How Data Drive Early Word Learning: A Cross-Linguistic Waiting Time Analysis

The extent to which word learning is delayed by maturation as opposed to accumulating data is a longstanding question in language acquisition. Further, the precise way in which data influence learning on a large scale is unknown—experimental results reveal that children can rapidly learn words from single instances as well as by aggregating ambiguous information across multiple situations. We analyze Wordbank, a large cross-linguistic dataset of word acquisition norms, using a statistical waiting time model to quantify the role of data in early language learning, building off Hidaka (2013). We find that the model both fits and accurately predicts the shape of children’s growth curves. Further analyses of model parameters suggest a primarily data-driven account of early word learning. The parameters of the model directly characterize both the amount of data required and the rate at which informative data occurs. With high statistical certainty, words require on the order of ∼ 10 learning instances, which occur on average once every two months. Our method is extremely simple, statistically principled, and broadly applicable to modeling data-driven learning effects in development

Logical word learning: The case of kinship

We examine the conceptual development of kinship through the lens of program induction. We present a computational model for the acquisition of kinship term concepts, resulting in the first computational model of kinship learning that is closely tied to developmental phenomena. We demonstrate that our model can learn several kinship systems of varying complexity using cross-linguistic data from English, Pukapuka, Turkish, and Yanomamö. More importantly, the behavioral patterns observed in children learning kinship terms, under-extension and over-generalization, fall out naturally from our learning model. We then conducted interviews to simulate realistic learning environments and demonstrate that the characteristic-to-defining shift is a consequence of our learning model in naturalistic contexts containing abstract and concrete features. We use model simulations to understand the influence of logical simplicity and children’s learning environment on the order of acquisition of kinship terms, providing novel predictions for the learning trajectories of these words. We conclude with a discussion of how this model framework generalizes beyond kinship terms, as well as a discussion of its limitations

Inferring priors in compositional cognitive models

We apply Bayesian data analysis to a structured cognitivemodel in order to determine the priors that support humangeneralizations in a simple concept learning task. We mod-eled 250,000 ratings in a “number game” experiment wheresubjects took examples of a numbers produced by a program(e.g. 4, 16, 32) and rated how likely other numbers (e.g. 8vs. 9) would be to be generated. This paper develops a dataanalysis technique for a family of compositional “Language ofThought” (LOT) models which permits discovery of subjects’prior probability of mental operations (e.g. addition, multi-plication, etc.) in this domain. Our results reveal high cor-relations between model mean predictions and subject gener-alizations, but with some qualitative mismatch for a stronglycompositional prior

Learning and the language of thought

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 179-191).This thesis develops the hypothesis that key aspects of learning and development can be understood as rational statistical inferences over a compositionally structured representation system, a language of thought (LOT) (Fodor, 1975). In this setup, learners have access to a set of primitive functions and learning consists of composing these functions in order to created structured representations of complex concepts. We present an inductive statistical model over these representations that formalizes an optimal Bayesian trade-off between representational complexity and fit to the observed data. This approach is first applied to the case of number-word acquisition, for which statistical learning with a LOT can explain key developmental patterns and resolve philosophically troublesome aspects of previous developmental theories. Second, we show how these same formal tools can be applied to children's acquisition of quantifiers. The model explains how children may achieve adult competence with quantifiers' literal meanings and presuppositions, and predicts several of the most-studied errors children make while learning these words. Finally, we model adult patterns of generalization in a massive concept-learning experiment. These results provide evidence for LOT models over other approaches and provide quantitative evaluation of different particular LOTs.by Steven Thomas Piantadosi.Ph.D

The Goldilocks Effect: Human Infants Allocate Attention to Visual Sequences That Are Neither Too Simple Nor Too Complex

Human infants, like immature members of any species, must be highly selective in sampling information from their environment to learn efficiently. Failure to be selective would waste precious computational resources on material that is already known (too simple) or unknowable (too complex). In two experiments with 7- and 8-month-olds, we measure infants’ visual attention to sequences of events varying in complexity, as determined by an ideal learner model. Infants’ probability of looking away was greatest on stimulus items whose complexity (negative log probability) according to the model was either very low or very high. These results suggest a principle of infant attention that may have broad applicability: infants implicitly seek to maintain intermediate rates of information absorption and avoid wasting cognitive resources on overly simple or overly complex events

Beyond Boolean logic: exploring representation languages for learning complex concepts

We study concept learning for semantically-motivated, set-theoretic concepts. We first present an experiment in which we show that subjects learn concepts which cannot be represented by a simple Boolean logic. We then present a computational model which is similarly capable of learning these concepts,and show that it provides a good fit to human learning curves. Additionally, we compare the performance of several potential representation languages which are richer than Boolean logic in predicting human response distributions. Keywords: Rule-based concept learning; probabilistic model;semantics