The wolf or the sheep? Paranoid and teleological thinking give rise to distinct social hallucinations in vision
Bibliographic record
Abstract
Paranoia (belief that others intend harm) and excessive teleological thinking (ascription of purpose to events) are problematic because they can cause a departure from consensual reality. Aberrant theorizing about other minds has been implicated in these higher-level cognitive processes. But we know that human vision can also detect social agents and extract rich information about their goals and intentions even before higher-level theory-of-mind processes are engaged. Might paranoia and teleological thinking have roots in earlier visual perception? Using simple displays that evoke the impression that one disc (the ‘wolf’) is pursuing another (the ‘sheep’) amongst distractors, we find that human participants with more paranoid and excessive teleological thinking tend to perceive chasing even when there is none (experiments 1 and 2) — errors that might be characterized as “social hallucinations”. However, in both between- (experiment 3) and within-participant designs (experiments 4a and 4b), we find that paranoid people have problems detecting sheep, while those high in teleology have problems detecting wolves — each confidently mis-ascribing that role (the wolf for teleology, the sheep for paranoia) to the wrong discs. Moreover, both types of errors correlate with hallucinatory percepts in the real world. These data demonstrate that different people are prone to hallucinate different kinds of social relationships — each colored by different beliefs about intentions, each with their own phenomenology and cognitive/emotional consequences, yet each operating even in visual detection itself, beyond higher-level reasoning.
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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 | 0.012 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".