Are We Puppets on a String? Comparing the Impact of Contingency and Validity on Implicit and Explicit Evaluations
Bibliographic record
Abstract
Research has demonstrated that implicit and explicit evaluations of the same object can diverge. Explanations of such dissociations frequently appeal to dual-process theories, such that implicit evaluations are assumed to reflect object-valence contingencies independent of their perceived validity, whereas explicit evaluations reflect the perceived validity of object-valence contingencies. Although there is evidence supporting these assumptions, it remains unclear if dissociations can arise in situations in which object-valence contingencies are judged to be true or false during the learning of these contingencies. Challenging dual-process accounts that propose a simultaneous operation of two parallel learning mechanisms, results from three experiments showed that the perceived validity of evaluative information about social targets qualified both explicit and implicit evaluations when validity information was available immediately after the encoding of the valence information; however, delaying the presentation of validity information reduced its qualifying impact for implicit, but not explicit, evaluations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".