The fragility of implicit evaluation updating: The role of cognitive and ecological constraints
Bibliographic record
Abstract
Although negative implicit (automatic) evaluations of even well-known social targets can show remarkable temporary shifts toward positivity, such shifts rarely persist over time. Here we report 9 experiments (8 preregistered; n = 2,717) that used novel social targets to investigate two potential explanations for the intransigence of implicit evaluations: cognitive constraints reflecting a fundamental inability of implicit evaluations to adaptively and enduringly incorporate new information vs. ecological constraints reflecting moderately negative but ubiquitous cues present in one’s environment. In Exp. 1–2, we identified two procedures that used diagnostic behavioral information to successfully overturn experimentally created negative implicit (AMP) evaluations of a novel target. In Exp. 3, negative-to-positive changes induced by these procedures persisted without any decrement over a 2-day delay, thus militating against the possibility of cognitive constraints on durable implicit evaluation updating. However, in line with the ecological constraint hypothesis, when participants were exposed to moderately negative information with limited diagnosticity on day 3, implicit evaluations became markedly negative (Exp. 4, 5A, and 5C). Such return of early negativity generalized across initial learning modalities and types of subsequent ecological cues (Exp. 6) but not to explicit evaluations (Exp. 4–6). Together, the present data suggest that although implicit evaluations are cognitively capable of changing in the long term, even moderately negative information present in the environment can easily undermine interventions designed to create implicit evaluation change. We discuss implications for the basic nature of implicit social cognitive processes and the possibility of successful debiasing interventions.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".