We propose that humans are adapted to transfer knowledge to, and receive knowledge from, conspecifics by teaching. This adaptation, which we call 'pedagogy', involves the emergence of a special communication system that does not presuppose either language or high-level theory of mind, but could itself provide a basis facilitating the development of these human-specific abilities both in phylogenetic and ontogenetic terms. We speculate that tool manufacturing and mediated tool use made the evolution of such a new social learning mechanism necessary. However, the main body of evidence supporting this hypothesis comes from developmental psychology. We argue that many central phenomena of human infant social cognition that may seem puzzling in the light of their standard functional explanation can be more coherently and plausibly interpreted as reflecting the adaptations to receive knowledge from social partners through teaching.

What is the role of the linguistic environment in children’s early word learning? Here we provide a preliminary analysis of one child’s linguistic development, using a portion of the high-density longitudinal data collected for the Human Speechome Project. We focus particularly on the development of the child’s productive vocabulary from the age of 9 to 24 months and the relationship between the child’s language development and the caregivers ’ speech. We find significant correlations between input frequencies and age of acquisition for individual words. In addition, caregivers ’ utterance length, type-token ratio, and proportion of single-word utterances all show significant temporal relationships with the child’s development, suggesting that caregivers “tune ” their utterances to the linguistic ability of the child.

We report a quantitative analysis of the cross-utterance coordination observed in child-directed language, where successive utterances often overlap in a manner that makes their constituent structure more prominent, and describe the application of a recently published unsupervised algorithm for grammar induction to the largest available corpus of such language, producing a grammar capable of accepting and generating novel wellformed sentences. We also introduce a new corpus-based method for assessing the precision and recall of an automatically acquired generative grammar without recourse to human judgment. The present work sets the stage for the eventual development of more powerful unsupervised algorithms for language acquisition, which would make use of the coordination structures present in natural child-directed speech.

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Child-directed speech has a distinctive structure and may have facilitatory effects on children’s language learning. We consider these facilitatory effects from the perspective of Marr’s levels of analysis: could they arise at the computational level or must they be located at at the algorithmic or implementation levels? To determine if the effects could be due to computational level benefits, we examine the question of what samples from a language should best facilitate learning by identifying the optimal linguistic input for an ideal Bayesian learner. Our analysis leads to a mathematical definition of the “representativeness” of linguistic data, which can be computed for any probabilistic model of language learning. We use this measure to re-examine the debate over whether language learning can be improved by “starting small ” (i.e. learning from data that have limited complexity). We compare the representativeness of corpora with differing levels of complexity, showing that while optimal corpora for a complex language are also complex, it is possible to construct relatively good corpora with limited complexity. We discuss the implications of these results for the level of analysis at which a benefit of starting small must be located.