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Detecting temporal changes in acoustic scenes: The variable benefit of selective attention. | LitMetric

Detecting temporal changes in acoustic scenes: The variable benefit of selective attention.

Hear Res

Institut de Neurosciences Cognitives et Intégratives d'Aquitaine, CNRS, Université de Bordeaux, 146 rue Leo-Saignat, F-33076, Bordeaux, France; Institut Polytechnique de Bordeaux, Ecole Nationale Supérieure de Cognitique, 109 avenue Roul, F-33400, Talence, France. Electronic address: catherine.se

Published: September 2017


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Article Abstract

Four experiments investigated change detection in acoustic scenes consisting of a sum of five amplitude-modulated pure tones. As the tones were about 0.7 octave apart and were amplitude-modulated with different frequencies (in the range 2-32 Hz), they were perceived as separate streams. Listeners had to detect a change in the frequency (experiments 1 and 2) or the shape (experiments 3 and 4) of the modulation of one of the five tones, in the presence of an informative cue orienting selective attention either before the scene (pre-cue) or after it (post-cue). The changes left intensity unchanged and were not detectable in the spectral (tonotopic) domain. Performance was much better with pre-cues than with post-cues. Thus, change deafness was manifest in the absence of an appropriate focusing of attention when the change occurred, even though the streams and the changes to be detected were acoustically very simple (in contrast to the conditions used in previous demonstrations of change deafness). In one case, the results were consistent with a model based on the assumption that change detection was possible if and only if attention was endogenously focused on a single tone. However, it was also found that changes resulting in a steepening of amplitude rises were to some extent able to draw attention exogenously. Change detection was not markedly facilitated when the change produced a discontinuity in the modulation domain, contrary to what could be expected from the perspective of predictive coding.

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http://dx.doi.org/10.1016/j.heares.2017.07.013DOI Listing

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