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

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

A cognitive analysis of deception without lying

When the interests of interlocutors are not aligned, either party may wish to avoid truthful disclosure. A sender wishing to conceal the truth from a receiver may lie by providing false information, mislead by actively encouraging the receiver to reach a false conclusion, or simply be uninformative by providing little or no relevant information. Lying entails moral and other hazards, such as detection and its consequences, and is thus often avoided. We focus here on the latter two strategies, arguably more pernicious and prevalent, but not without their own drawbacks. We argue and show in two studies that when choosing between these options, senders consider the level of suspicion likely to be exercised on the part of the receiver and how much truth must be revealed in order to mislead. Extending Bayesian models of cooperative communication to include higher level inference regarding the helpfulness of the sender leads to insight into the strategies employed in non-cooperative contexts.Keith Ransom, Wouter Voorspoels, Amy Perfors, Daniel J. Navarr

Stochastic search algorithms can tell us who to trust (and why)

Relying on information from other people (social testimony) is essential for efficiently learning and reasoning about the world. However, determining who to trust is often challenging. In this paper, we argue that trust in social agents (i.e., those providing testimony) can be evaluated by assessing how optimally they have acquired their knowledge. Building on theories that describe knowledge acquisition as a stochastic search through a space of hypotheses, we present a framework which yields predictions about which agents will provide better testimony (because they are more likely to have uncovered higher-probability hypotheses) in different contexts. This approach allows us to jointly predict how the quality of testimony is affected by 1) features of the agents themselves, like their expertise; 2) consensus among multiple agents; and 3) features of the topic and hypothesis space, like its knowability. We present initial simulations demonstrating how even a basic implementation of our framework yields insight into which types of agents and topics are more likely to result in accurate testimony (and why). We conclude by discussing how this preliminary research might be extended to address more complicated social reasoning scenarios

The role of stimulus‐specific perceptual fluency in statistical learning

Humans have the ability to learn surprisingly complicated statistical information in a variety of modalities and situations, often based on relatively little input. These statistical learning (SL) skills appear to underlie many kinds of learning, but despite their ubiquity, we still do not fully understand precisely what SL is and what individual differences on SL tasks reflect. Here, we present experimental work suggesting that at least some individual differences arise from stimulus-specific variation in perceptual fluency: the ability to rapidly or efficiently code and remember the stimuli that SL occurs over. Experiment 1 demonstrates that participants show improved SL when the stimuli are simple and familiar; Experiment 2 shows that this improvement is not evident for simple but unfamiliar stimuli; and Experiment 3 shows that for the same stimuli (Chinese characters), SL is higher for people who are familiar with them (Chinese speakers) than those who are not (English speakers matched on age and education level). Overall, our findings indicate that performance on a standard SL task varies substantially within the same (visual) modality as a function of whether the stimuli involved are familiar or not, independent of stimulus complexity. Moreover, test–retest correlations of performance in an SL task using stimuli of the same level of familiarity (but distinct items) are stronger than correlations across the same task with stimuli of different levels of familiarity. Finally, we demonstrate that SL performance is predicted by an independent measure of stimulus-specific perceptual fluency that contains no SL component at all. Our results suggest that a key component of SL performance may be related to stimulus-specific processing and familiarity

Who Likes What? Comparing Personal Preferences with Group Predictions based on Gender and Extraversion Across Common Semantic Domains.

Some people like coffee while others prefer tea, but little is known about whether preferences like these are shared among groups and whether they vary systematically across many common semantic categories. This study addresses this gap by examining two major sources of variation -- gender and extraversion -- across twelve categories or domains, ranging from fruit and animals to sports and personal qualities. In Study 1, participants rated their own preferences for a set of 300 exemplars. Results showed significant preference differences between men and women for 40% of items spread across all categories, and smaller but reliable differences between introverts and extraverts for 11% of items concentrated in domains like personal qualities. Study 2 used an allocentric categorisation task where the same participants categorised items based on which they thought would be preferred by men vs women or introverts vs extraverts. Using the ratings from Study 1 to score accuracy, the judgments from Study 2 showed that participants were sensitive to even subtle differences in preference, although accuracy varied by the judge's gender and extraversion: women were more accurate than men across many categories and introverts more accurate than extraverts for a few categories. We also found incorrect but widely shared judgments for about 20% of items, suggestive of inaccurate stereotypes about group preferences. Together, these results suggest widespread and systematic variation by gender (and to a lesser extent extraversion) that can be accurately predicted by others, although with systematic biases. Our results have implications for theories of semantic representation and social cognition

Acquiring variation in an artificial language: children and adults are sensitive to socially conditioned linguistic variation

Languages exhibit sociolinguistic variation, such that adult native speakers condition the usage of linguistic variants on social context, gender, and ethnicity, among other cues. While the existence of this kind of socially conditioned variation is well-established, less is known about how it is acquired. Studies of naturalistic language use by children provide various examples where children's production of sociolinguistic variants appears to be conditioned on similar factors to adults’ production, but it is difficult to determine whether this reflects knowledge of sociolinguistic conditioning or systematic differences in the input to children from different social groups. Furthermore, artificial language learning experiments have shown that children have a tendency to eliminate variation, a process which could potentially work against their acquisition of sociolinguistic variation. The current study used a semi-artificial language learning paradigm to investigate learning of the sociolinguistic cue of speaker identity in 6-year-olds and adults. Participants were trained and tested on an artificial language where nouns were obligatorily followed by one of two meaningless particles and were produced by one of two speakers (one male, one female). Particle usage was conditioned deterministically on speaker identity (Experiment 1), probabilistically (Experiment 2), or not at all (Experiment 3). Participants were given tests of production and comprehension. In Experiments 1 and 2, both children and adults successfully acquired the speaker identity cue, although the effect was stronger for adults and in Experiment 1. In addition, in all three experiments, there was evidence of regularization in participants' productions, although the type of regularization differed with age: children showed regularization by boosting the frequency of one particle at the expense of the other, while adults regularized by conditioning particle usage on lexical items. Overall, results demonstrate that children and adults are sensitive to speaker identity cues, an ability which is fundamental to tracking sociolinguistic variation, and that children's well-established tendency to regularize does not prevent them from learning sociolinguistically conditioned variation

The Bayesian boom: good thing or bad?

A series of high-profile critiques of Bayesian models of cognition have recently sparked controversy. These critiques question the contribution of rational, normative considerations in the study of cognition. The present article takes central claims from these critiques and evaluates them in light of specific models. Closer consideration of actual examples of Bayesian treatments of different cognitive phenomena allows one to defuse these critiques showing that they cannot be sustained across the diversity of applications of the Bayesian framework for cognitive modeling. More generally, there is nothing in the Bayesian framework that would inherently give rise to the deficits that these critiques perceive, suggesting they have been framed at the wrong level of generality. At the same time, the examples are used to demonstrate the different ways in which consideration of rationality uniquely benefits both theory and practice in the study of cognition