Neural Measures Reveal Implicit Learning during Language Processing.

Language input is highly variable; phonological, lexical, and syntactic features vary systematically across different speakers, geographic regions, and social contexts. Previous evidence shows that language users are sensitive to these contextual changes and that they can rapidly adapt to local regularities. For example, listeners quickly adjust to accented speech, facilitating comprehension. It has been proposed that this type of adaptation is a form of implicit learning. This study examined a similar type of adaptation, syntactic adaptation, to address two issues: (1) whether language comprehenders are sensitive to a subtle probabilistic contingency between an extraneous feature (font color) and syntactic structure and (2) whether this sensitivity should be attributed to implicit learning. Participants read a large set of sentences, 40% of which were garden-path sentences containing temporary syntactic ambiguities. Critically, but unbeknownst to participants, font color probabilistically predicted the presence of a garden-path structure, with 75% of garden-path sentences (and 25% of normative sentences) appearing in a given font color. ERPs were recorded during sentence processing. Almost all participants indicated no conscious awareness of the relationship between font color and sentence structure. Nonetheless, after sufficient time to learn this relationship, ERPs time-locked to the point of syntactic ambiguity resolution in garden-path sentences differed significantly as a function of font color. End-of-sentence grammaticality judgments were also influenced by font color, suggesting that a match between font color and sentence structure increased processing fluency. Overall, these findings indicate that participants can implicitly detect subtle co-occurrences between physical features of sentences and abstract, syntactic properties, supporting the notion that implicit learning mechanisms are generally operative during online language processing

An exemplar model should be able to explain all syntactic priming phenomena : a commentary on Ambridge (2020)

The authors argue that Ambridge’s radical exemplar account of language cannot clearly explain all syntactic priming evidence, such as inverse preference effects (greater priming for less frequent structures), and the contrast between short-lived lexical boost and long-lived abstract priming. Moreover, without recourse to a level of abstract syntactic structure, Ambridge’s account cannot explain abstract priming in amnesia patients or cross-linguistic priming. Instead, the authors argue that abstract representations remain the more parsimonious account for the wide variety of syntactic priming phenomena

Children's acquisition of science terms: does fast mapping work?

About the book: This proceedings contains 99 selected papers from the 8th Conference of the International Association for the Study of Child Language (IASCL) held in Donostia-San Sebastián in the Spanish Basque Country in July 1999. The proceedings includes the plenary addresses by Jean-Paul Bronckart, Brian MacWhinney, and Miquel Siguan. The other 96 papers are organized into sections on bilingualism, discourse, phonology, language disorders, lexicon, morphology, syntax, and signed languages. Several of these sections include symposia with introductions as well as individual papers

Toward a prenominal syntax? A brief look at statistical alternations

This pilot study aims to show that people indeed use subconscious statistical processing to aid in the acquisition of constructions, and frequent form-function mappings emerge as structures that work well together. The current study is a modified replication of Wells et.al. (2009), in which frequency distributions of NL-English speakers' relative clauses were manipulated, causing them to more quickly process a less frequent, irregular form. The construction under consideration here is the prenominal clause, rare in English, but attested in many primary languages. The hypothesis was that, given minimal exposure to this construction, subjects would statistically re-categorize their linguistic systems. The infrequent/irregular prenominal phrase was compared with the frequent/regular postnominal RC. Pre- and Post-Tests recorded participants’ self-paced reading times. During two brief Experience Blocks, spaced two days apart, subjects received limited exposure to both target structures. Reading times in the prenominal structure decreased more than that of the RC, for each subject, indicating faster processing. A preliminary analysis of results shows that all subjects reanalyzed the statistical distributions of the prenominal clause.Ope

An integrated theory of language production and comprehension

Currently, production and comprehension are regarded as quite distinct in accounts of language processing. In rejecting this dichotomy, we instead assert that producing and understanding are interwoven, and that this interweaving is what enables people to predict themselves and each other. We start by noting that production and comprehension are forms of action and action perception. We then consider the evidence for interweaving in action, action perception, and joint action, and explain such evidence in terms of prediction. Specifically, we assume that actors construct forward models of their actions before they execute those actions, and that perceivers of others' actions covertly imitate those actions, then construct forward models of those actions. We use these accounts of action, action perception, and joint action to develop accounts of production, comprehension, and interactive language. Importantly, they incorporate well-defined levels of linguistic representation (such as semantics, syntax, and phonology). We show (a) how speakers and comprehenders use covert imitation and forward modeling to make predictions at these levels of representation, (b) how they interweave production and comprehension processes, and (c) how they use these predictions to monitor the upcoming utterances. We show how these accounts explain a range of behavioral and neuroscientific data on language processing and discuss some of the implications of our proposal