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Inhibition and Interference in Selective Attention: Some Tests of a Neural Network Model
Abstract
We describe a neural network system that models selective action—that is, how an organism selectively responds to an object when other objects evoke competing responses. Performance of the model during simulations of various selective action situations reveals a number of interesting patterns of data. Specifically, the model shows a complex relationship between how much a distractor interferes with response to a target and how much inhibition is associated with the distractor. Subsequent experiments with human subjects reveal that the paradoxical behaviour of the model is also observed in human behaviour. We conclude that the similar performance characteristics of the model and human subjects in a variety of situations suggest that the model has captured some of the essential properties of mammalian selective action mechanisms.
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“As the external input increases, inhibition has to increase accordingly, and this is visible in the form of larger inhibitory rebounds following stimulus offset (Houghton et al., 1996;Schuch et al., 2010).”
From Automatic imitation of multiple agents: A computational model (2019) · cites this paper as Houghton et al., 1996
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Metadata
- Authors
- Houghton, G., Tipper, S. P., Weaver, B., & Shore, D. I.
- Affiliations
- University College London (GB)
- Bangor University (GB)
- Venue
- Visual Cognition
- Publisher
- Informa UK Limited
- Published
- 1996-06
- Volume
- 3
- Issue
- 2
- Pages
- 119-164
- ISSN
- 1350-6285 (ISSN-L)
- 1464-0716
- OpenAlex
- W2079311980
- Cited by
- 144
- References
- 96
- Citations by year
- 2024: 2 · 2023: 1 · 2022: 1 · 2021: 2 · 2020: 2 · 2019: 4 · 2018: 2 · 2017: 6 · 2016: 2 · 2015: 3 · 2014: 7 · 2013: 3 · 2012: 5
- FWCI
- 2.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Visual Cognition (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- selective attention, inhibition, distractor interference, neural network model, competing responses
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