Bibliographic record
Abstract
Social power, alcohol intoxication, and anonymity all have strong influences on human cognition and behavior. However, the social consequences of each of these conditions can be diverse, sometimes producing prosocial outcomes and other times enabling antisocial behavior. We present a general model of disinhibition to explain how these seemingly contradictory effects emerge from a single underlying mechanism: The decreased salience of competing response options prevents activation of the Behavioral Inhibition System (BIS). As a result, the most salient response in any given situation is expressed, regardless of whether it has prosocial or antisocial consequences. We review three distinct routes through which power, alcohol intoxication, and anonymity reduce the salience of competing response options, namely, through Behavioral Approach System (BAS) activation, cognitive depletion, and reduced social desirability concerns. We further discuss how these states can both reveal and shape the person. Overall, our approach allows for multiple domain-specific models to be unified within a common conceptual framework that explains how both situational and dispositional factors can influence the expression of disinhibited behavior, producing both prosocial and antisocial outcomes.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".