Individual differences in artificial and natural language statistical learning.

Statistical learning (SL) is considered a cornerstone of cognition. While decades of research have unveiled the remarkable breadth of structures that participants can learn from statistical patterns in experimental contexts, how this ability interfaces with real-world cognitive phenomena remains inconclusive. These mixed results may arise from the fact that SL is often treated as a general ability that operates uniformly across all domains, typically assuming that sensitivity to one kind of regularity implies equal sensitivity to others. In a preregistered study, we sought to clarify the link between SL and language by aligning the type of structure being processed in each task. We focused on the learning of trigram patterns using artificial and natural language statistics, to evaluate whether SL predicts sensitivity to comparable structures in natural speech. Adults were trained and tested on an artificial language incorporating statistically-defined syllable trigrams. We then evaluated their sensitivity to similar statistical structures in natural language using a multiword chunking task, which examines serial recall of high-frequency word trigrams-one of the building blocks of language. Participants' aptitude in learning artificial syllable trigrams positively correlated with their sensitivity to high-frequency word trigrams in natural language, suggesting that similar computations span learning across both tasks. Short-term SL taps into key aspects of long-term language acquisition when the statistical structures-and the computations used to process them-are comparable. Better aligning the specific statistical patterning across tasks may therefore provide an important steppingstone toward elucidating the relationship between SL and cognition at large.

Multiword units lead to errors of commission in children's spontaneous production: "What corpus data can tell us?*".

Psycholinguistic research over the past decade has suggested that children's linguistic knowledge includes dedicated representations for frequently-encountered multiword sequences. Important evidence for this comes from studies of children's production: it has been repeatedly demonstrated that children's rate of speech errors is greater for word sequences that are infrequent and thus unfamiliar to them than for those that are frequent. In this study, we investigate whether children's knowledge of multiword sequences can explain a phenomenon that has long represented a key theoretical fault line in the study of language development: errors of subject-auxiliary non-inversion in question production (e.g., "why we can't go outside?*"). In doing so we consider a type of error that has been ignored in discussion of multiword sequences to date. Previous work has focused on errors of omission - an absence of accurate productions for infrequent phrases. However, if children make use of dedicated representations for frequent sequences of words in their productions, we might also expect to see errors of commission - the appearance of frequent phrases in children's speech even when such phrases are not appropriate. Through a series of corpus analyses, we provide the first evidence that the global input frequency of multiword sequences (e.g., "she is going" as it appears in declarative utterances) is a valuable predictor of their errorful appearance (e.g., the uninverted question "what she is going to do?*") in naturalistic speech. This finding, we argue, constitutes powerful evidence that multiword sequences can be represented as linguistic units in their own right.

Testing a computational model of causative overgeneralizations: Child judgment and production data from English, Hebrew, Hindi, Japanese and K'iche'.

How do language learners avoid the production of verb argument structure overgeneralization errors ( *The clown laughed the man c.f. The clown made the man laugh), while retaining the ability to apply such generalizations productively when appropriate? This question has long been seen as one that is both particularly central to acquisition research and particularly challenging. Focussing on causative overgeneralization errors of this type, a previous study reported a computational model that learns, on the basis of corpus data and human-derived verb-semantic-feature ratings, to predict adults' by-verb preferences for less- versus more-transparent causative forms (e.g., * The clown laughed the man vs The clown made the man laugh) across English, Hebrew, Hindi, Japanese and K'iche Mayan. Here, we tested the ability of this model (and an expanded version with multiple hidden layers) to explain binary grammaticality judgment data from children aged 4;0-5;0, and elicited-production data from children aged 4;0-5;0 and 5;6-6;6 ( N=48 per language). In general, the model successfully simulated both children's judgment and production data, with correlations of r=0.5-0.6 and r=0.75-0.85, respectively, and also generalized to unseen verbs. Importantly, learners of all five languages showed some evidence of making the types of overgeneralization errors - in both judgments and production - previously observed in naturalistic studies of English (e.g., *I'm dancing it). Together with previous findings, the present study demonstrates that a simple learning model can explain (a) adults' continuous judgment data, (b) children's binary judgment data and (c) children's production data (with no training of these datasets), and therefore constitutes a plausible mechanistic account of the acquisition of verbs' argument structure restrictions.

Meaningfulness Beats Frequency in Multiword Chunk Processing.

