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→Bush and Mosteller Learning Curve
[[File:Bush and Mosteller eq2.gif]]
Fig 1: “Weights determining the effects of previous rewards on current associative strength effectively decline as an exponential function of time” (65).
an exponential series, the rate at which the weight declines being controlled by α.
[[File:Bush and Mosteller graph1.gif||||“Weights determining the effects of previous rewards on current associative strength effectively decline as an exponential function of time” (65). [Reproduced with permission from Oxford University Press from ref. 65 (Copyright 2010, Paul W. Glimcher).)]]
The Bush and Mosteller (23, 24) equation was critically important, because it was the first use of this kind of iterative error-based rule for reinforcement learning; additionally, it forms the basis of all modern approaches to this problem. This is a fact often obscured by what is known as the Rescorla–Wagner model of classical conditioning (25). The Rescorla–Wagner model was an important extension of the Bush and Mosteller approach (23, 24) to the study of what happens to associative strength when two cues predict the same event. Their findings were so influential that the basic Bush and Mosteller rule is now often mistakenly attributed to Rescorla and Wagner by neurobiologists.
==References==
<references>
[[category: epistemiology]]