The influence of threat on visuospatial perception, affordances, and protective behaviour: A systematic review and meta-analysis
Bibliographic record
Abstract
Perception has been conceptualised as an active and adaptive process, based upon incoming sensory inputs, which are modified by top-down factors such as cognitions. Visuospatial perception is thought to be scaled based on threat, with highly threatening objects or contexts visually inflated to promote escape or avoidance behaviours. This meta-analytical systematic review quantified the effect and evidence quality of threat-evoked visuospatial scaling, as well as how visuospatial scaling relates to affordances (perceived action capabilities) and behavioural avoidance/escape outcomes. Databases and grey literature were systematically searched inclusive to 10/04/24. Studies were assessed with a customised Risk of Bias form and meta-analysis was performed using a random-effects model. 12,354 records were identified. Of these, 49 experiments (n = 3027) were included in the review. There was consistent evidence that threat the of height influenced contextual perception (g = 0.66, 95% CI: 0.45, 0.88) and affordances (g = -0.43, 95% CI: -0.84, -0.03). Threatening objects were viewed as larger (g = 0.76, 95% CI: 0.26, 1.26) and as closer (g = 0.30, 95% CI: 0.17, 0.42). Bodily threat (pain) yielded conflicting effects on visuospatial perception/affordances. We conclude that threat may influence visuospatial perception and affordances. However, since behavioural measures were poorly reported, their relationship with visuospatial perception/affordances remains elusive.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.019 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".