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Li, Michael Y.; Callaway, Fred; Thompson, William D.; Adams, Ryan P.; Griffiths, Thomas L. – Cognitive Science, 2023
Humans can learn complex functional relationships between variables from small amounts of data. In doing so, they draw on prior expectations about the form of these relationships. In three experiments, we show that people learn to adjust these expectations through experience, learning about the likely forms of the functions they will encounter.…
Descriptors: Learning Processes, Expectation, Experience, Relationship
Whalen, Andrew; Griffiths, Thomas L.; Buchsbaum, Daphna – Cognitive Science, 2018
Social learning has been shown to be an evolutionarily adaptive strategy, but it can be implemented via many different cognitive mechanisms. The adaptive advantage of social learning depends crucially on the ability of each learner to obtain relevant and accurate information from informants. The source of informants' knowledge is a particularly…
Descriptors: Social Development, Socialization, Bayesian Statistics, Behavior Patterns
Austerweil, Joseph L.; Griffiths, Thomas L.; Palmer, Stephen E. – Cognitive Science, 2017
How does the visual system recognize images of a novel object after a single observation despite possible variations in the viewpoint of that object relative to the observer? One possibility is comparing the image with a prototype for invariance over a relevant transformation set (e.g., translations and dilations). However, invariance over…
Descriptors: Prior Learning, Inferences, Visual Acuity, Recognition (Psychology)
Kemp, Charles; Tenenbaum, Joshua B.; Niyogi, Sourabh; Griffiths, Thomas L. – Cognition, 2010
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…
Descriptors: Family Relationship, Logical Thinking, Models, Concept Formation
Goodman, Noah D.; Tenenbaum, Joshua B.; Feldman, Jacob; Griffiths, Thomas L. – Cognitive Science, 2008
This article proposes a new model of human concept learning that provides a rational analysis of learning feature-based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space--a concept language of logical rules. This article compares the model predictions to human generalization judgments in several…
Descriptors: Mathematics Education, Concept Formation, Models, Prediction
Griffiths, Thomas L.; Kalish, Michael L. – Cognitive Science, 2007
Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute…
Descriptors: Probability, Diachronic Linguistics, Statistical Inference, Language Universals

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