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

TL;DR

This study compares extinction techniques for reducing relapse in cue-exposure therapy and finds that super-extinction leads to more recovery than standard methods.

Contribution

The study evaluates super and deepened-extinction protocols and provides new insights into their effectiveness compared to standard extinction.

Findings

Super-extinction produced more response recovery than single-cue extinction.

Configural associative models better explain the results than elemental models.

The Pearce configural model outperformed the configural Rescorla–Wagner model.

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.

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