Literature DB >> 35571864

Dissecting EXIT.

Samuel Paskewitz1, Matt Jones1.   

Abstract

Kruschke's EXIT model (Kruschke, 2001b) has been very successful in explaining a variety of learning phenomena by means of selective attention. In particular, EXIT produces learned predictiveness effects (Le Pelley & McLaren, 2003), the inverse base rate effect (Kruschke, 1996; Medin & Edelson, 1988), inattention after blocking (Beesley & Le Pelley, 2011; Kruschke & Blair, 2000), differential cue use across the stimulus space (Aha & Goldstone, 1992) and conditional learned predictiveness effects (Uengoer, Lachnit, Lotz, Koenig, & Pearce, 2013). We dissect EXIT into its component mechanisms (error-driven learning, selective attention, attentional competition, rapid attention shifts and exemplar mediation of attention) and test whether simplified versions of EXIT can explain the same experimental results as the full model. Most phenomena can be explained by either rapid attention shifts or attentional competition, without the need for combining them as in EXIT. There is little evidence for exemplar mediation of attention when people learn linearly separable category structures (e.g. Kruschke & Blair, 2000; Le Pelley & McLaren, 2003); whether or not it is needed for non-linear categories depends on stimulus representation (Aha & Goldstone, 1992; Uengoer et al., 2013). On the whole, we believe that attentional competition-embodied in a model which we dub CompAct-offers the simplest explanation for the experimental results we examine.

Entities:  

Year:  2020        PMID: 35571864      PMCID: PMC9098183          DOI: 10.1016/j.jmp.2020.102371

Source DB:  PubMed          Journal:  J Math Psychol        ISSN: 0022-2496            Impact factor:   1.387


  27 in total

1.  Learned associability and associative change in human causal learning.

Authors:  M E Le Pelley; I P L McLaren
Journal:  Q J Exp Psychol B       Date:  2003-02

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

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

3.  Dopamine, Inference, and Uncertainty.

Authors:  Samuel J Gershman
Journal:  Neural Comput       Date:  2017-09-28       Impact factor: 2.026

4.  Effects of outcome and trial frequency on the inverse base-rate effect.

Authors:  Hilary J Don; Evan J Livesey
Journal:  Mem Cognit       Date:  2017-04

5.  Problem structure and the use of base-rate information from experience.

Authors:  D L Medin; S M Edelson
Journal:  J Exp Psychol Gen       Date:  1988-03

6.  Rule-plus-exception model of classification learning.

Authors:  R M Nosofsky; T J Palmeri; S C McKinley
Journal:  Psychol Rev       Date:  1994-01       Impact factor: 8.934

7.  Overt attention and predictiveness in human contingency learning.

Authors:  M E Le Pelley; Tom Beesley; Oren Griffiths
Journal:  J Exp Psychol Anim Behav Process       Date:  2011-04

8.  Base rates in category learning.

Authors:  J K Kruschke
Journal:  J Exp Psychol Learn Mem Cogn       Date:  1996-01       Impact factor: 3.051

9.  Attentional changes in blocking are not a consequence of lateral inhibition.

Authors:  Oren Griffiths; M E Le Pelley
Journal:  Learn Behav       Date:  2009-02       Impact factor: 1.986

10.  Contextual control of attentional allocation in human discrimination learning.

Authors:  Metin Uengoer; Harald Lachnit; Anja Lotz; Stephan Koenig; John M Pearce
Journal:  J Exp Psychol Anim Behav Process       Date:  2012-11-12
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