Paranoid and teleological thinking give rise to distinct social hallucinations in vision
Bibliographic record
Abstract
Paranoia (believing others intend harm) and excess teleological thinking (ascribing too much purpose) are non-consensual beliefs about agents. Human vision rapidly detects agents and their intentions. Might paranoia and teleology have roots in visual perception? Using displays that evoke the impression that one disc (‘wolf’) is chasing another (‘sheep’), we find that paranoia and teleology involve perceiving chasing when there is none (studies 1 and 2) — errors we characterize as social hallucinations. When asked to identify the wolf or the sheep (studies 3, 4a, and 4b), we find high-paranoia participants struggled to identify sheep, while high-teleology participants were impaired at identifying wolves — both despite high-confidence. Both types of errors correlated with hallucinatory percepts in the real world. Although paranoia and teleology both involve excess perception of agency, the current results collectively suggest a perceptual distinction between the two, perhaps with clinical import. When asked to judge if a chase was present in a visual display of moving discs, people with higher paranoia and teleological thinking were more likely to perceive a chase in its absence. They were also worse at detecting the chaser and the chased, yet highly confident when there was no chase.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".