Literature DB >> 22391153

Extinction from a rationalist perspective.

C R Gallistel1.   

Abstract

The merging of the computational theory of mind and evolutionary thinking leads to a kind of rationalism, in which enduring truths about the world have become implicit in the computations that enable the brain to cope with the experienced world. The dead reckoning computation, for example, is implemented within the brains of animals as one of the mechanisms that enables them to learn where they are (Gallistel, 1990, 1995). It integrates a velocity signal with respect to a time signal. Thus, the manner in which position and velocity relate to one another in the world is reflected in the manner in which signals representing those variables are processed in the brain. I use principles of information theory and Bayesian inference to derive from other simple principles explanations for: (1) the failure of partial reinforcement to increase reinforcements to acquisition; (2) the partial reinforcement extinction effect; (3) spontaneous recovery; (4) renewal; (5) reinstatement; (6) resurgence (aka facilitated reacquisition). Like the principle underlying dead-reckoning, these principles are grounded in analytic considerations. They are the kind of enduring truths about the world that are likely to have shaped the brain's computations.
Copyright © 2012 Elsevier B.V. All rights reserved.

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Year:  2012        PMID: 22391153      PMCID: PMC3350810          DOI: 10.1016/j.beproc.2012.02.008

Source DB:  PubMed          Journal:  Behav Processes        ISSN: 0376-6357            Impact factor:   1.777


  34 in total

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4.  Feeling the future: experimental evidence for anomalous retroactive influences on cognition and affect.

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5.  Dynamic averaging and foraging decisions in horses (Equus callabus).

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Journal:  J Comp Psychol       Date:  2005-08       Impact factor: 2.231

Review 6.  Context, time, and memory retrieval in the interference paradigms of Pavlovian learning.

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Journal:  Psychol Bull       Date:  1993-07       Impact factor: 17.737

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8.  Time and Associative Learning.

Authors:  Peter D Balsam; Michael R Drew; C R Gallistel
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9.  Temporal maps and informativeness in associative learning.

Authors:  Peter D Balsam; C Randy Gallistel
Journal:  Trends Neurosci       Date:  2009-01-10       Impact factor: 13.837

10.  Balancing variable patch quality with predation risk.

Authors:  Michael F Winterrowd; Lynn D Devenport
Journal:  Behav Processes       Date:  2004-07-30       Impact factor: 1.777

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  15 in total

1.  Rescaling of temporal expectations during extinction.

Authors:  Michael R Drew; Carolyn Walsh; Peter D Balsam
Journal:  J Exp Psychol Anim Learn Cogn       Date:  2017-01       Impact factor: 2.478

2.  Information: theory, brain, and behavior.

Authors:  Greg Jensen; Ryan D Ward; Peter D Balsam
Journal:  J Exp Anal Behav       Date:  2013-10-04       Impact factor: 2.468

3.  Individual differences in learning predict the return of fear.

Authors:  Samuel J Gershman; Catherine A Hartley
Journal:  Learn Behav       Date:  2015-09       Impact factor: 1.986

4.  Behavioral momentum and accumulation of mass in multiple schedules.

Authors:  Andrew R Craig; Paul J Cunningham; Timothy A Shahan
Journal:  J Exp Anal Behav       Date:  2015-03-18       Impact factor: 2.468

5.  Time-scale-invariant information-theoretic contingencies in discrimination learning.

Authors:  Abigail Kalmbach; Eileen Chun; Kathleen Taylor; Charles R Gallistel; Peter D Balsam
Journal:  J Exp Psychol Anim Learn Cogn       Date:  2019-04-25       Impact factor: 2.478

Review 6.  Resurgence as Choice.

Authors:  Timothy A Shahan; Andrew R Craig
Journal:  Behav Processes       Date:  2016-10-26       Impact factor: 1.777

7.  The computational nature of memory modification.

Authors:  Samuel J Gershman; Marie-H Monfils; Kenneth A Norman; Yael Niv
Journal:  Elife       Date:  2017-03-15       Impact factor: 8.140

8.  Behavioral momentum theory fails to account for the effects of reinforcement rate on resurgence.

Authors:  Andrew R Craig; Timothy A Shahan
Journal:  J Exp Anal Behav       Date:  2016-05       Impact factor: 2.468

9.  Separation of time-based and trial-based accounts of the partial reinforcement extinction effect.

Authors:  Mark E Bouton; Amanda M Woods; Travis P Todd
Journal:  Behav Processes       Date:  2013-08-17       Impact factor: 1.777

Review 10.  Time to rethink the neural mechanisms of learning and memory.

Authors:  Charles R Gallistel; Peter D Balsam
Journal:  Neurobiol Learn Mem       Date:  2013-12-03       Impact factor: 2.877

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