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Learning to detect a tone in unpredictable noise
Author(s) -
Pete R. Jones,
David R. Moore,
Daniel E. Shub,
Sygal Amitay
Publication year - 2014
Publication title -
the journal of the acoustical society of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.619
H-Index - 187
eISSN - 1520-8524
pISSN - 0001-4966
DOI - 10.1121/1.4865267
Subject(s) - tone (literature) , masking (illustration) , noise (video) , task (project management) , computer science , acoustics , correlation , speech recognition , audiology , mathematics , psychology , artificial intelligence , physics , medicine , art , image (mathematics) , literature , management , geometry , economics , visual arts
Eight normal-hearing listeners practiced a tone-detection task in which a 1-kHz target was masked by a spectrally unpredictable multitone complex. Consistent learning was observed, with mean masking decreasing by 6.4 dB over five sessions (4500 trials). Reverse-correlation was used to estimate how listeners weighted each spectral region. Weight-vectors approximated the ideal more closely after practice, indicating that listeners were learning to attend selectively to the task relevant information. Once changes in weights were accounted for, no changes in internal noise (psychometric slope) were observed. It is concluded that this task elicits robust learning, which can be understood primarily as improved selective attention.

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