Online abstract. One of the most astonishing features of human language is its capacity to convey information efficiently in context. Many theories provide informal accounts of communicative inference, yet there have been few successes in making precise, quantitative predictions about pragmatic reasoning. We examine judgments about simple referential communication games, modeling behavior in these games by assuming that speakers attempt to be informative, and that listeners use Bayesian inference to recover speakers' intended referents. Our model provides a close, parameter-free fit to human judgments, suggesting that using information-theoretic tools to predict pragmatic reasoning may lead to more effective formal models of communication.One of the most astonishing features of human language is its ability to convey information efficiently in context. Each utterance need not carry every detail; instead, listeners can infer speakers' intended meanings by assuming utterances convey only relevant information. These communicative inferences rely on the shared assumption that speakers are informative, but not more so than is necessary given the communicators' common knowledge and the task at hand. Many theories provide high-level accounts of these kinds of inferences (1-3), yet-perhaps be-1

English and Russian color terms divide the color spectrum differently. Unlike English, Russian makes an obligatory distinction between lighter blues (''goluboy'') and darker blues (''siniy''). We investigated whether this linguistic difference leads to differences in color discrimination. We tested English and Russian speakers in a speeded color discrimination task using blue stimuli that spanned the siniy/goluboy border. We found that Russian speakers were faster to discriminate two colors when they fell into different linguistic categories in Russian (one siniy and the other goluboy) than when they were from the same linguistic category (both siniy or both goluboy). Moreover, this category advantage was eliminated by a verbal, but not a spatial, dual task. These effects were stronger for difficult discriminations (i.e., when the colors were perceptually close) than for easy discriminations (i.e., when the colors were further apart). English speakers tested on the identical stimuli did not show a category advantage in any of the conditions. These results demonstrate that (i) categories in language affect performance on simple perceptual color tasks and (ii) the effect of language is online (and can be disrupted by verbal interference).categorization ͉ cross-linguistic ͉ Whorf

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The MacArthur-Bates Communicative Development Inventories (CDIs) are a widely used family of parent-report instruments for easy and inexpensive data-gathering about early language acquisition. CDI data have been used to explore a variety of theoretically important topics, but, with few exceptions, researchers have had to rely on data collected in their own lab. In this paper, we remedy this issue by presenting Wordbank, a structured database of CDI data combined with a browsable web interface. Wordbank archives CDI data across languages and labs, providing a resource for researchers interested in early language, as well as a platform for novel analyses. The site allows interactive exploration of patterns of vocabulary growth at the level of both individual children and particular words. We also introduce wordbankr, a software package for connecting to the database directly. Together, these tools extend the abilities of students and researchers to explore quantitative trends in vocabulary development.

In simple tests of preference, infants as young as newborns prefer faces and face-like stimuli over distractors. Little is known, however, about the development of attention to faces in complex scenes. We recorded eye-movements of 3-, 6-, and 9-month-old infants and adults during free-viewing of clips from A Charlie Brown Christmas (an animated film). The tendency to look at faces increased with age. Using novel computational tools, we found that 3-month-olds were less consistent (across individuals) in where they looked than were older infants. Moreover, younger infants' fixations were best predicted by low-level image salience, rather than the locations of faces. Between 3 and 9 months of age, infants gradually focus their attention on faces. We discuss several possible interpretations of this shift in terms of social development, cross-modal integration, and attentional/executive control.A lot can be learned about our social world from the faces of others. Faces provide information about age, race, gender, physical health, emotional state, and focus of attention, giving observers a window into the mental states of other human beings. During the first year after birth, infants begin to extract a large amount of information from faces: they begin to recognize identities (Pascalis, De Haan, Nelson, & De Schonen, 1998), recognize and prefer faces from their own race , detect affect (Cohn & Tronick, 1983;Tronick, 1989), and follow gaze (Corkum & Moore, 1998;Scaife & Bruner, 1975). However, these sophisticated abilities are of little use if infants don't look at faces to begin with. Put another way, to extract information from faces, infants must first attend to them.Although there is a large literature on the origins and development of infants' face representations during infancy, far less research has examined the behavior of infants outside of controlled laboratory settings. In particular, both the extent to which infants attend to faces when other objects are present-as in most real-world situations-and the extent to which this behavior changes across development are still largely unknown. The reasons for this gap in the literature may be both methodological and theoretical. Methodologically, standard lookingtime paradigms used in infant research typically produce only qualitative evidence and do not make sense in older populations; hence it is difficult to design experimental paradigms whose results can be compared across wide age ranges. Theoretically, many researchers have been interested primarily in the question of innateness: whether human infants are predisposed to treat human faces as "special" relative to other objects and whether the representations underlying these judgments are qualitatively similar to those used by adults. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the result...

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ABSTRACT-Word learning is a ''chicken and egg'' problem. If a child could understand speakers' utterances, it would be easy to learn the meanings of individual words, and once a child knows what many words mean, it is easy to infer speakers' intended meanings. To the beginning learner, however, both individual word meanings and speakers' intentions are unknown. We describe a computational model of word learning that solves these two inference problems in parallel, rather than relying exclusively on either the inferred meanings of utterances or cross-situational word-meaning associations. We tested our model using annotated corpus data and found that it inferred pairings between words and object concepts with higher precision than comparison models. Moreover, as the result of making probabilistic inferences about speakers' intentions, our model explains a variety of behavioral phenomena described in the word-learning literature. These phenomena include mutual exclusivity, one-trial learning, cross-situational learning, the role of words in object individuation, and the use of inferred intentions to disambiguate reference.When children learn their first words, they face a challenging joint-inference problem: They are both trying to infer what meaning a speaker is attempting to communicate at the moment a sentence is uttered and trying to learn the more stable mappings between words and referents that constitute the lexicon of their language. With either of these pieces of information, their task becomes considerably easier. Knowing the meanings of some words, a child can often figure out what a speaker is talking about, and inferring the meaning of a speaker's utterance allows a child to work backward and learn basic-level object names with relative ease. However, for a learner without either of these pieces of information, word learning is a hard computational problem. Quine (1960) suggested an apt metaphor: A word learner is climbing the inside of a chimney, ''supporting himself against each side by pressure against the others'' (p. 93).Many accounts of word learning focus primarily on one aspect of this problem. Social theories suggest that learners rely on a rich understanding of the goals and intentions of speakers and assume that-at least in the case of object nouns-once the child understands what is being talked about, the mappings between words and referents are relatively easy to learn (St.

Access to data is a critical feature of an efficient, progressive and ultimately self-correcting scientific ecosystem. But the extent to which in-principle benefits of data sharing are realized in practice is unclear. Crucially, it is largely unknown whether published findings can be reproduced by repeating reported analyses upon shared data (‘analytic reproducibility’). To investigate this, we conducted an observational evaluation of a mandatory open data policy introduced at the journal Cognition. Interrupted time-series analyses indicated a substantial post-policy increase in data available statements (104/417, 25% pre-policy to 136/174, 78% post-policy), although not all data appeared reusable (23/104, 22% pre-policy to 85/136, 62%, post-policy). For 35 of the articles determined to have reusable data, we attempted to reproduce 1324 target values. Ultimately, 64 values could not be reproduced within a 10% margin of error. For 22 articles all target values were reproduced, but 11 of these required author assistance. For 13 articles at least one value could not be reproduced despite author assistance. Importantly, there were no clear indications that original conclusions were seriously impacted. Mandatory open data policies can increase the frequency and quality of data sharing. However, suboptimal data curation, unclear analysis specification and reporting errors can impede analytic reproducibility, undermining the utility of data sharing and the credibility of scientific findings.