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. 2025 Jul 15.
doi: 10.3758/s13420-025-00681-4. Online ahead of print.

Super and deepened-extinction in human predictive learning and a comparison of associative models

Ovidiu Brudan  1 , Hedwig Eisenbarth  2 , Steven Glautier  3
Affiliations

Super and deepened-extinction in human predictive learning and a comparison of associative models

Ovidiu Brudan et al. Learn Behav. .

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.

Keywords: Akaike information; Associative learning; Configural; Extinction; Maximum likelihood; Pearce; Relapse; Rescorla–Wagner; Response recovery.

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Conflict of interest statement

Declaration. Conflicts of Interest/Competing Interests: None. Ethics Approval: The procedures were approved by the Ethics Committee at the University of Southampton. Consent to Participate: All participants read an information sheet and gave informed consent to participate. Consent for Publication: All participants agreed to the publication of anonymous data.

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