Whereas a growing bulk of work has demonstrated that both adults and children are sensitive to frequently occurring word sequences, little is known about the potential role of meaning in the processing of such multiword chunks. Here, we take a first step toward assessing the contribution of meaningfulness in the processing of multiword sequences, using items that varied in chunk meaningfulness. In a phrasal-decision study, we compared reaction times for triads of three-word sequences, corresponding to idiomatic expressions, compositional phrases, and phrasal fragments, while controlling for phrase and substring frequencies. Chunk meaningfulness, as assessed by a separate subjective rating study, was found to speed up decision times for all three types of strings: The more meaningful a multiword sequence was judged to be, the faster it was processed, independently of whether it was idiomatic, compositional in nature, or a phrasal fragment. These results highlight the importance of taking meaning into account when considering the processing of multiword chunks, consistent with predictions of construction-based approaches to language.

Statistically Induced Chunking Recall: A Memory-Based Approach to Statistical Learning.

The computations involved in statistical learning have long been debated. Here, we build on work suggesting that a basic memory process, chunking, may account for the processing of statistical regularities into larger units. Drawing on methods from the memory literature, we developed a novel paradigm to test statistical learning by leveraging a robust phenomenon observed in serial recall tasks: that short-term memory is fundamentally shaped by long-term distributional learning. In the statistically induced chunking recall (SICR) task, participants are exposed to an artificial language, using a standard statistical learning exposure phase. Afterward, they recall strings of syllables that either follow the statistics of the artificial language or comprise the same syllables presented in a random order. We hypothesized that if individuals had chunked the artificial language into word-like units, then the statistically structured items would be more accurately recalled relative to the random controls. Our results demonstrate that SICR effectively captures learning in both the auditory and visual modalities, with participants displaying significantly improved recall of the statistically structured items, and even recall specific trigram chunks from the input. SICR also exhibits greater test-retest reliability in the auditory modality and sensitivity to individual differences in both modalities than the standard two-alternative forced-choice task. These results thereby provide key empirical support to the chunking account of statistical learning and contribute a valuable new tool to the literature.

The crosslinguistic acquisition of sentence structure: Computational modeling and grammaticality judgments from adult and child speakers of English, Japanese, Hindi, Hebrew and K'iche'.

This preregistered study tested three theoretical proposals for how children form productive yet restricted linguistic generalizations, avoiding errors such as *The clown laughed the man, across three age groups (5-6 years, 9-10 years, adults) and five languages (English, Japanese, Hindi, Hebrew and K'iche'). Participants rated, on a five-point scale, correct and ungrammatical sentences describing events of causation (e.g., *Someone laughed the man; Someone made the man laugh; Someone broke the truck; ?Someone made the truck break). The verb-semantics hypothesis predicts that, for all languages, by-verb differences in acceptability ratings will be predicted by the extent to which the causing and caused event (e.g., amusing and laughing) merge conceptually into a single event (as rated by separate groups of adult participants). The entrenchment and preemption hypotheses predict, for all languages, that by-verb differences in acceptability ratings will be predicted by, respectively, the verb's relative overall frequency, and frequency in nearly-synonymous constructions (e.g., X made Y laugh for *Someone laughed the man). Analysis using mixed effects models revealed that entrenchment/preemption effects (which could not be distinguished due to collinearity) were observed for all age groups and all languages except K'iche', which suffered from a thin corpus and showed only preemption sporadically. All languages showed effects of event-merge semantics, except K'iche' which showed only effects of supplementary semantic predictors. We end by presenting a computational model which successfully simulates this pattern of results in a single discriminative-learning mechanism, achieving by-verb correlations of around r = 0.75 with human judgment data.

Language learning as language use: A cross-linguistic model of child language development.

