Cited by 45Neural and Behavioral Psychology StudiesVisual perception and processing mechanismsVisual Attention and Saliency Detection

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

A Multi-level Account of Selective Attention

Serences, John T., Kastner, Sabine·2014·Oxford Handbooks Online

Abstract

To achieve behavioural goals, relevant sensory stimuli must be processed more quickly and reliably than irrelevant distracters. The ability to prioritize relevant over irrelevant stimuli is usually referred to as selective information processing, or selective attention. Over the last 50–60 years, there has been an ongoing debate about the point along the sensory–response processing stream at which selective attention operates: are relevant and irrelevant inputs segregated early in processing based on low-level featural differences, or does this segregation occur late in processing after the meaning of each stimulus has been computed? As with nearly all dichotomies in psychology, the emerging consensus is that neither extreme is correct. Instead, depending on task demands, the mechanisms of selective attention can flexibly operate on the quality of low-level sensory representations as well as on later stages of semantic analysis and decision-making.

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Crossref · 10.1093/oxfordhb/9780199675111.013.022

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Metadata

Authors
Serences, J. T., & Kastner, S.
Affiliations
University of California San Diego (US)
Venue
Oxford Handbooks Online
Published
2014-04-01
OpenAlex
W181843969
Cited by
45
References
5
Citations by year
2026: 1 · 2025: 4 · 2024: 2 · 2023: 3 · 2022: 5 · 2021: 5 · 2020: 6 · 2019: 5 · 2018: 5 · 2017: 1 · 2016: 5 · 2015: 3
FWCI
1.07
Type
book
Language
en
Open access
closed
Locations
Oxford University Press eBooks (publishedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
Keywords
selective attention, semantic analysis, decision-making, distractors, task demands

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