Аннотация
Our native language has a lifelong effect on how we perceive speech
sounds. Behaviorally, this is manifested as categorical perception,
but the neural mechanisms underlying this phenomenon are still unknown.
Here, we constructed a computational model of categorical perception,
following principles consistent with infant speech learning. A self-organizing
network was exposed to a statistical distribution of speech input
presented as neural activity patterns of the auditory periphery,
resembling the way sound arrives to the human brain. In the resulting
neural map, categorical perception emerges from most single neurons
of the model being maximally activated by prototypical speech sounds,
while the largest variability in activity is produced at category
boundaries. Consequently, regions in the vicinity of prototypes become
perceptually compressed, and regions at category boundaries become
expanded. Thus, the present study offers a unifying framework for
explaining the neural basis of the warping of perceptual space associated
with categorical perception.
- (computer),neurological,neurons,neurons:
- brain,brain:
- language,humans,infant,learning,learning:
- networks
- perception,language,perception,speech
- perception,speech
- perception:
- physiology,categorical
- physiology,child
- physiology,mental
- physiology,models,neural
- physiology,phonetics,sound
- processes,mental
- processes:
- spectrography,speech,speech
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