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Distinguishing Neural Adaptation and Predictive Coding Hypotheses in Auditory Change Detection

Overview of attention for article published in Brain Topography, October 2016
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Article details
Title
Distinguishing Neural Adaptation and Predictive Coding Hypotheses in Auditory Change Detection
Published in
Brain Topography, October 2016
DOI 10.1007/s10548-016-0529-8
Pubmed ID
Authors
Abstract

The auditory mismatch negativity (MMN) component of event-related potentials (ERPs) has served as a neural index of auditory change detection. MMN is elicited by presentation of infrequent (deviant) sounds randomly interspersed among frequent (standard) sounds. Deviants elicit a larger negative deflection in the ERP waveform compared to the standard. There is considerable debate as to whether the neural mechanism of this change detection response is due to release from neural adaptation (neural adaptation hypothesis) or from a prediction error signal (predictive coding hypothesis). Previous studies have not been able to distinguish between these explanations because paradigms typically confound the two. The current study disambiguated effects of stimulus-specific adaptation from expectation violation using a unique stimulus design that compared expectation violation responses that did and did not involve stimulus change. The expectation violation response without the stimulus change differed in timing, scalp distribution, and attentional modulation from the more typical MMN response. There is insufficient evidence from the current study to suggest that the negative deflection elicited by the expectation violation alone includes the MMN. Thus, we offer a novel hypothesis that the expectation violation response reflects a fundamentally different neural substrate than that attributed to the canonical MMN.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 78 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 15 19%
Student > Ph. D. Student 14 18%
Student > Master 12 15%
Student > Doctoral Student 6 8%
Student > Bachelor 5 6%
Other 7 9%
Unknown 19 24%
Readers by discipline
Readers by discipline Count As %
Neuroscience 27 35%
Psychology 10 13%
Agricultural and Biological Sciences 5 6%
Linguistics 4 5%
Nursing and Health Professions 2 3%
Other 7 9%
Unknown 23 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 October 2016.
All research outputs
#22,606,555
of 27,672,907 outputs
Outputs from Brain Topography
#411
of 549 outputs
Outputs of similar age
#241,624
of 313,684 outputs
Outputs of similar age from Brain Topography
#9
of 12 outputs
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So far Altmetric has tracked 549 research outputs from this source. They receive a mean Attention Score of 4.4. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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