Eliminating unpredictable variation through iterated learning

Human languages may be shaped not only by the (individual psychological) processes of language acquisition, but also by population-level processes arising from repeated language learning and use. One prevalent feature of natural languages is that they avoid unpredictable variation. The current work explores whether linguistic predictability might result from a process of iterated learning in simple diffusion chains of adults. An iterated artificial language learning methodology was used, in which participants were organised into diffusion chains: the first individual in each chain was exposed to an artificial language which exhibited unpredictability in plural marking, and subsequent learners were exposed to the language produced by the previous learner in their chain. Diffusion chains, but not isolate learners, were found to cumulatively increase predictability of plural marking by lexicalising the choice of plural marker. This suggests that such gradual, cumulative population-level processes offer a possible explanation for regularity in language

Constraining generalisation in artificial language learning : children are rational too

Successful language acquisition involves generalization, but learners must balance this against the acquisition of lexical constraints. Examples occur throughout language. For example, English native speakers know that certain noun-adjective combinations are impermissible (e.g. strong winds, high winds, strong breezes, *high breezes). Another example is the restrictions imposed by verb subcategorization, (e.g. I gave/sent/threw the ball to him; I gave/sent/threw him the ball; donated/carried/pushed the ball to him; * I donated/carried/pushed him the ball). Such lexical exceptions have been considered problematic for acquisition: if learners generalize abstract patterns to new words, how do they learn that certain specific combinations are restricted? (Baker, 1979). Certain researchers have proposed domain-specific procedures (e.g. Pinker, 1989 resolves verb subcategorization in terms of subtle semantic distinctions). An alternative approach is that learners are sensitive to distributional statistics and use this information to make inferences about when generalization is appropriate (Braine, 1971). A series of Artificial Language Learning experiments have demonstrated that adult learners can utilize statistical information in a rational manner when determining constraints on verb argument-structure generalization (Wonnacott, Newport & Tanenhaus, 2008). The current work extends these findings to children in a different linguistic domain (learning relationships between nouns and particles). We also demonstrate computationally that these results are consistent with the predictions of domain-general hierarchical Bayesian model (cf. Kemp, Perfors & Tenebaum, 2007)

Variability, negative evidence, and the acquisition of verb argument constructions

We present a hierarchical Bayesian framework for modeling the acquisition of verb argument constructions. It embodies a domain-general approach to learning higher-level knowledge in the form of inductive constraints (or overhypotheses), and has been used to explain other aspects of language development such as the shape bias in learning object names. Here, we demonstrate that the same model captures several phenomena in the acquisition of verb constructions. Our model, like adults in a series of artificial language learning experiments, makes inferences about the distributional statistics of verbs on several levels of abstraction simultaneously. It also produces the qualitative learning patterns displayed by children over the time course of acquisition. These results suggest that the patterns of generalization observed in both children and adults could emerge from basic assumptions about the nature of learning. They also provide an example of a broad class of computational approaches that can resolve Baker's Paradox

Comparing generalisation in children and adults learning an artificial language

Successful language acquisition involves generalization, but learners must balance this against the acquisition of lexical constraints. Examples occur throughout language. For example, English native speakers know that certain noun-adjective combinations are impermissible (e.g., strong winds, high winds, strong breezes, *high breezes). Another example is the restrictions imposed by verb sub-categorization (e.g., I gave/sent/threw the ball to him; I gave/sent/threw him the ball; I donated/carried/pushed the ball to him; * I donated/carried/pushed him the ball; Baker, 1979). A central debate has been the extent to which learning such patterns depends on semantic cues (Pinker, 1989) and/or distributional statistics (Braine et al., 1990). The current experiments extend previous work which used Artificial Language learning to demonstrate that adults (Wonnacott et al., 2008) and 6 year olds (Wonnacott, 2011) are able to learn lexically based restrictions on generalization using distributional statistics. Here we directly compare the two age groups learning the same artificial language, with a view to exploring maturational differences in language learning. In addition to manipulating frequency (across high and low frequency items) and quantity of exposure (across days), languages were constructed such that a word’s semantic class was helpful for learning the restrictions for some types of lexical items, but potentially misleading for others

Higher order inference in verb argument structure acquisition

Successful language learning combines generalization and the acquisition of lexical constraints. The conflict is particularly clear for verb argument structures, which may generalize to new verbs (John gorped the ball to Bill ->John gorped Bill the ball), yet resist generalization with certain lexical items (John carried the ball to Bill -> *John carried Bill the ball). The resulting learnability “paradox” (Baker 1979) has received great attention in the acquisition literature. Wonnacott, Newport & Tanenhaus 2008 demonstrated that adult learners acquire both general and verb-specific patterns when acquiring an artificial language with two competing argument structures, and that these same constraints are reflected in real time processing. The current work follows up and extends this program of research in two new experiments. We demonstrate that the results are consistent with a hierarchical Bayesian model, originally developed by Kemp, Perfors & Tenebaum (2007) to capture the emergence of feature biases in word learning

Pragmatic competence and pragmatic tolerance in foreign language acquisition – revisiting the case of scalar implicatures

