Nevertheless, it persists: Dimension-based statistical learning and normalization of speech impact different levels...

Speech is notoriously variable, with no simple mapping from acoustics to linguistically-meaningful units like words and phonemes. Empirical research on this theoretically central issue establishes at least two classes of perceptual phenomena that accommodate acoustic variability: normalization and perceptual learning. Intriguingly, perceptual learning is supported by learning across acoustic variability, but normalization is thought to counteract acoustic variability leaving open questions about how these two phenomena might interact. Here, we examine the joint impact of normalization and perceptual learning on how acoustic dimensions map to vowel categories. As listeners categorized nonwords as setch or satch, they experienced a shift in short-term distributional regularities across the vowels' acoustic dimensions. Introduction of this 'artificial accent' resulted in a shift in the contribution of vowel duration in categorization. Although this dimension-based statistical learning impacted the influence of vowel duration on vowel categorization, the duration of these very same vowels nonetheless maintained a consistent influence on categorization of a subsequent consonant via duration contrast, a form of normalization. Thus, vowel duration had a duplex role consistent with normalization and perceptual learning operating on distinct levels in the processing hierarchy. We posit that whereas normalization operates across auditory dimensions, dimension-based statistical learning impacts the connection weights among auditory dimensions and phonetic categories.