Interstimulus-interval dependence in auditory cortex reflects perceptual organization of streams under informational masking [data]

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Authors: Clara Raudonat, Eva Doroszewski, Alexander Gutschalk
Cited by: 0

Abstract

The perceptual saliency of a regular sound sequence embedded into a well-chosen multi-tone-masker background fluctuates despite the absence of change in the physical stimulus. When such tone streams are perceived, each tone of the stream evokes a negative-going response referred to as awareness-related negativity (ARN). Here we probe if the ARN amplitude shows a dependence on the inter-stimulus interval (ISI) within perceived tone streams, similar to the N1 response evoked by unmasked tone streams independently of a task. Isochronous target streams were presented under multi-tone masking with ISIs of 500, 850, and 1200 ms, while listeners indicated when they perceived the onset of the target streams. A control experiment applied the same tone streams without masking in the presence of a second, narrow-band noise stream. In two separate runs, listeners either attended the tone streams and indicated their beginning and ending, or they attended the distractor streams, in which they performed a deviance detection task. Activity in auditory cortex was recorded with magnetoencephalography. Experiment 1 revealed a significant amplitude increment of the ARN with increasing ISI. In Experiment 2, the known amplitude dependence of the N1 was observed for attended and unattended runs alike. Critically, the negative difference waveform (Nd) between attended and unattended runs did not increase with the ISI. These results support an interpretation of the ARN as being related to the neural formation of an auditory stream and not as a pure reflection of attentional response enhancement.

Fields of Study & Subjects

Neuroscience and Music Perception Hearing Cochlea Tinnitus Genetics Multisensory perception and integration

Publication details

Journal / source
University Library Heidelberg
Publication date
15 January 2026
Document type
Dataset