Shifting the sociometer: opioid receptor blockade lowers self-esteem
Bibliographic record
Abstract
Given the evolutionary importance of social ties for survival, humans are thought to have evolved psychobiological mechanisms to monitor and safeguard the status of their social bonds. At the psychological level, self-esteem is proposed to function as a gauge-'sociometer'-reflecting one's social belongingness status. At the biological level, endogenous opioids appear to be an important substrate for the hedonic signalling needed to regulate social behaviour. We investigated whether endogenous opioids may serve as the biological correlate of the sociometer. We administered 50 mg naltrexone (an opioid receptor antagonist) and placebo in a counterbalanced order to 26 male and female participants on two occasions ∼1 week apart. Participants reported lower levels of self-esteem-particularly self-liking-on the naltrexone (vs placebo) day. We also explored a potential behavioural consequence of naltrexone administration: attentional bias to accepting (smiling) faces-an early-stage perceptual process thought to maximize opportunities to restore social connection. Participants exhibited heightened attentional bias towards accepting faces on the naltrexone (vs placebo) day, which we interpret as an indicator of heightened social need under opioid receptor blockade. We discuss implications of these findings for understanding the neurobiological underpinnings of sociality as well as the relationship between adverse social conditions, low self-esteem and psychopathology.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".