Literature DB >> 31464474

Contingency, contiguity, and causality in conditioning: Applying information theory and Weber's Law to the assignment of credit problem.

C R Gallistel1, Andrew R Craig1, Timothy A Shahan1.   

Abstract

Contingency is a critical concept for theories of associative learning and the assignment of credit problem in reinforcement learning. Measuring and manipulating it has, however, been problematic. The information-theoretic definition of contingency-normalized mutual information-makes it a readily computed property of the relation between reinforcing events, the stimuli that predict them and the responses that produce them. When necessary, the dynamic range of the required temporal representation divided by the Weber fraction gives a psychologically realistic plug-in estimates of the entropies. There is no measurable prospective contingency between a peck and reinforcement when pigeons peck on a variable interval schedule of reinforcement. There is, however, a perfect retrospective contingency between reinforcement and the immediately preceding peck. Degrading the retrospective contingency by gratis reinforcement reveals a critical value (.25), below which performance declines rapidly. Contingency is time scale invariant, whereas the perception of proximate causality depends-we assume-on there being a short, fixed psychologically negligible critical interval between cause and effect. Increasing the interval between a response and reinforcement that it triggers degrades the retrograde contingency, leading to a decline in performance that restores it to at or above its critical value. Thus, there is no critical interval in the retrospective effect of reinforcement. We conclude with a short review of the broad explanatory scope of information-theoretic contingencies when regarded as causal variables in conditioning. We suggest that the computation of contingencies may supplant the computation of the sum of all future rewards in models of reinforcement learning. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Mesh:

Year:  2019        PMID: 31464474     DOI: 10.1037/rev0000163

Source DB:  PubMed          Journal:  Psychol Rev        ISSN: 0033-295X            Impact factor:   8.934


  4 in total

1.  Predicting the Future With a Scale-Invariant Temporal Memory for the Past.

Authors:  Wei Zhong Goh; Varun Ursekar; Marc W Howard
Journal:  Neural Comput       Date:  2022-02-17       Impact factor: 2.026

Review 2.  The learning of prospective and retrospective cognitive maps within neural circuits.

Authors:  Vijay Mohan K Namboodiri; Garret D Stuber
Journal:  Neuron       Date:  2021-10-21       Impact factor: 17.173

3.  How do real animals account for the passage of time during associative learning?

Authors:  Vijay Mohan K Namboodiri
Journal:  Behav Neurosci       Date:  2022-04-28       Impact factor: 2.154

4.  Relapse: An introduction.

Authors:  Timothy A Shahan
Journal:  J Exp Anal Behav       Date:  2020-01-03       Impact factor: 2.468

  4 in total

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