On the Interaction Between Stimulus Features and Context in the Perception of Causality
Bibliographic record
Abstract
Much research has supported the hypothesis that the perceptual system generates impressions of causality based on simple cues available in the environment. We review evidence that this extraction of causal relations from perceptual cues is likely an automatic property of the visual system akin to other basic perceptual processes, such as perceptual grouping and illusory contour completion. We posit that it is this automatic characteristic of perceptual causality that underlies the proliferation of context effects associated with perceived causal events. Here, the presence of a perceived causal relationship may impact the perception of other features of the causal stimulus and other stimuli surrounding the causal event. We discuss current research, and present future research directions that promise to uncover some of the mechanisms underlying how causality is attributed from, and changes how we perceive and respond to, simple low-level stimuli in the environment. Such research will enrich our understanding of how the perception of causally-relevant stimulus features interacts with their context to enable us to effectively perceive, understand, and act upon our environment.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".