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Record W2199914527 · doi:10.1111/jir.12246

Affective learning in adults with intellectual disability: an experiment using evaluative conditioning

2015· article· en· W2199914527 on OpenAlexaff
Isabelle Blanchette, Véronique Treillet, Sarah Davies

Bibliographic record

VenueJournal of Intellectual Disability Research · 2015
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsPsychologyConditioningExtinction (optical mineralogy)Association (psychology)Developmental psychologyCognitionClassical conditioningLearning disabilityCognitive psychologyAudiologyNeurosciencePsychotherapist

Abstract

fetched live from OpenAlex

BACKGROUND: Evaluative conditioning is a form of affective learning in which initially neutral stimuli acquire an affective value through association with negative or positive stimuli. Recent research shows an important role for cognitive resources in this type of learning. This form of affective learning has rarely been studied in intellectual disability (ID). METHOD: We examined evaluative conditioning in 16 adults with mild to moderate ID compared to age- and gender-matched control participants. Neutral shapes and symbols were repeatedly paired with positive, neutral or negative unconditioned stimuli (faces or International Affective Picture System images). There was also an extinction phase. RESULTS: There was significant acquisition of conditioning in both groups. Stimuli paired with positive images were evaluated more positively, and stimuli paired with negative images were evaluated more negatively. Post-extinction ratings however show that these novel affective associations were not maintained by individuals with ID as much as by individuals in the control group. CONCLUSIONS: We conclude that ID modulates some aspects of affective learning but not necessarily initial preference acquisition.

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)0.008
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.511
GPT teacher head0.519
Teacher spread0.008 · 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.

Study designQualitative
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

Citations3
Published2015
Admission routes1
Has abstractyes

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