Irrelevant tones produce an irrelevant speech effect: Implications for phonological coding in working memory.
Abstract
A series of studies addresses the possibility that tones disrupt serial recall of visually presented material in the same way as speech. A stream of changing tones is as disruptive of visual serial recall as 4 syllables (Experiments 1 and 2). Similar effects were also shown with a repeated syllable that changed only in pitch (Experiment 3). Just as for speech, the effect of tones is not at encoding but during storage of the serial lists (Experiments 4 and 5). The results suggest that speech and tones are equipotent in their capacity to disrupt short-term memory. A «blackboard» model of working memory to account for the effects is outlined
Cited by (21)
- Working memory: Looking back and looking forwardBaddeley, Alan · 2003 · Nature Reviews Neuroscience
- Working Memory: Theories, Models, and ControversiesBaddeley, Alan · 2012 · Annual Review of Psychology
- Dorsal and ventral streams: a framework for understanding aspects of the functional anatomy of languageHickok, Gregory, Poeppel, David · 2004 · Cognition
- Auditory-motor interaction revealed by fMRI: speech, music, and working memory in area SptHickok, Gregory, Buchsbaum, Bradley, Humphries, Colin, Muftuler, Tugan · 2003 · Journal of Cognitive Neuroscience
- Short-Term Memory for Serial Order: The Start-End ModelHenson, Richard N.A. · 1998 · Cognitive 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: Irrelevant tones produce an irrelevant speech effect: Implications for phonological coding in working memory. https://app.cochat.ai/papers/10.1037//0278-7393.19.2.369
- The paper
- Jones, D. M., & Macken, W. J. (1993). Irrelevant tones produce an irrelevant speech effect: Implications for phonological coding in working memory. Journal of Experimental Psychology Learning Memory and Cognition. https://doi.org/10.1037//0278-7393.19.2.369
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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.