Do as I say, not as I do:a lexical distributional account of English locative verb class acquisition

Children overgeneralise verbs to ungrammatical structures early in acquisition, but retreat from these overgeneralisations as they learn semantic verb classes. In a large corpus of English locative utterances (e.g., the woman sprayed water onto the wall/wall with water), we found structural biases which changed over development and which could explain overgeneralisation behaviour. Children and adults had similar verb classes and a correspondence analysis suggested that lexical distributional regularities in the adult input could help to explain the acquisition of these classes. A connectionist model provided an explicit account of how structural biases could be learned over development and how these biases could be reduced by learning verb classes from distributional regularities

Lexical distributional cues, but not situational cues, are readily used to learn abstract locative verb-structure associations.

Children must learn the structural biases of locative verbs in order to avoid making overgeneralisation errors (e.g., *I filled water into the glass). It is thought that they use linguistic and situational information to learn verb classes that encode structural biases. In addition to situational cues, we examined whether children and adults could use the lexical distribution of nouns in the post-verbal noun phrase to assign novel verbs to locative classes. In Experiment 1, children and adults used lexical distributional cues to assign verb classes, but were unable to use situational cues appropriately. In Experiment 2, adults generalised distributionally-learned classes to novel verb arguments, demonstrating that distributional information can cue abstract verb classes. Taken together, these studies show that human language learners can use a lexical distributional mechanism that is similar to that used by computational linguistic systems that use large unlabelled corpora to learn verb meaning

Frequency Sensitivity of Neural Responses to English Verb Argument Structure Violations

How are verb-argument structure preferences acquired? Children typically receive very little negative evidence, raising the question of how they come to understand the restrictions on grammatical constructions. Statistical learning theories propose stochastic patterns in the input contain sufficient clues. For example, if a verb is very common, but never observed in transitive constructions, this would indicate that transitive usage of that verb is illegal. Ambridge et al. (2008) have shown that in offline grammaticality judgements of intransitive verbs used in transitive constructions, low-frequency verbs elicit higher acceptability ratings than high-frequency verbs, as predicted if relative frequency is a cue during statistical learning. Here, we investigate if the same pattern also emerges in on-line processing of English sentences. EEG was recorded while healthy adults listened to sentences featuring transitive uses of semantically matched verb pairs of differing frequencies. We replicate the finding of higher acceptabilities of transitive uses of low- vs. high-frequency intransitive verbs. Event-Related Potentials indicate a similar result: early electrophysiological signals distinguish between misuse of high- vs low-frequency verbs. This indicates online processing shows a similar sensitivity to frequency as off-line judgements, consistent with a parser that reflects an original acquisition of grammatical constructions via statistical cues. However, the nature of the observed neural responses was not of the expected, or an easily interpretable, form, motivating further work into neural correlates of online processing of syntactic constructions

The retreat from locative overgeneralisation errors : a novel verb grammaticality judgment study

Whilst some locative verbs alternate between the ground- and figure-locative constructions (e.g. Lisa sprayed the flowers with water/Lisa sprayed water onto the flowers), others are restricted to one construction or the other (e.g. *Lisa filled water into the cup/*Lisa poured the cup with water). The present study investigated two proposals for how learners (aged 5–6, 9–10 and adults) acquire this restriction, using a novel-verb-learning grammaticality-judgment paradigm. In support of the semantic verb class hypothesis, participants in all age groups used the semantic properties of novel verbs to determine the locative constructions (ground/figure/both) in which they could and could not appear. In support of the frequency hypothesis, participants’ tolerance of overgeneralisation errors decreased with each increasing level of verb frequency (novel/low/high). These results underline the need to develop an integrated account of the roles of semantics and frequency in the retreat from argument structure overgeneralisation

Children Use Statistics and Semantics in the Retreat from Overgeneralization

How do children learn to restrict their productivity and avoid ungrammatical utterances? The present study addresses this question by examining why some verbs are used with un- prefixation (e.g., unwrap) and others are not (e.g., *unsqueeze). Experiment 1 used a priming methodology to examine children's (3–4; 5–6) grammatical restrictions on verbal un- prefixation. To elicit production of un-prefixed verbs, test trials were preceded by a prime sentence, which described reversal actions with grammatical un- prefixed verbs (e.g., Marge folded her arms and then she unfolded them). Children then completed target sentences by describing cartoon reversal actions corresponding to (potentially) un- prefixed verbs. The younger age-group's production probability of verbs in un- form was negatively related to the frequency of the target verb in bare form (e.g., squeez/e/ed/es/ing), while the production probability of verbs in un- form for both age groups was negatively predicted by the frequency of synonyms to a verb's un- form (e.g., release/*unsqueeze). In Experiment 2, the same children rated the grammaticality of all verbs in un- form. The older age-group's grammaticality judgments were (a) positively predicted by the extent to which each verb was semantically consistent with a semantic “cryptotype” of meanings - where “cryptotype” refers to a covert category of overlapping, probabilistic meanings that are difficult to access - hypothesised to be shared by verbs which take un-, and (b) negatively predicted by the frequency of synonyms to a verb's un- form. Taken together, these experiments demonstrate that children as young as 4;0 employ pre-emption and entrenchment to restrict generalizations, and that use of a semantic cryptotype to guide judgments of overgeneralizations is also evident by age 6;0. Thus, even early developmental accounts of children's restriction of productivity must encompass a mechanism in which a verb's semantic and statistical properties interact

The ubiquity of frequency effects in first language acquisition

This review article presents evidence for the claim that frequency effects are pervasive in children's first language acquisition, and hence constitute a phenomenon that any successful account must explain. The article is organized around four key domains of research: children's acquisition of single words, inflectional morphology, simple syntactic constructions, and more advanced constructions. In presenting this evidence, we develop five theses. (i) There exist different types of frequency effect, from effects at the level of concrete lexical strings to effects at the level of abstract cues to thematic-role assignment, as well as effects of both token and type, and absolute and relative, frequency. High-frequency forms are (ii) early acquired and (iii) prevent errors in contexts where they are the target, but also (iv) cause errors in contexts in which a competing lower-frequency form is the target. (v) Frequency effects interact with other factors (e.g. serial position, utterance length), and the patterning of these interactions is generally informative with regard to the nature of the learning mechanism. We conclude by arguing that any successful account of language acquisition, from whatever theoretical standpoint, must be frequency sensitive to the extent that it can explain the effects documented in this review, and outline some types of account that do and do not meet this criterion

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.Additional co-authors: Rukmini Bhaya Nair, Seth Campbell, Clifton Pye, Pedro Mateo Pedro, Sindy Fabiola Can Pixabaj, Mario Marroquín Pelíz, Margarita Julajuj Mendoz

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