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Abstract Perception changes rapidly and implicitly as a function of passive exposure to speech that samples different acoustic distributions. Past research has shown that this statistical learning generalizes across talkers and, to some extent, new items, but these studies involved listeners’ active engagement in processing statistics-bearing stimuli. In this study, we manipulated the relationship between voice onset time (VOT) and fundamental frequency (F0) to establish distributional regularities either aligned with American English or reversed to create a subtle foreign accent. We then tested whether statistical learning across passive exposure to these distributions generalized to new items never experienced in the accent. Experiment 1 showed statistical learning across passive exposure but no generalization of learning when exposure and test items shared the same initial consonant but differed in vowels (bear/pear → beer/pier) or when they differed in initial consonant but shared distributional regularities across VOT and F0 dimensions (deer/tear → beer/pier). Experiment 2 showed generalization to stimuli that shared the statistics-bearing phoneme (bear/pear → beer/pier), but only when the response set included tokens from both exposure and generalization stimuli. Moreover, statistical learning transferred to influence the subtle acoustics of listeners’ own speech productions but did not generalize to influence productions of stimuli not heard in the accent. In sum, passive exposure is thus sufficient to support statistical learning and its generalization, but task demands modulate this dynamic. Moreover, production does not simply mirror perception: generalization in perception was not accompanied by transfer to production. 

Abstract Communicating with a speaker with a different accent can affect one’s own speech. Despite the strength of evidence for perception-production transfer in speech, the nature of transfer has remained elusive, with variable results regarding the acoustic properties that transfer between speakers and the characteristics of the speakers who exhibit transfer. The current study investigates perception-production transfer through the lens of statistical learning across passive exposure to speech. Participants experienced a short sequence of acoustically variable minimal pair (beer/pier) utterances conveying either an accent or typical American English acoustics, categorized a perceptually ambiguous test stimulus, and then repeated the test stimulus aloud. In thecanonicalcondition, /b/–/p/ fundamental frequency (F0) and voice onset time (VOT) covaried according to typical English patterns. In thereversecondition, the F0xVOT relationship reversed to create an “accent” with speech input regularities atypical of American English. Replicating prior studies, F0 played less of a role in perceptual speech categorization in reverse compared with canonical statistical contexts. Critically, this down-weighting transferred to production, with systematic down-weighting of F0 in listeners’ own speech productions in reverse compared with canonical contexts that was robust across male and female participants. Thus, the mapping of acoustics to speech categories is rapidly adjusted by short-term statistical learning across passive listening and these adjustments transfer to influence listeners’ own speech productions. 

Speech conveys both linguistic messages and a wealth of social and identity information about a talker. This information arrives as complex variations across many acoustic dimensions. Ultimately, speech communication depends on experience within a language community to develop shared long-term knowledge of the mapping from acoustic patterns to the category distinctions that support word recognition, emotion evaluation, and talker identification. A great deal of research has focused on the learning involved in acquiring long-term knowledge to support speech categorization. Inadvertently, this focus may give the impression of a mature learning endpoint. Instead, there seems to be no firm line between perception and learning in speech. The contributions of acoustic dimensions are malleably reweighted continuously as a function of regularities evolving in short-term input. In this way, continuous learning across speech impacts the very nature of the mapping from sensory input to perceived category. This article presents a case study in understanding how incoming sensory input—and the learning that takes place across it—interacts with existing knowledge to drive predictions that tune the system to support future behavior. 

In two experiments (N = 179), we studied the effects of contextual similarity and training mode on the comprehension of new vocabulary. Participants were trained on new vocabulary in blocks of semantically similar, phonologically similar, or unrelated items. Each participant was trained through passive exposure, active comprehension, or active production. Same number of items were trained in clusters of 9 in Experiment 1 and clusters of 3 in Experiment 2, manipulating difficulty during training. Results showed a detrimental and persistent effect of semantic similarity, and a less robust effect of phonological similarity, both of which grew larger over time. We also found a negative and largely independent influence of production mode on learning, which, contrary to the similarity effect, shrank with time. Neither effect was modulated by difficulty at training time. These findings shed further light on the factors influencing new vocabulary learning and open new avenues for larger-scale and classroom-level studies. 

Statistical learning (SL) is typically assumed to be a core mechanism by which organisms learn covarying structures and recurrent patterns in the environment, with the main purpose of facilitating processing of expected events. Within this theoretical framework, the environment is viewed as relatively stable, and SL ‘captures’ the regularities therein through implicit unsupervised learning by mere exposure. Focusing primarily on language— the domain in which SL theory has been most influential—we review evidence that the environment is far from fixed: it is dynamic, in continual flux, and learners are far from passive absorbers of regularities; they interact with their environments, thereby selecting and even altering the patterns they learn from. We therefore argue for an alternative cognitive architecture, where SL serves as a subcomponent of an information foraging (IF) system. IF aims to detect and assimilate novel recurrent patterns in the input that deviate from randomness, for which SL supplies a baseline. The broad implications of this viewpoint and their relevance to recent debates in cognitive neuroscience are discussed. 

Given the fundamental role of working memory (WM) in all domains of cognition, a central question has been whether WM is domain-general. However, the term ‘domain-general’ has been used in different, and sometimes misleading, ways. By reviewing recent evidence and biologically plausible models of WM, we show that the level of domain-generality varies substantially between three facets of WM: in terms of computations, WM is largely domain-general. In terms of neural correlates, it contains both domain-general and domain-specific elements. Finally, in terms of application, it is mostly domain-specific. This variance encourages a shift of focus towards uncovering domain-general computational principles and away from domain-general approaches to the analysis of individual differences and WM training, favoring newer perspectives, such as training-as-skill-learning. 

In two experiments (N = 179), we studied the effect of contextual similarity and training mode on new vocabulary learning. Adult participants were trained on blocks of items that were semantically similar, phonologically similar, or unrelated to one another. Each participant was trained through passive exposure, active comprehension, or active production of the new vocabulary. Exp 1 trained items in clusters of 9, whereas Exp 2 trained the same number of items in clusters of 3. Exp 2 also assessed delayed retention 48-72 hours after training. Results showed a robust and negative impact of semantic similarity and production mode on vocabulary learning. A detrimental effect of phonological similarity was only observed in the delayed test. These results suggest that adding the challenge of resolving similarity-induced competition and articulating the word-form negatively impacts the quick acquisition of new vocabulary.