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On the Division of Attention: A Disproof of the Single Channel Hypothesis
Abstract
In dichotic listening, subjects are apparently unable to attend simultaneously to two concurrent, auditory speech messages. However, in two experiments reported here, it is shown that people can attend to and repeat back continuous speech at the same time as taking in complex, unrelated visual scenes, or even while sight-reading piano music. In both cases performance with divided attention was very good, and in the case of sight-reading was as good as with undivided attention. There was little or no effect of the dual task on the accuracy of speech shadowing. These results are incompatible with the hypothesis that human attention is limited by the capacity of a general–purpose central processor in the nervous system. An alternative, “multi-channel”, hypothesis is outlined.
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Cites
Broadbent, D. E. (1958) · doi:10.1037/10037-000 · Cited by 4,337
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Ericsson, K. A., & Kintsch, W. (1995) · Psychological Review · doi:10.1037/0033-295x.102.2.211 · Cited by 3,498
Dual-task interference in simple tasks: data and theory
Pashler, H. (1994) · Psychological Bulletin · doi:10.1037/0033-2909.116.2.220 · Cited by 3,112
A cognitive model of drug urges and drug-use behavior: role of automatic and nonautomatic processes
Tiffany, S. T. (1990) · Psychological Review · doi:10.1037/0033-295x.97.2.147 · Cited by 1,554
Toward a rational and mechanistic account of mental effort
Shenhav, A., Musslick, S., Lieder, F., Kool, W., Griffiths, T. L., Cohen, J. D., & Botvinick, M. M. (2017) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-072116-031526 · Cited by 1,077
Metadata
- Authors
- Allport, D. A., Antonis, B., & Reynolds, P.
- Venue
- Quarterly Journal of Experimental Psychology
- Publisher
- SAGE Publications
- Published
- 1972-05
- Volume
- 24
- Issue
- 2
- Pages
- 225-235
- ISSN
- 0033-555X (ISSN-L)
- OpenAlex
- W2129318928
- Cited by
- 598
- References
- 1
- FWCI
- 2.92
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
