The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
A unified model for perceptual learning
Overview
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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 · cited 3×
“Posner [ 17 ] suggests a very similar explanation of taskirrelevant learning to that of recent reinforcement models.”
BackgroundInfluentialTesting the efficiency and independence of attentional networks
Fan, J., McCandliss, B. D., Sommer, T., Raz, A., & Posner, M. I. (2002) · Journal of Cognitive Neuroscience · doi:10.1162/089892902317361886 · Cited by 3,717
Multiple reward signals in the brain
Schultz, W. (2000) · Nature Reviews Neuroscience · doi:10.1038/35044563 · Cited by 1,303
Coull, J. T., Frith, C. D., Frackowiak, R. S. J., & Grasby, P. M. (1996) · Neuropsychologia · doi:10.1016/0028-3932(96)00029-2 · Cited by 566
Attentional control of early perceptual learning.
Ahissar, M., & Hochstein, S. (1993) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.90.12.5718 · Cited by 497
Temporal plasticity in the primary auditory cortex induced by operant perceptual learning
Bao, S., Chang, E. F., Woods, J., & Merzenich, M. M. (2004) · Nature Neuroscience · doi:10.1038/nn1293 · Cited by 265
Cited by
Benefits of multisensory learning
Shams, L., & Seitz, A. R. (2008) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2008.07.006 · Cited by 1,178
Self-citationPerceptual Learning: Toward a Comprehensive Theory
Watanabe, T., & Sasaki, Y. (2015) · Annual Review of Psychology · doi:10.1146/annurev-psych-010814-015214 · Cited by 347
Self-citationBreath of Life: The Respiratory Vagal Stimulation Model of Contemplative Activity
Gerritsen, R. J. S., & Band, G. P. H. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00397 · Cited by 342
“A limitation to the effective strength of attention training is that transfer of effects to other contexts and untrained skills is known to be rather limited (Seitz and Watanabe, 2005; Green and Bavelier, 2012; Keshavan et al., 2014; Simons et al., 2016).”
BackgroundRewards evoke learning of unconsciously processed visual stimuli in adult humans
Seitz, A. R., Kim, D., & Watanabe, T. (2009) · Neuron · doi:10.1016/j.neuron.2009.01.016 · Cited by 337
Self-citationGrounding cognitive control in associative learning
Abrahamse, E., Braem, S., Notebaert, W., & Verguts, T. (2016) · Psychological Bulletin · doi:10.1037/bul0000047 · Cited by 276
Metadata
- Authors
- SEITZ, A., & WATANABE, T.
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2005-07
- Volume
- 9
- Issue
- 7
- Pages
- 329-334
- ISSN
- 1364-6613 (ISSN-L)
- OpenAlex
- W2154559964
- Cited by
- 356
- References
- 6
- FWCI
- 5.91
- 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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A unified model for perceptual learning. https://app.cochat.ai/papers/10.1016/j.tics.2005.05.010
- The paper
- SEITZ, A., & WATANABE, T. (2005). A unified model for perceptual learning. Trends in Cognitive Sciences. https://doi.org/10.1016/j.tics.2005.05.010
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.
