Apparent Motion Can Impair and Enhance Target Visibility: The Role of Shape in Predicting and Postdicting Object Continuity
Bibliographic record
Abstract
Some previous studies have reported that the visibility of a target in the path of an apparent motion sequence is impaired; other studies have reported that it is facilitated. Here we test whether the relation of shape similarity between the inducing and target stimuli has an influence on visibility. Reasoning from a theoretical framework in which there are both predictive and postdictive influences on shape perception, we report experiments involving three-frame apparent motion sequences. In these experiments, we systematically varied the congruence between target shapes and contextual shapes (preceding and following). Experiment 1 established the baseline visibility of the target, when it was presented in isolation and when it was preceded or followed by a single contextual shape. This set the stage for Experiment 2, where the shape congruence between the target and both contextual shapes was varied orthogonally. The results showed a remarkable degree of synergy between predictive and postdictive influences, allowing a backward-masked shape that was almost invisible when presented in isolation to be discriminated with a d' of 2 when either of the contextual shapes are congruent. In Experiment 3 participants performed a shape-feature detection task with the same stimuli, with the results indicating that the predictive and postdictive effects were now absent. This finding confirms that shape congruence effects on visibility are specific to shape perception and are not due to either general alerting effects for objects in the path of a motion signal nor to low-level perceptual filling-in.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".