The computation of contingency in classical conditioning

Publisher Summary This chapter discusses a unified framework, which encompasses the computations, algorithms, and neurobiological implementations underlying classical conditioning. It presents an extensive mathematical analysis of the constraints on classical conditioning—that is, the precise contingency conditions under which mammals may and may not learn a particular association between two events in a classical conditioning situation. In classical conditioning, an unconditional stimulus (US)—that is, a cue, which is inherently biologically salient to an animal (such as an electric shock), is repeatedly paired with a conditional stimulus (CS), a cue that initially has no special significance to the animal over repeated trials, the animal can learn that the CS is predictive of or associated with the US. This phenomenon of associative learning is subject to laws and constraints: An association is learned to some extent in some conditions and to a lesser extent in others.

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