Add to ORKGResearch in computer science, engineering, mathematics, and statistics has produced a variety of tools that are useful in developing probabilistic models of human cognition. We provide an introduction to the principles of probabilistic inference that are used in the papers appearing in this special issue. We lay out the basic principles that underlie probabilistic models in detail, and then briefly survey some of the tools that can be used in applying these models to human cognition
Cognitive science aims to reverse-engineer the mind, and many of the engineering challenges the mind...
Cognitive science aims to reverse-engineer the mind, and many of the engineering challenges the mind...
AbstractA basic challenge for probabilistic models of cognition is explaining how probabilistically ...
Research in computer science, engineering, mathematics, and statistics has produced a variety of too...
Research in computer science, engineering, mathematics, and statistics has produced a variety of to...
In this article, which is a supplementary article to the TICS July Special Issue on probabilistic mo...
In this article, which is a supplementary article to the TICS July Special Issue on probabilistic mo...
In this chapter, we introduce some of the tools that can be used to address these challenges. By con...
In this chapter, we introduce some of the tools that can be used to address these challenges. By con...
Probabilistic models of cognition characterize the abstract computational problems underlying induct...
We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive ...
Inductive inference allows humans to make powerful generalizations from sparse data when learning ab...
Although probabilistic knowledge representations and probabilistic reasoning have by now secured the...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2...
Cognitive science aims to reverse-engineer the mind, and many of the engineering challenges the mind...
Cognitive science aims to reverse-engineer the mind, and many of the engineering challenges the mind...
Cognitive science aims to reverse-engineer the mind, and many of the engineering challenges the mind...
AbstractA basic challenge for probabilistic models of cognition is explaining how probabilistically ...
Research in computer science, engineering, mathematics, and statistics has produced a variety of too...
Research in computer science, engineering, mathematics, and statistics has produced a variety of to...
In this article, which is a supplementary article to the TICS July Special Issue on probabilistic mo...
In this article, which is a supplementary article to the TICS July Special Issue on probabilistic mo...
In this chapter, we introduce some of the tools that can be used to address these challenges. By con...
In this chapter, we introduce some of the tools that can be used to address these challenges. By con...
Probabilistic models of cognition characterize the abstract computational problems underlying induct...
We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive ...
Inductive inference allows humans to make powerful generalizations from sparse data when learning ab...
Although probabilistic knowledge representations and probabilistic reasoning have by now secured the...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2...
Cognitive science aims to reverse-engineer the mind, and many of the engineering challenges the mind...
Cognitive science aims to reverse-engineer the mind, and many of the engineering challenges the mind...
Cognitive science aims to reverse-engineer the mind, and many of the engineering challenges the mind...
AbstractA basic challenge for probabilistic models of cognition is explaining how probabilistically ...