Bayesian models provide a principled way to deal with uncertainty. In cognitive tasks the uncertainty is mainly in how to represent the observed data, as there is usually not enough data to uniquely determine the representation. Bayesian models of cognition implement a general method for dealing with representational uncertainty which can be applied to explain human performance in various cognitive tasks
International audienceHow can an incomplete and uncertain model of the environment be used to percei...
Abstract How can an incomplete and uncertain model of the environment be used to perceive, infer, de...
The development of cognitive models involves the creative scientific formalization of assumptions, b...
There has been a recent explosion in research applying Bayesian models to cognitive phenomena. This ...
Item does not contain fulltextThis chapter provides an introduction to Bayesian models and their app...
Bayesian models of cognition provide a powerful way to understand the behavior and goals of individu...
A widely shared view in the cognitive sciences is that discovering and assessing explanations of cog...
We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive ...
Probability theory forms a natural framework for explaining the impressive success of people at solv...
Recent debates in the psychological literature have raised questions about what assumptions underpin...
Recent debates in the psychological literature have raised questions about the assumptions that unde...
Human thought is remarkably flexible: we can think about infinitely many different situations despit...
Bayesian models of human learning are becoming increasingly popular in cognitive science. We argue t...
Abstract: The prominence of Bayesian modeling of cognition has increased recently largely because of...
Cognitive systems, whether human or engineered, must often reason from and act on probabilistic info...
International audienceHow can an incomplete and uncertain model of the environment be used to percei...
Abstract How can an incomplete and uncertain model of the environment be used to perceive, infer, de...
The development of cognitive models involves the creative scientific formalization of assumptions, b...
There has been a recent explosion in research applying Bayesian models to cognitive phenomena. This ...
Item does not contain fulltextThis chapter provides an introduction to Bayesian models and their app...
Bayesian models of cognition provide a powerful way to understand the behavior and goals of individu...
A widely shared view in the cognitive sciences is that discovering and assessing explanations of cog...
We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive ...
Probability theory forms a natural framework for explaining the impressive success of people at solv...
Recent debates in the psychological literature have raised questions about what assumptions underpin...
Recent debates in the psychological literature have raised questions about the assumptions that unde...
Human thought is remarkably flexible: we can think about infinitely many different situations despit...
Bayesian models of human learning are becoming increasingly popular in cognitive science. We argue t...
Abstract: The prominence of Bayesian modeling of cognition has increased recently largely because of...
Cognitive systems, whether human or engineered, must often reason from and act on probabilistic info...
International audienceHow can an incomplete and uncertain model of the environment be used to percei...
Abstract How can an incomplete and uncertain model of the environment be used to perceive, infer, de...
The development of cognitive models involves the creative scientific formalization of assumptions, b...
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