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Record W3208250726 · doi:10.1016/j.bbr.2021.113632

Imaginal extinction and the vividness of mental imagery: Exploring the reduction of fear within the mind’s eye

2021· article· en· W3208250726 on OpenAlexfundno aff
Johanna M. Hoppe, Emily A. Holmes, Thomas Ågren

Bibliographic record

VenueBehavioural Brain Research · 2021
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsnot available
FundersVetenskapsrådetLupina FoundationOak Foundation
KeywordsPsychologyMental imageExtinction (optical mineralogy)Stimulus (psychology)Fear conditioningDevelopmental psychologyCognitive psychologyCognitionNeuroscienceAmygdalaChemistry

Abstract

fetched live from OpenAlex

Patients are encouraged to produce vivid mental imagery during imaginal exposure, as it is assumed to promote fear reduction. Nevertheless, the link between fear reduction and imagery vividness is unclear. We investigated the impact of vividness on fear responses using an experimental analogue of imaginal exposure - imaginal extinction - in which conditioned fear, measured with skin conductance, is reduced through exposure to mental imagery of the conditioned stimulus. We examined (1) if task-specific vividness (high vs low) of the conditioned stimulus during imaginal extinction moderated the reduction of fear responses, and (2) if task-specific vividness influenced remaining fear responses 24 h later. Findings suggest that high vividness may be advantageous for fear reduction during imaginal extinction, but it may not influence fear responses in the longer term. A possible clinical implication is that high imagery vividness during imaginal exposure may not be vital for overall treatment outcome. As high vividness is associated with increased levels of distress, a future direction would be to explore whether similar fear reduction can be obtained with less vivid imaginal exposure and thereby make treatment tolerable for more patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.176
GPT teacher head0.433
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations24
Published2021
Admission routes1
Has abstractyes

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