Previous L2 studies used binary Truth-Value-Judgment tasks (TVJ) to investigate L1-L2 differences in scalar implicature derivation (some X implicates some but not all X). They examined participants’ judgments of sentences with weak scalar expressions (“Timothy ate some of the pretzels”) when stronger ones are true (“Timothy ate all of the pretzels”). Some studies indicate adult L2 learners are less likely than L1 users to accept such statements while others found the opposite, concluding that implicature derivation is "costly" for L2 learners, rendering them less pragmatically competent than L1 users. Importantly, related L1 research suggests that TVJs only capture sensitivity to under- informativeness. This sensitivity might be completely overridden by metalinguistic attitudes in binary tasks, whereas graded tasks reveal nuanced judgment patterns. Exploring L2 response-behaviors, we tested English L1 speakers and competent German L2 English learners using binary and graded tasks. In both tasks, we found evidence of pragmatic responding with no evidence of differences between groups. Bayes Factor analyses of the graded data favored H0 over the hypotheses that L2 learners provide fewer or more rejections to under-informative input than L1 learners. We explore implications for L2 learners' pragmatic abilities, differences with previous studies, and the role of TVJs in under-informative contexts

Input effects on the acquisition of a novel phrasal construction in five year olds

The present experiments demonstrate that children as young as five years old (M = 5;2) generalize beyond their input on the basis of minimal exposure to a novel argument structure construction. The novel construction that was used involved a non-English phrasal pattern: VN1N2, paired with a novel abstract meaning: N2 approaches N1. At the same time, we find that children are keenly sensitive to the input: they show knowledge of the construction after a single day of exposure but this grows stronger after three days; also, children generalize more readily to new verbs when the input contains more than one verb

Skewing the evidence : the effect of input structure on child and adult learning of lexically based patterns in an artificial language

Successful language acquisition requires both generalization and lexically based learning. Previous research suggests that this is achieved, at least in part, by tracking distributional statistics at and above the level of lexical items. We explored this learning using a semi- artificial language learning paradigm with 6-year-olds and adults, looking at learning of co- occurrence relationships between (meaningless) particles and English nouns. Both age groups showed stronger lexical learning (and less generalization) given “skewed” languages where a majority particle co-occurred with most nouns. In addition, adults, but not children, were affected by overall lexicality, showing weaker lexical learning (more generalization) when some input nouns were seen to alternate (i.e. occur with both particles). The results suggest that restricting generalization is affected by distributional statistics above the level of words/bigrams. Findings are discussed within the framework offered by models capturing generalization as rational inference, namely hierarchical-Bayesian and simplicity-based models

The effects of linear order in category learning: some replications of Ramscar et al., (2010) and their implications for replicating training studies

Ramscar, Yarlett, Dye, Denny, and Thorpe (2010) showed how, consistent with the predictions of error-driven learning models, the order in which stimuli are presented in training can affect category learning. Specifically, learners exposed to artificial language input where objects preceded their labels learned the discriminating features of categories better than learners exposed to input where labels preceded objects. We sought to replicate this finding in two online experiments employing the same tests used originally: A four pictures test (match a label to one of four pictures) and a four labels test (match a picture to one of four labels). In our study, only findings from the four pictures test were consistent with the original result. Additionally, the effect sizes observed were smaller, and participants over-generalized high-frequency category labels more than in the original study. We suggest that although Ramscar, Yarlett, Dye, Denny, and Thorpe (2010) feature-label order predictions were derived from error-driven learning, they failed to consider that this mechanism also predicts that performance in any training paradigm must inevitably be influenced by participant prior experience. We consider our findings in light of these factors, and discuss implications for the generalizability and replication of training studies

The impact of multi-word units in early foreign language learning and teaching contexts: a systematic review

This systematic review reports on research investigating the impact of multi-word unit (MWU) input on young learners' second language (L2) attainment in instructed settings. Recent findings suggest that L2 learners can generalise from MWU input, abstract patterns and employ such schemata productively via slot-filling, indicating that MWUs are key catalysts of learners' L2 development. Simultaneously, primary school L2 instruction is on the rise worldwide and the importance of MWUs is acknowledged in curricula, teacher education and teaching materials. Therefore, the incentive of this review is to systematically report the state of the art of research regarding the impact of MWU instruction in early L2 teaching contexts. The review covers English, German and French research into typically developing monolingual children aged 5–12 learning an L2 in instructed teaching settings. Only two of the total results (n = 2233) met the inclusion criteria. Following quality assessment using the Mixed Methods Appraisal Tool and based on a narrative synthesis of available results, we cannot report trustworthy evidence of the effectiveness of teaching MWUs to young L2 learners. We highlight the lack of research evidence and conclude that existing research lacks robust evidence that MWU input already established in teaching contexts has a measurable effect on specific aspects of students' L2 attainment, such as productive skills. Although we promote MWU's potentially facilitating role in L2 development, we call for more classroom-based intervention research on MWUs in primary school contexts to enable much-needed evidence-based recommendations for L2 teaching to support L2 learning outcomes in primary schools