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Predicting Choices and Timings of Phoneme Categorization with a Perceptual Decision Model of Phonemic Processing
의사결정 모델을 이용한 음소 분류과제의 선택과 반응시간 예측

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Authors
김진영
Advisor
이상훈
Major
자연과학대학 뇌인지과학과
Issue Date
2013-02
Publisher
서울대학교 대학원
Keywords
phoneme categorizationperceptual decisionspeech perceptionsensory encodinglikelihood decodingneural model
Description
학위논문 (석사)-- 서울대학교 대학원 : 뇌인지과학과, 2013. 2. 이상훈.
Abstract
Despite crucial roles of pre-lexical units in speech perception, modeling efforts so far have been heavily focused on information processing at lexical or post-lexical stages, impeding the mechanistic investigation of speech perception. Given this dearth of frameworks for studying pre-lexical units, the current study proposes a system-level neural model for phoneme classification. A lynchpin idea behind the proposed model is that the brain represents phonemes as probabilistic quantities, likelihoods. With this idea, our model bridges three well-known canonical computations in the brain – sensory encoding, likelihood decoding and evidence accumulation - along a cascade hierarchy of neural processing towards generating inputs to a next stage of speech perception. At the initial stage, sensory neurons with different tuning curves for physical properties relevant to phoneme discrimination compute individual likelihoods for the presence of those properties. Phoneme neurons at the following stage compute likelihoods for specific phonemes by summing the outputs of those sensory encoding neurons with weighting curves tuned for their preferred phonemes. At the final stage, evidence-accumulation neurons compute and accumulate over time evidence to reach a discrete phoneme classification by integrating outputs of phoneme neurons in a task-optimal manner over time. The accumulation-to-bound mechanism operating at this stage translates probabilistic information represented in the phoneme neurons output into concrete choices at a certain time. This translation allowed us to test the empirical viability of our model by assessing its capability of predicting actual patterns of choice fractions and reaction times exhibited by human listeners engaging in phoneme classification under various listening conditions. Using a small number of parameters, the model predicted not only the static, categorical structure of phoneme classification as a function of physical stimulus property, but also the adaptation-induced, dynamic changes in classification on an identical stimulus. Furthermore, the model was flexible enough to cover the wide range of individual differences in phoneme classification behavior. With these behavioral constraints in conjunction with neural and computational constraints exercised in model construction, our model provides a framework for studying neural mechanisms underlying initial stages of speech processing by generating hypotheses and predictions that are testable by neurophysiological and behavioral experiments.
Language
English
URI
https://hdl.handle.net/10371/131699
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College of Natural Sciences (자연과학대학)Brain and Cognitive Sciences (뇌인지과학과)Theses (Master's Degree_뇌인지과학과)
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