Serial dependence transfers between perceptual objects
Bibliographic record
Abstract
Abstract Judgments of the present visual world are affected by what came before. When judgments of visual properties such as orientation are biased in the direction of preceding stimuli, this is called visual serial dependence. Visual serial dependence is thought to arise from mechanisms that support perceptual continuity: because physical properties of an object usually vary smoothly in time, perception might be accurately stabilized by smoothing the perceived features in time. However, mechanisms that support perceptual continuity should be object-specific, because the orientation of one object is more related to its own past than to the past of a distinct object. Thus, we tested the perceptual continuity explanation by comparing the magnitude of serial dependence between objects and within objects. Across three experiments, we manipulated objecthood by varying the color, the location, and both the color and the location of Gabor patches. We observed a serial dependence effect in every experiment but did not observe an effect of objecthood in any experiment. We further observed serial dependence even when the orientations of two successive stimuli were nearly orthogonal. These data are inconsistent with explanations of serial dependence based on visual continuity. We hypothesize that serial dependence arises from a combination of perceptual features and internal response variables, which interact within a common task or decisional context.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".