Individual Differences in Distributional Learning for Speech: What's Ideal for Ideal Observers?

Purpose: Speech perception is facilitated by listeners' ability to dynamically modify the mapping to speech sounds given systematic variation in speech input. For example, the degree to which listeners show categorical perception of speech input changes as a function of distributional variability in the input, with perception becoming less categorical as the input, becomes more variable. Here, we test the hypothesis that higher level receptive language ability is linked to the ability to adapt to low-level distributional cues in speech input. Method: Listeners (n = 58) completed a distributional learning task consisting of 2 blocks of phonetic categorization for words beginning with /g/ and /k/. In 1 block, the distributions of voice onset time values specifying /g/ and /k/ had narrow variances (i.e., minimal variability). In the other block, the distributions of voice onset times specifying /g/ and /k/ had wider variances (i.e., increased variability). In addition, all listeners completed an assessment battery for receptive language, nonverbal intelligence, and reading fluency. Results: As predicted by an ideal observer computational framework, the participants in aggregate showed identification responses that were more categorical for consistent compared to inconsistent input, indicative of distributional learning. However, the magnitude of learning across participants showed wide individual variability, which was predicted by receptive language ability but not by nonverbal intelligence or by reading fluency. Conclusion: The results suggest that individual differences in distributional learning for speech are linked, at least in part, to receptive language ability, reflecting a decreased ability among those with weaker receptive language to capitalize on consistent input distributions.