Word learning as Bayesian inference

The authors present a Bayesian framework for understanding how adults and children learn the meanings of words. The theory explains how learners can generalize meaningfully from just one or a few positive examples of a novel word's referents, by making rational inductive inferences that integrate prior knowledge about plausible word meanings with the statistical structure of the observed examples. The theory addresses shortcomings of the two best known approaches to modeling word learning, based on deductive hypothesis elimination and associative learning. Three experiments with adults and children test the Bayesian account's predictions in the context of learning words for object categories at multiple levels of a taxonomic hierarchy. Results provide strong support for the Bayesian account over competing accounts, in terms of both quantitative model fits and the ability to explain important qualitative phenomena. Several extensions of the basic theory are discussed, illustrating the broader potential for Bayesian models of word learning.     

Action understanding as inverse planning

Humans are adept at inferring the mental states underlying other agents’ actions, such as goals, beliefs, desires, emotions and other thoughts. We propose a computational framework based on Bayesian inverse planning for modeling human action understanding. The framework represents an intuitive theory of intentional agents’ behavior based on the principle of rationality: the expectation that agents will plan approximately rationally to achieve their goals, given their beliefs about the world. The mental states that caused an agent’s behavior are inferred by inverting this model of rational planning using Bayesian inference, integrating the likelihood of the observed actions with the prior over mental states. This approach formalizes in precise probabilistic terms the essence of previous qualitative approaches to action understanding based on an “intentional stance” [Dennett, D. C. (1987). The intentional stance. Cambridge, MA: MIT Press] or a “teleological stance” [Gergely, G., Nádasdy, Z., Csibra, G., & Biró, S. (1995). Taking the intentional stance at 12 months of age. Cognition, 56, 165-193]. In three psychophysical experiments using animated stimuli of agents moving in simple mazes, we assess how well different inverse planning models based on different goal priors can predict human goal inferences. The results provide quantitative evidence for an approximately rational inference mechanism in human goal inference within our simplified stimulus paradigm, and for the flexible nature of goal representations that human observers can adopt. We discuss the implications of our experimental results for human action understanding in real-world contexts, and suggest how our framework might be extended to capture other kinds of mental state inferences, such as inferences about beliefs, or inferring whether an entity is an intentional agent.     

Learning overhypotheses with hierarchical Bayesian models

Inductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical Bayesian models can help to explain how the rest are acquired. To illustrate this claim, we develop models that acquire two kinds of overhypotheses--overhypotheses about feature variability (e.g. the shape bias in word learning) and overhypotheses about the grouping of categories into ontological kinds like objects and substances.     

Parent-child conversations about science: the socialization of gender inequities?

This study investigated the family as a context for the gender typing of science achievement. Adolescents (N = 52) from 2 age levels (mean ages = 11 and 13 years) participated with their mothers and fathers on separate occasions; families were from predominantly middle-income European American backgrounds. Questionnaires measured the parents' and the child's attitudes. Each parent also engaged his or her child in 4 structured teaching activities (including science and nonscience tasks). There were no child gender or grade-level differences in children's science-related grades, self-efficacy, or interest. However, parents were more likely to believe that science was less interesting and more difficult for daughters than sons. In addition, parents' beliefs significantly predicted children's interest and self-efficacy in science. When parents' teaching language was examined, fathers tended to use more cognitively demanding speech with sons than with daughters during one of the science tasks.     

Dispositional and task-specific social-cognitive determinants of physical effort perseverance

The authors performed 3 studies to investigate the effects of social-cognitive variables on physical effort perseverance. Linear hierarchical regressions indicated that task-specific variables and perceived ability or competence accounted for the majority of perseverance variance in all 3 studies. The strongest single predictors in this cluster of variables were perceived competence, confidence, and readiness to invest effort. Physical self-health and ability accounted for a lesser portion of effort perseverance variance, with self-presentation confidence being the major single predictor in this cluster. The goal orientation cluster accounted for the least amount of effort perseverance variance. Together with task-specific confidence and the readiness to invest effort, as well as determination and commitment and competence, the findings support the contention that task-specific efficacious beliefs to a large extent determine persistence and endurance behaviors.     

A probabilistic model of theory formation

Concept learning is challenging in part because the meanings of many concepts depend on their relationships to other concepts. Learning these concepts in isolation can be difficult, but we present a model that discovers entire systems of related concepts. These systems can be viewed as simple theories that specify the concepts that exist in a domain, and the laws or principles that relate these concepts. We apply our model to several real-world problems, including learning the structure of kinship systems and learning ontologies. We also compare its predictions to data collected in two behavioral experiments. Experiment 1 shows that our model helps to explain how simple theories are acquired and used for inductive inference. Experiment 2 suggests that our model provides a better account of theory discovery than a more traditional alternative that focuses on features rather than relations.