Super and deepened-extinction in human predictive learning and a comparison of associative models
Super and deepened-extinction in human predictive learning and a comparison of associative models
Cue-exposure is a treatment (e.g. for addictions and phobias) that aims to extinguish conditioned responses to target cues. However, especially in the case of addiction, relapse still occurs after cue-exposure and this may be due to recovery of conditioned responses outside of the extinction context. Super-extinction and deepened-extinction are two compound-cue extinction procedures which have been assessed for their capacity to produce more robust extinction than standard single-cue extinction procedures. We carried out further assessment of super and deepened-extinction protocols but found no evidence that they produced less response recovery compared to single-cue extinction. Contrariwise, super-extinction actually produced more recovery than the other two conditions. These results can be understood in terms of configural associative models (configural Rescorla–Wagner and Pearce configural model) but not in terms of the simple elemental Rescorla–Wagner model. Furthermore, the configural models provided better fits to overall data, and the Pearce configural model was better than the configural Rescorla–Wagner model.
Pearce-configural, Rescorla-Wagner, akaike weight analysis, associative learning, extinction, maximum likelihood, relapse, response recovery
Brudan, Ovidiu
181623dc-85bf-4516-8383-c39a7a929464
Eisenbarth, Hedwig
ff837e52-e40e-4c77-8ec8-9fb744d2b22f
Glautier, Steven
964468b2-3ad7-40cc-b4be-e35c7dee518f
15 July 2025
Brudan, Ovidiu
181623dc-85bf-4516-8383-c39a7a929464
Eisenbarth, Hedwig
ff837e52-e40e-4c77-8ec8-9fb744d2b22f
Glautier, Steven
964468b2-3ad7-40cc-b4be-e35c7dee518f
Brudan, Ovidiu, Eisenbarth, Hedwig and Glautier, Steven
(2025)
Super and deepened-extinction in human predictive learning and a comparison of associative models.
Learning & Behavior.
(doi:10.3758/s13420-025-00681-4).
Abstract
Cue-exposure is a treatment (e.g. for addictions and phobias) that aims to extinguish conditioned responses to target cues. However, especially in the case of addiction, relapse still occurs after cue-exposure and this may be due to recovery of conditioned responses outside of the extinction context. Super-extinction and deepened-extinction are two compound-cue extinction procedures which have been assessed for their capacity to produce more robust extinction than standard single-cue extinction procedures. We carried out further assessment of super and deepened-extinction protocols but found no evidence that they produced less response recovery compared to single-cue extinction. Contrariwise, super-extinction actually produced more recovery than the other two conditions. These results can be understood in terms of configural associative models (configural Rescorla–Wagner and Pearce configural model) but not in terms of the simple elemental Rescorla–Wagner model. Furthermore, the configural models provided better fits to overall data, and the Pearce configural model was better than the configural Rescorla–Wagner model.
Text
BrudanEisenbarthGlautierSEDE_Rev1
- Accepted Manuscript
Text
s13420-025-00681-4
- Version of Record
More information
Accepted/In Press date: 12 June 2025
Published date: 15 July 2025
Keywords:
Pearce-configural, Rescorla-Wagner, akaike weight analysis, associative learning, extinction, maximum likelihood, relapse, response recovery
Identifiers
Local EPrints ID: 503341
URI: http://eprints.soton.ac.uk/id/eprint/503341
ISSN: 1543-4494
PURE UUID: d26c4ed3-6829-4b7a-a113-065f8b88d8d5
Catalogue record
Date deposited: 29 Jul 2025 16:48
Last modified: 22 Aug 2025 01:43
Export record
Altmetrics
Contributors
Author:
Hedwig Eisenbarth
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics
