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Representing multiple objects as an ensemble enhances visual cognition
Open accessAbstract
The visual system can only accurately represent a handful of objects at once. How do we cope with this severe capacity limitation? One possibility is to use selective attention to process only the most relevant incoming information. A complementary strategy is to represent sets of objects as a group or ensemble (e.g. represent the average size of items). Recent studies have established that the visual system computes accurate ensemble representations across a variety of feature domains and current research aims to determine how these representations are computed, why they are computed and where they are coded in the brain. Ensemble representations enhance visual cognition in many ways, making ensemble coding a crucial mechanism for coping with the limitations on visual processing.
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Brady, T. F., Konkle, T., & Alvarez, G. A. (2011) · Journal of Vision · doi:10.1167/11.5.4 · Cited by 556
Self-citationConsciousness cannot be separated from function
Cohen, M. A., & Dennett, D. C. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.06.008 · Cited by 542
Information integration without awareness
Mudrik, L., Faivre, N., & Koch, C. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.04.009 · Cited by 277
Hullman, J., Resnick, P., & Adar, E. (2015) · PLOS ONE · doi:10.1371/journal.pone.0142444 · Cited by 194
Hypothetical Outcome Plots Help Untrained Observers Judge Trends in Ambiguous Data
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Metadata
- Authors
- Alvarez, G. A.
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2011-03
- Volume
- 15
- Issue
- 3
- Pages
- 122-131
- ISSN
- 1364-6613 (ISSN-L)
- OpenAlex
- W2098353881
- Cited by
- 586
- References
- 100
- FWCI
- 1.93
- Type
- journal article
- Language
- en
- Open access
- green
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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.
