Neural dynamics of perceptual order and context effects for variable-rate speech syllables.
How does the brain extract invariant properties of variable-rate speech? A neural model, called PHONET, is developed to explain aspects of this process and, along the way, data about perceptual context effects. For example, in consonant-vowel (CV) syllables, such as /ba/ and /wa/, an increase in the duration of the vowel can cause a switch in the percept of the preceding consonant from /w/ to /b/ (J.L. Miller & Liberman, 1979). The frequency extent of the initial formant transitions of fixed duration also influences the percept (Schwab, Sawusch, & Nusbaum, 1981). PHONET quantitatively simulates over 98% of the variance in these data, using a single set of parameters. The model also qualitatively explains many data about other perceptual context effects. In the model, C and V inputs are filtered by parallel auditory streams that respond preferentially to the transient and sustained properties of the acoustic signal before being stored in parallel working memories. A lateral inhibitory network of onset- and rate-sensitive cells in the transient channel extracts measures of frequency transition rate and extent. Greater activation of the transient stream can increase the processing rate in the sustained stream via a cross-stream automatic gain control interaction. The stored activities across these gain-controlled working memories provide a basis for rate-invariant perception, since the transient-to-sustained gain control tends to preserve the relative activities across the transient and sustained working memories as speech rate changes. Comparisons with alternative models tested suggest that the fit cannot be attributed to the simplicity of the data. Brain analogues of model cell types are described.
Boardman
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I
Boston University, Massachusetts, USA.
Grossberg
S
S
Myers
C
C
Cohen
M
M
D001288:Attention / Q000502:physiology; D001306:Auditory Pathways / Q000502:physiology; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D006801:Humans; D011939:Mental Recall; D016571:Neural Networks, Computer; D010700:Phonetics; D011578:Psycholinguistics; D013061:Speech Acoustics; D013067:Speech Perception / Q000502:physiology*