Auditory temporal assimilation: A discriminant analysis of electrophysiological evidence

Hiroshige Takeichi, Takako Mitsudo, Yoshitaka Nakajima, Gerard B. Remijn, Yoshinobu Goto, Shozo Tobimatsu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

A portion of the data from an event-related potential (ERP) experiment [1] on auditory temporal assimilation [2, 3] was reanalyzed by constructing Gaussian Naïve Bayes Classifiers [4]. In auditory temporal assimilation, two neighboring physically-unequal time intervals marked by three successive tone bursts are illusorily perceived to have the same duration if the two time intervals satisfy a certain relationship. The classifiers could discriminate the subject's task, which was judgment of the equivalence between the two intervals, at an accuracy of 86-96% as well as their subjective judgments to the physically equivalent stimulus at an accuracy of 82-86% from individual ERP average waveforms. Chernoff information [5] provided more consistent interpretations compared with classification errors as to the selection of the component most strongly associated with the perceptual judgment. This may provide us with a simple but somewhat robust neurodecoding scheme.

Original languageEnglish
Title of host publicationNeural Information Processing - 16th International Conference, ICONIP 2009, Proceedings
Pages299-308
Number of pages10
EditionPART 2
DOIs
Publication statusPublished - Dec 1 2009
Event16th International Conference on Neural Information Processing, ICONIP 2009 - Bangkok, Thailand
Duration: Dec 1 2009Dec 5 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5864 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th International Conference on Neural Information Processing, ICONIP 2009
CountryThailand
CityBangkok
Period12/1/0912/5/09

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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