» Articles » PMID: 35571864

Dissecting EXIT

Overview
Journal J Math Psychol
Date 2022 May 16
PMID 35571864
Authors
Affiliations
Soon will be listed here.
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.

Citing Articles

Better generalization through distraction? Concurrent load reduces the size of the inverse base-rate effect.

Dome L, Wills A Psychon Bull Rev. 2025; .

PMID: 40000598 DOI: 10.3758/s13423-025-02661-1.


Explaining the Return of Fear with Revised Rescorla-Wagner Models.

Paskewitz S, Stoddard J, Jones M Comput Psychiatr. 2024; 6(1):213-237.

PMID: 38774783 PMC: 11104307. DOI: 10.5334/cpsy.88.


A Statistical Foundation for Derived Attention.

Paskewitz S, Jones M J Math Psychol. 2023; 112.

PMID: 36909347 PMC: 10004174. DOI: 10.1016/j.jmp.2022.102728.


The quest for simplicity in human learning: Identifying the constraints on attention.

Galdo M, Weichart E, Sloutsky V, Turner B Cogn Psychol. 2022; 138:101508.

PMID: 36152354 PMC: 10324982. DOI: 10.1016/j.cogpsych.2022.101508.


As within, so without, as above, so below: Common mechanisms can support between- and within-trial category learning dynamics.

Weichart E, Galdo M, Sloutsky V, Turner B Psychol Rev. 2022; 129(5):1104-1143.

PMID: 35849355 PMC: 10321570. DOI: 10.1037/rev0000381.


References
1.
Gershman S . Dopamine, Inference, and Uncertainty. Neural Comput. 2017; 29(12):3311-3326. DOI: 10.1162/neco_a_01023. View

2.
Kruschke J . Base rates in category learning. J Exp Psychol Learn Mem Cogn. 1996; 22(1):3-26. DOI: 10.1037//0278-7393.22.1.3. View

3.
Medin D, Edelson S . Problem structure and the use of base-rate information from experience. J Exp Psychol Gen. 1988; 117(1):68-85. DOI: 10.1037//0096-3445.117.1.68. View

4.
Don H, Livesey E . Effects of outcome and trial frequency on the inverse base-rate effect. Mem Cognit. 2016; 45(3):493-507. DOI: 10.3758/s13421-016-0667-y. View

5.
Le Pelley M, Beesley T, Griffiths O . Overt attention and predictiveness in human contingency learning. J Exp Psychol Anim Behav Process. 2011; 37(2):220-9. DOI: 10.1037/a0021384. View