty facilitates procedural learning
Dang, J., Xiao, S., Zhang, T., Liu, Y., Jiang, B., & Mao, L. (2016) · Scandinavian Journal of Psychology · doi:10.1111/sjop.12292 · Cited by 30
Cortico-spinal modularity in the parieto-frontal system: A new perspective on action control
Bufacchi, R. J., Battaglia-Mayer, A., Iannetti, G. D., & Caminiti, R. (2023) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2023.102537 · Cited by 22
Metadata
- Authors
- Ashby, F. G., & Maddox, W. T.
- Venue
- Annual Review of Psychology
- Publisher
- Annual Reviews
- Published
- 2005-02-01
- Volume
- 56
- Issue
- 1
- Pages
- 149-178
- ISSN
- 0066-4308 (ISSN-L)
- 1545-2085
- OpenAlex
- W2129668249
- Cited by
- 938
- References
- 9
- FWCI
- 8.51
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
