Literature DB >> 23046090

Erasing the engram: the unlearning of procedural skills.

Matthew J Crossley1, F Gregory Ashby, W Todd Maddox.   

Abstract

Huge amounts of money are spent every year on unlearning programs--in drug-treatment facilities, prisons, psychotherapy clinics, and schools. Yet almost all of these programs fail, since recidivism rates are high in each of these fields. Progress on this problem requires a better understanding of the mechanisms that make unlearning so difficult. Much cognitive neuroscience evidence suggests that an important component of these mechanisms also dictates success on categorization tasks that recruit procedural learning and depend on synaptic plasticity within the striatum. A biologically detailed computational model of this striatal-dependent learning is described (based on Ashby & Crossley, 2011). The model assumes that a key component of striatal-dependent learning is provided by interneurons in the striatum called the tonically active neurons (TANs), which act as a gate for the learning and expression of striatal-dependent behaviors. In their tonically active state, the TANs prevent the expression of any striatal-dependent behavior. However, they learn to pause in rewarding environments and thereby permit the learning and expression of striatal-dependent behaviors. The model predicts that when rewards are no longer contingent on behavior, the TANs cease to pause, which protects striatal learning from decay and prevents unlearning. In addition, the model predicts that when rewards are partially contingent on behavior, the TANs remain partially paused, leaving the striatum available for unlearning. The results from 3 human behavioral studies support the model predictions and suggest a novel unlearning protocol that shows promising initial signs of success. PsycINFO Database Record (c) 2013 APA, all rights reserved.

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Year:  2012        PMID: 23046090      PMCID: PMC3543754          DOI: 10.1037/a0030059

Source DB:  PubMed          Journal:  J Exp Psychol Gen        ISSN: 0022-1015


  79 in total

1.  The effects of concurrent task interference on category learning: evidence for multiple category learning systems.

Authors:  E M Waldron; F G Ashby
Journal:  Psychon Bull Rev       Date:  2001-03

2.  Generalizing a neuropsychological model of visual categorization to auditory categorization of vowels.

Authors:  W Todd Maddox; Michelle R Molis; Randy L Diehl
Journal:  Percept Psychophys       Date:  2002-05

3.  ALCOVE: an exemplar-based connectionist model of category learning.

Authors:  J K Kruschke
Journal:  Psychol Rev       Date:  1992-01       Impact factor: 8.934

4.  A neurobiological theory of automaticity in perceptual categorization.

Authors:  F Gregory Ashby; John M Ennis; Brian J Spiering
Journal:  Psychol Rev       Date:  2007-07       Impact factor: 8.934

Review 5.  Goal-directed instrumental action: contingency and incentive learning and their cortical substrates.

Authors:  B W Balleine; A Dickinson
Journal:  Neuropharmacology       Date:  1998 Apr-May       Impact factor: 5.250

Review 6.  A neural substrate of prediction and reward.

Authors:  W Schultz; P Dayan; P R Montague
Journal:  Science       Date:  1997-03-14       Impact factor: 47.728

7.  Array models for category learning.

Authors:  W K Estes
Journal:  Cogn Psychol       Date:  1986-10       Impact factor: 3.468

8.  A retrieval cue for extinction attenuates spontaneous recovery.

Authors:  D C Brooks; M E Bouton
Journal:  J Exp Psychol Anim Behav Process       Date:  1993-01

9.  Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey.

Authors:  M N Shadlen; W T Newsome
Journal:  J Neurophysiol       Date:  2001-10       Impact factor: 2.714

10.  A neuropsychological theory of multiple systems in category learning.

Authors:  F G Ashby; L A Alfonso-Reese; A U Turken; E M Waldron
Journal:  Psychol Rev       Date:  1998-07       Impact factor: 8.934

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  13 in total

Review 1.  A matched filter hypothesis for cognitive control.

Authors:  Evangelia G Chrysikou; Matthew J Weber; Sharon L Thompson-Schill
Journal:  Neuropsychologia       Date:  2013-11-05       Impact factor: 3.139

2.  Dopamine dependence in aggregate feedback learning: A computational cognitive neuroscience approach.

Authors:  Vivian V Valentin; W Todd Maddox; F Gregory Ashby
Journal:  Brain Cogn       Date:  2016-09-03       Impact factor: 2.310

3.  Increased cognitive load enables unlearning in procedural category learning.

Authors:  Matthew J Crossley; W Todd Maddox; F Gregory Ashby
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2018-04-19       Impact factor: 3.051

4.  Perceptual category learning and visual processing: An exercise in computational cognitive neuroscience.

Authors:  George Cantwell; Maximilian Riesenhuber; Jessica L Roeder; F Gregory Ashby
Journal:  Neural Netw       Date:  2017-03-06

5.  The time course of explicit and implicit categorization.

Authors:  J David Smith; Alexandria C Zakrzewski; Eric R Herberger; Joseph Boomer; Jessica L Roeder; F Gregory Ashby; Barbara A Church
Journal:  Atten Percept Psychophys       Date:  2015-10       Impact factor: 2.199

6.  A neural interpretation of exemplar theory.

Authors:  F Gregory Ashby; Luke Rosedahl
Journal:  Psychol Rev       Date:  2017-04-06       Impact factor: 8.934

7.  Context-dependent savings in procedural category learning.

Authors:  Matthew J Crossley; F Gregory Ashby; W Todd Maddox
Journal:  Brain Cogn       Date:  2014-10-17       Impact factor: 2.310

Review 8.  Multiple stages of learning in perceptual categorization: evidence and neurocomputational theory.

Authors:  George Cantwell; Matthew J Crossley; F Gregory Ashby
Journal:  Psychon Bull Rev       Date:  2015-12

9.  The role of feedback contingency in perceptual category learning.

Authors:  F Gregory Ashby; Lauren E Vucovich
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2016-05-05       Impact factor: 3.051

10.  Modulation of Dopamine for Adaptive Learning: A Neurocomputational Model.

Authors:  Jeffrey B Inglis; Vivian V Valentin; F Gregory Ashby
Journal:  Comput Brain Behav       Date:  2020-06-12
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