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Cited by 391

The DOI matches its Crossref record; 221 of the 221 references it cites were found in a registry or an index.

Forty-five years after Broadbent (1958): Still no identification without attention.

Lachter, Joel, Forster, Kenneth I., Ruthruff, Eric·2004·Psychological Review

Abstract

According to D. E. Broadbent's (1958) selective filter theory, people do not process unattended stimuli beyond the analysis of basic physical properties. This theory was later rejected on the basis of numerous findings that people identify irrelevant (and supposedly unattended) stimuli. A careful review of this evidence, however, reveals strong reasons to doubt that these irrelevant stimuli were in fact unattended. This review exposed a clear need for new experiments with tight control over the locus of attention. The authors present 5 such experiments using a priming paradigm. When steps were taken to ensure that irrelevant stimuli were not attended, these stimuli produced no priming effects. Hence, the authors found no evidence that unattended stimuli can be identified. The results support a modern version of Broadbent's selective theory, updated to reflect recent research advances.

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Metadata

Authors
Lachter, J., Forster, K. I., & Ruthruff, E.
Venue
Psychological Review
Published
2004-10
Volume
111
Issue
4
Pages
880-913
ISSN
  • 0033-295X (ISSN-L)
  • 1939-1471
OpenAlex
W2156559440
Cited by
391
References
221
FWCI
5.60
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Forty-five years after Broadbent (1958): Still no identification without attention. https://app.cochat.ai/papers/10.1037/0033-295x.111.4.880
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Lachter, J., Forster, K. I., & Ruthruff, E. (2004). Forty-five years after Broadbent (1958): Still no identification without attention. Psychological Review. https://doi.org/10.1037/0033-295x.111.4.880

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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.