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

When do memory limitations lead to regularization? An experimental and computational investigation

The Less is More hypothesis suggests that one reason adults and children differ in their ability to learn language is that they also differ in other cognitive capacities. According to one version of this hypothesis, children's relatively poor memory may make them more likely to regularize inconsistent input (Hudson Kam & Newport, 2005, 2009). This paper reports the result of an experimental and computational investigation of one aspect of this version of the hypothesis. A series of seven experiments in which adults were placed under a high cognitive load during a language-learning task reveal that in adults, increased load during learning (as opposed to retrieval) does not result in increased regularization. A computational model offers a possible explanation for these results. It demonstrates that, unless memory limitations distort the data in a particular way, regularization should occur only in the presence of both memory limitations and a prior bias for regularization. Taken together, these findings suggest that the difference in regularization between adults and children may not be solely attributable to differences in memory limitations during learning.Amy Perfor

Probability matching vs over-regularization in language: participant behavior depends on their interpretation of the task

In a variety of domains, children have been observed to overregularize inconsistent input, while adults are more likely to “probability match” to any inconsistency. Many explanations for this have been offered, usually relating to cognitive differences between children and adults. Here we explore an additional possibility: that differences in the social assumptions participants bring to the experiment can drive differences in over-regularization behavior. We explore this in the domain of language, where assumptions about error and communicative purpose might have a large effect. Indeed, we find that participants who experience less pressure to be “correct” and who have more reason to believe that any inconsistencies do not correspond to an underlying regularity do over-regularize more. Implications for language acquisition in children and adults are discussed.Amy Perfor

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

Learning to learn categories

Learning to categorize objects in the world is more than just learning the specific facts that characterize individual categories. We can also learn more abstract knowledge about how categories in a domain tend to be organized -- extending even to categories that we've never seen examples of. These abstractions allow us to learn and generalize examples of new categories much more quickly than if we had to start from scratch with each category encountered. We present a model for "learning to learn" to categorize in this way, and demonstrate that it predicts human behavior in a novel experimental task. Both human and model performance suggest that higher-order and lower-order generalizations can be equally as easy to acquire. In addition, although both people and the model show impaired generalization when categories have to be inferred compared to when they don't, human performance is more strongly affected. We discuss the implications of these findings.Amy Perfors and Joshua Tenenbau

Musicians are better at learning non-native sound contrasts even in non-tonal languages

It is very difficult for adults to perceive phonetic contrasts in their non-native language. In this study we explored the effects of phonetic training for different populations of people (musicians and non-musicians)and with different kinds of phoneme contrast (timing-based, like the Hindi /g/-/k/ contrast, and pitch-based, like the Mandarin /l/-/l/ tonal contrast). We found that musicians had superior perception for both contrasts, not just the pitch-based one. For both phonemes, training had little to no effect. We consider the implications of this for first and second language acquisition.Amy Perfors and Jia Hoong On

Hypothesis generation, sparse categories, and the positive test strategy

We consider the situation in which a learner must induce the rule that explains an observed set of data but the hypothesis space of possible rules is not explicitly enumerated or identified. The first part of the article demonstrates that as long as hypotheses are sparse (i.e., index less than half of the possible entities in the domain) then a positive test strategy is near optimal. The second part of this article then demonstrates that a preference for sparse hypotheses (a sparsity bias) emerges as a natural consequence of the family resemblance principle; that is, it arises from the requirement that good rules index entities that are more similar to one another than they are to entities that do not satisfy the rule.Daniel J. Navarro and Amy F. Perfor

Enlightenment grows from fundamentals

Jones & Love (J&L) contend that the Bayesian approach should integrate process constraints with abstract computational analysis. We agree, but argue that the fundamentalist/enlightened dichotomy is a false one: Enlightened research is deeply intertwined with – and to a large extent is impossible without – the basic, fundamental work upon which it is based.Matt Jones, Bradley C. Lov

Adult language learners under cognitive load do not over-regularize like children

The "less is more" hypothesis suggests that one reason adults and children differ in their language acquisition abilities is that they also differ in other cognitive capacities: for instance, the relatively poor memory and/or processing abilities of children may make them more likely to over-regularize inconsistent input (Singleton & Newport, 2004; Hudson Kam & Newport, 2005). We investigate this hypothesis by placing adults under a high cognitive load using a standard task. Does their tendency to over-regularize in a simultaneous language-learning task increase? Results indicate that although the cognitive load is high enough to impair overall learning, neither the presence of load nor poor working memory predicts greater overregularization. This suggests that if the "less is more" hypothesis explains over-regularization in children, the relevant cognitive capacity is not one that was impaired by our load task.Amy Perfors and Nicholas Burnshttp://cognitivesciencesociety.org/conference2010/schedule.htm

Phonetic training makes word learning easier

Motivated by the idea that differences between adult and child language learners may stem in part from initially minor differences (such as in phonetic perception) that cascade throughout other aspects of language learning, we explored to what extent training adults on a novel phonetic contrast results in improved learning of words that incorporate that contrast. Results indicate that distributional training on a novel phonetic contrast improves word learning as well as the ability to discriminate a related contrast. We discuss implications for how adults' phonological abilities in affect other aspects of language learning, and also for understanding the effectiveness of different phonetic training regimes.Amy Perfors and David Dunbarhttp://cognitivesciencesociety.org/conference2010/schedule.htm