Social evaluations under conflict: negative judgments of conflicting information are easier than positive judgments
Bibliographic record
Abstract
In the current study, we used functional magnetic resonance imaging to investigate how the brain facilitates social judgments despite evaluatively conflicting information. Participants learned consistent (positive or negative) and ambivalent (positive and negative) person information and were then asked to provide binary judgments of these targets in situations that either resolved conflict by prioritizing a subset of information or not. Self-report, decision time and brain data confirm that integrating contextual information into our evaluations of objects or people allows for nuanced (social) evaluations. The same mixed trait information elicited or failed to elicit evaluative conflict dependent on the situation. Crucially, we provide data suggesting that negative judgments are easier and may be considered the 'default' action when experiencing evaluative conflict: weaker activation in dorsolateral prefrontal cortex during trials of evaluative conflict was related to a greater likelihood of unfavorable judgments, and greater activation was related to more favorable judgments. Since negative outcome consequences are arguably more detrimental and salient, this finding supports the idea that additional regulation and a more active selection process are necessary to override an initial negative response to evaluatively conflicting information.
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 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 | 0.002 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".