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Piantadosi, Steven T.; Tenenbaum, Joshua B.; Goodman, Noah D. – Cognition, 2012
In acquiring number words, children exhibit a qualitative leap in which they transition from understanding a few number words, to possessing a rich system of interrelated numerical concepts. We present a computational framework for understanding this inductive leap as the consequence of statistical inference over a sufficiently powerful…
Descriptors: Statistical Inference, Number Concepts, Models, Computation
Griffiths, Thomas L.; Tenenbaum, Joshua B. – Journal of Experimental Psychology: General, 2011
Predicting the future is a basic problem that people have to solve every day and a component of planning, decision making, memory, and causal reasoning. In this article, we present 5 experiments testing a Bayesian model of predicting the duration or extent of phenomena from their current state. This Bayesian model indicates how people should…
Descriptors: Bayesian Statistics, Statistical Inference, Models, Prior Learning
Kemp, Charles; Tenenbaum, Joshua B. – Psychological Review, 2009
Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. This article presents a Bayesian framework that attempts to meet…
Descriptors: Logical Thinking, Inferences, Statistical Inference, Models
Griffiths, Thomas L.; Tenenbaum, Joshua B. – Psychological Review, 2009
Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations…
Descriptors: Causal Models, Prior Learning, Logical Thinking, Statistical Inference
Griffiths, Thomas L.; Steyvers, Mark; Tenenbaum, Joshua B. – Psychological Review, 2007
Processing language requires the retrieval of concepts from memory in response to an ongoing stream of information. This retrieval is facilitated if one can infer the gist of a sentence, conversation, or document and use that gist to predict related concepts and disambiguate words. This article analyzes the abstract computational problem…
Descriptors: Language Processing, Information Retrieval, Fundamental Concepts, Syntax
Griffiths, Thomas L.; Tenenbaum, Joshua B. – Cognition, 2007
People's reactions to coincidences are often cited as an illustration of the irrationality of human reasoning about chance. We argue that coincidences may be better understood in terms of rational statistical inference, based on their functional role in processes of causal discovery and theory revision. We present a formal definition of…
Descriptors: Probability, Statistical Inference, Bayesian Statistics, Theories