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Clinical Trial
. 2011 Oct;280(1-2):228-35.
doi: 10.1016/j.heares.2011.06.001. Epub 2011 Jun 13.

The role of temporal regularity in auditory segregation

Affiliations
Clinical Trial

The role of temporal regularity in auditory segregation

Lefkothea-Vasiliki Andreou et al. Hear Res. 2011 Oct.

Abstract

The idea that predictive modelling and extraction of regularities plays a pivotal role in auditory segregation has recently attracted considerable attention. The present study investigated the effect of one basic form of regularity, rhythmic regularity, on auditory stream segregation. We departed from the classic streaming paradigm and developed a new stimulus, Rand-AB, consisting of two, concurrently presented, temporally uncorrelated, tone sequences (with frequencies A and B). To evaluate segregation, we used an objective measure of the extent to which listeners are able to selectively attend to one of the sequences in the presence of the other. Performance was quantified on a difficult pattern detection task which involves detecting a rarely occurring pattern of amplitude modulation applied to three consecutive A or B tones. In all cases the attended sequence was temporally irregular (with a random inter-tone-interval (ITI) between 100 and 400 ms) and the regularity status of the competing sequence was set to one of four conditions: (1) random ITI between 100 and 400 ms (2) isochronous with ITI = 400 ms. (3) isochronous with ITI = 250 ms (equal to the mean rate of the attended sequence) (4) isochronous with ITI = 100 ms. For a frequency separation of 2 (but not 4) semi tones we observed improved performance in conditions (3) and (4) relative to (1), suggesting that stream segregation is facilitated when the distracter sequence is temporally regular, but that the effect of temporal regularity as a cue for segregation is limited to relatively fast rates and to situations where frequency separation is insufficient for segregation. These findings provide new evidence to support models of streaming that involve segregation based on the formation of predictive models.

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