Characterizing attentional resources.
Abstract
Other studies have found that it is easier to divide attention when messages can be discriminated on the basis of stimulus and response features. The present study extended these results and explored whether dual-task performance is a function of similarity of central processing and, more specifically, the semantic similarity of the competing messages. In a dichotic listening task, subjects detected targets in concurrent messages that either differed semantically and required different central processing (the mixed condition) or were semantically similar and required similar central processing (the same condition). Three criteria are developed to determine whether the tasks in the mixed condition call upon distinct resources. The results are discussed in terms of three metaphors for resources: fuel, structure, and skills.
Cited by (16)
- Working memory, comprehension, and aging: A review and a new viewHasher, Lynn, Zacks, Rose T. · 1988 · Psychology of Learning and Motivation
- Dual-task interference in simple tasks: data and theoryPashler, Harold · 1994 · Psychological Bulletin
- Multiple Resources and Mental WorkloadWickens, Christopher D. · 2008 · Human Factors: The Journal of the Human Factors and Ergonomics Society
- An opportunity cost model of subjective effort and task performanceKurzban, Robert, Duckworth, Angela, Kable, Joseph W., Myers, Justus · 2013 · Behavioral and Brain Sciences
- On cognitive busyness: When person perceivers meet persons perceived.Gilbert, Daniel T., Pelham, Brett W., Krull, Douglas S. · 1988 · Journal of Personality and Social Psychology
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Characterizing attentional resources. https://app.cochat.ai/papers/10.1037//0096-3445.116.1.68
- The paper
- Hirst, W., & Kalmar, D. (1987). Characterizing attentional resources. Journal of Experimental Psychology: General. https://doi.org/10.1037//0096-3445.116.1.68
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.