Examining a Hybrid Account of Salience-Based Amplification during Perceptual Average Judgments
Bibliographic record
Abstract
Exhaustive sampling models of perceptual averaging contend that individuals process all items within object ensembles when extracting mean feature information (e.g., mean size) rather than just a subset of items. Such models stand at odds, however, with a number of recent works demonstrating an attentional amplification effect whereby perceptual average judgments are consistently biased towards a subset of the most salient items within object ensembles. To reconcile these findings, a hybrid account has been raised that suggests amplification effects emerge through the mixing of visual representations. That is, individuals may hold an overarching representation of all items within an ensemble as well as more explicit representations of a subset of individual items within working memory. From this perspective, amplification effects come about through a post-perceptual influence of individual object representations at the time of response. In support of this view, it was recently shown that the inclusion of patterned masks following the presentation of ensemble displays greatly attenuates attentional amplification effects, presumably by disrupting explicit access to individual items. In the present experiments, we provide a systematic evaluation of this hybrid account. In contrast to what was observed previously, we show that masking ensemble displays work to increase, rather than decrease, the attentional amplification effect. Additionally, we show that this effect of masking on the amplification effect is sensitive to both display duration (with greater amplification for brief display durations) and display-type (with the effect of masking being present for grid-like arrays, but largely absent for circular arrays). Moreover, we demonstrate that the amplification effect remains present even when individuals are given unlimited access to object ensembles (when judgements are concurrent with perception). Overall, these results provide evidence against the hybrid account of the attentional amplification effect and support partial sampling models of perceptual averaging.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".