A perceptual learning experiment provides evidence that the mental lexicon cannot consist solely of detailed acoustic traces of recognition episodes. In a training lexical decision phase, listeners heard an ambiguous [f–s] fricative sound, replacing either [f] or [s] in words. In a test phase, listeners then made lexical decisions to visual targets following auditory primes. Critical materials were minimal pairs that could be a word with either [f] or [s] (cf. English knife–nice), none of which had been heard in training. (...) Listeners interpreted the minimal pair words differently in the second phase according to the training received in the first phase. Therefore, lexically mediated retuning of phoneme perception not only influences categorical decisions about fricatives (Norris, McQueen, & Cutler, 2003), but also benefits recognition of words outside the training set. The observed generalization across words suggests that this retuning occurs prelexically. Therefore, lexical processing involves sublexical phonological abstraction, not only accumulation of acoustic episodes. (shrink)

Two experiments investigated the mechanism by which listeners adjust their interpretation of accented speech that is similar to a regional dialect of American English. Only a subset of the vowels of English (the front vowels) were shifted during adaptation, which consisted of listening to a 20‐min segment of the “Wizard of Oz.” Compared to a baseline (unadapted) condition, listeners showed significant adaptation to the accented speech, as indexed by increased word judgments on a lexical decision task. Adaptation also generalized to (...) test words that had not been presented in the accented passage but that contained the shifted vowels. A control experiment showed that the adaptation effect was specific to the direction of the shift in the vowel space and not to a general relaxation of the criterion for what constitutes a good exemplar of the accented vowel category. Taken together, these results provide evidence for a context‐specific vowel adaptation mechanism that enables a listener to adjust to the dialect of a particular talker. (shrink)

An important distinction between phonology and syntax has been overlooked. All phonological patterns belong to the regular region of the Chomsky Hierarchy, but not all syntactic patterns do. We argue that the hypothesis that humans employ distinct learning mechanisms for phonology and syntax currently offers the best explanation for this difference.

This paper examines whether adults can adapt to novel accents of their native language that contain unfamiliar context-dependent phonological alternations. In two experiments, French participants listen to short stories read in accented speech. Their knowledge of the accents is then tested in a forced-choice identification task. In Experiment 1, two groups of listeners are exposed to newly created French accents in which certain vowels harmonize or disharmonize, respectively, to the rounding of the preceding vowel. Despite the cross-linguistic predominance of vowel (...) harmony over disharmony, the two groups adapt equally well to both accents, suggesting that this typological difference is not reflected in perceptual learning. Experiment 2 further explores the mechanism underlying this type of phonological learning. Participants are exposed to an accent in which some vowels harmonize and others disharmonize, yielding an increased featural complexity. They adapt less well to this regularity, showing that adaptation to novel accents involves feature-based inferences. (shrink)

Statistical learning refers to the ability to identify structure in the input based on its statistical properties. For many linguistic structures, the relevant statistical features are distributional: They are related to the frequency and variability of exemplars in the input. These distributional regularities have been suggested to play a role in many different aspects of language learning, including phonetic categories, using phonemic distinctions in word learning, and discovering non-adjacent relations. On the surface, these different aspects share few commonalities. Despite this, (...) we demonstrate that the same computational framework can account for learning in all of these tasks. These results support two conclusions. The first is that much, and perhaps all, of distributional statistical learning can be explained by the same underlying set of processes. The second is that some aspects of language can be learned due to domain-general characteristics of memory. (shrink)

We reply to McQueen's commentary by comparing the parsimony of his account of relevant data and the computational model he favors with the explanation and model we favor. His account requires multiple independent explanations and mechanisms. Ours requires one: lexical feedback.