While usage-based approaches to language development enjoy considerable support from computational studies, there have been few attempts to answer a key computational challenge posed by usage-based theory: the successful modeling of language learning as language use. We present a usage-based computational model of language acquisition which learns in a purely incremental fashion, through online processing based on chunking, and which offers broad, cross-linguistic coverage while uniting key aspects of comprehension and production within a single framework. The model's design reflects memory constraints imposed by the real-time nature of language processing, and is inspired by psycholinguistic evidence for children's sensitivity to the distributional properties of multiword sequences and for shallow language comprehension based on local information. It learns from corpora of child-directed speech, chunking incoming words together to incrementally build an item-based "shallow parse." When the model encounters an utterance made by the target child, it attempts to generate an identical utterance using the same chunks and statistics involved during comprehension. High performance is achieved on both comprehension- and production-related tasks: the model's shallow parsing is evaluated across 79 single-child corpora spanning English, French, and German, while its production performance is evaluated across over 200 single-child corpora representing 29 languages from the CHILDES database. The model also succeeds in capturing findings from children's production of complex sentence types. Together, our modeling results suggest that much of children's early linguistic behavior may be supported by item-based learning through online processing of simple distributional cues, consistent with the notion that acquisition can be understood as learning to process language. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Computational Investigations of Multiword Chunks in Language Learning.

Second-language learners rarely arrive at native proficiency in a number of linguistic domains, including morphological and syntactic processing. Previous approaches to understanding the different outcomes of first- versus second-language learning have focused on cognitive and neural factors. In contrast, we explore the possibility that children and adults may rely on different linguistic units throughout the course of language learning, with specific focus on the granularity of those units. Following recent psycholinguistic evidence for the role of multiword chunks in online language processing, we explore the hypothesis that children rely more heavily on multiword units in language learning than do adults learning a second language. To this end, we take an initial step toward using large-scale, corpus-based computational modeling as a tool for exploring the granularity of speakers' linguistic units. Employing a computational model of language learning, the Chunk-Based Learner, we compare the usefulness of chunk-based knowledge in accounting for the speech of second-language learners versus children and adults speaking their first language. Our findings suggest that while multiword units are likely to play a role in second-language learning, adults may learn less useful chunks, rely on them to a lesser extent, and arrive at them through different means than children learning a first language.

Prospects for usage-based computational models of grammatical development: argument structure and semantic roles.

UNLABELLED: The computational modeling of language development has enabled researchers to make impressive strides toward achieving a comprehensive psychological account of the processes and mechanisms whereby children acquire their mother tongues. Nevertheless, the field's primary focus on distributional information has lead to little progress in elucidating the processes by which children learn to compute meanings beyond the level of single words. This lack of psychologically motivated computational work on semantics poses an important challenge for usage-based computational accounts of acquisition in particular, which hold that grammatical development is closely tied to meaning. In the present review, we trace some initial steps toward answering this challenge through a survey of existing computational models of grammatical development that incorporate semantic information to learn to assign thematic roles and acquire argument structure. We argue that the time is ripe for usage-based computational accounts of grammatical development to move beyond purely distributional features of the input, and to incorporate information about the objects and actions observable in the learning environment. To conclude, we sketch possible avenues for extending previous approaches to modeling the role of semantics in grammatical development. WIREs Cogn Sci 2014, 5:489-499. doi: 10.1002/wcs.1295 For further resources related to this article, please visit the WIREs website. CONFLICT OF INTEREST: The authors have declared no conflicts of interest for this article.

Toward a unified account of comprehension and production in language development.

Although Pickering & Garrod (P&G) argue convincingly for a unified system for language comprehension and production, they fail to explain how such a system might develop. Using a recent computational model of language acquisition as an example, we sketch a developmental perspective on the integration of comprehension and production. We conclude that only through development can we fully understand the intertwined nature of comprehension and production in adult processing.

Perception and bias in the processing of compound versus phrasal stress: evidence from event-related brain potentials.

Previous research using picture/word matching tasks has demonstrated a tendency to incorrectly interpret phrasally stressed strings as compounds. Using event-related potentials, we sought to determine whether this pattern stems from poor perceptual sensitivity to the compound/phrasal stress distinction, or from a post-perceptual bias in behavioral response selection. A secondary aim was to gain insight into the role played by contrastive stress patterns in online sentence comprehension. The behavioral results replicated previous findings of a preference for compounds, but the electrophysiological data suggested a robust sensitivity to both stress patterns. When incongruent with the context, both compound and phrasal stress elicited a sustained left-lateralized negativity. Moreover, incongruent compound stress elicited a centro-parietal negativity (N400), while incongruent phrasal stress elicited a late posterior positivity (P600). We conclude that the previous findings of a preference for compounds are due to response selection bias, and not a lack of perceptual sensitivity. The present results complement previous evidence for the immediate use of meter in semantic processing, as well as evidence for late interactions between prosodic and syntactic information.