Listeners use descriptive contrast to disambiguate novel referents and make inferences about novel categories

In the face of unfamiliar language or objects, description is one cue people can use to learn about both. Beyond narrowing potential referents to those that match a descriptor, listeners could infer that a described object is one that contrasts with other relevant objects of the same type (e.g., “The tall cup” contrasts with another, shorter cup). This contrast may be in relation to other objects present in the environment or to the referent’s category. In two experiments, we investigate whether listeners use descriptive contrast to resolve reference and make inferences about novel referents’ categories. While participants use size adjectives contrastively to guide novel referent choice, they do not reliably do so using color adjectives (Experiment 1). Their contrastive inferences go beyond the current referential context: participants use description to infer that a novel object is atypical of its category (Experiment 2). Overall, people are able to use descriptive contrast to resolve reference and make inferences about a novel object’s category, allowing them to infer new word meanings and learn about new categories’ feature distributions

Quantifying the Roles of Visual, Linguistic, and Visual-Linguistic Complexity in Verb Acquisition

Children typically learn the meanings of nouns earlier than the meanings of verbs. However, it is unclear whether this asymmetry is a result of complexity in the visual structure of categories in the world to which language refers, the structure of language itself, or the interplay between the two sources of information. We quantitatively test these three hypotheses regarding early verb learning by employing visual and linguistic representations of words sourced from large-scale pre-trained artificial neural networks. Examining the structure of both visual and linguistic embedding spaces, we find, first, that the representation of verbs is generally more variable and less discriminable within domain than the representation of nouns. Second, we find that if only one learning instance per category is available, visual and linguistic representations are less well aligned in the verb system than in the noun system. However, in parallel with the course of human language development, if multiple learning instances per category are available, visual and linguistic representations become almost as well aligned in the verb system as in the noun system. Third, we compare the relative contributions of factors that may predict learning difficulty for individual words. A regression analysis reveals that visual variability is the strongest factor that internally drives verb learning, followed by visual-linguistic alignment and linguistic variability. Based on these results, we conclude that verb acquisition is influenced by all three sources of complexity, but that the variability of visual structure poses the most significant challenge for verb learning

Probabilistic Cue Combination: Less is More

Learning about the structure of the world requires learning probabilistic relationships: rules in which cues do not predict outcomes with certainty. However, in some cases, the ability to track probabilistic relationships is a handicap, leading adults to perform non-normatively in prediction tasks. For example, in the dilution effect, predictions made from the combination of two cues of different strengths are less accurate than those made from the stronger cue alone. Here we show that dilution is an adult problem; 11-month-old infants combine strong and weak predictors normatively. These results extend and add support for the less is more hypothesis: limited cognitive resources can lead children to represent probabilistic information differently from adults, and this difference in representation can have important downstream consequences for prediction

Noisy-Kids

Preschoolers flexibly adapt to linguistic input in a noisy channe

The role of working memory in transfer of implicit learning

Implicit learning, behavioral change accompanied by an inability to consciously describe the means by which it has occurred, has been demonstrated in a number of domains. One question concerns the role of working memory in the learning process – if participants do not have conscious access to the learned information, what is the role of conscious attention and working memory in their learning? This paper further explores the question by studying the role of working memory on transferability of implicitly learned knowledge in the Balls and Boxes problem. Participants were given a puzzle to solve and then either the same puzzle or a horizontally inverted isomorph under single-task or working-memory interference conditions. As hypothesized, participants have little difficulty transferring their learned knowledge to the new problem unless their working memory is loaded.</p

Characterizing the Typical Information Curves of Diverse Languages

Optimal coding theories of language predict that speakers will keep the amount of information in their utterances relatively uniform under the constraints imposed by their language, but how much do these constraints influence information structure, and how does this influence vary across languages? We present a novel method for characterizing the information structure of sentences across a diverse set of languages. While the structure of English is broadly consistent with the shape predicted by optimal coding, many languages are not consistent with this prediction. We proceed to show that the characteristic information curves of languages are partly related to a variety of typological features from phonology to word order. These results present an important step in the direction of exploring upper bounds for the extent to which linguistic codes can be optimal for communication

Iterated Learning

A comparison of adult, child, and dyad performance on a dot pattern recall task to study how structure emerges in a novel language