Tuning perception: the content of visual working memory biases the quality of visual awareness
Bibliographic record
Abstract
The likelihood that an individual will become subjectively aware of a visual stimulus can be affected by experimental manipulations of the stimulus itself (i.e., visual masking) or manipulation of cognitive factors. For example, previous studies have demonstrated that items held in visual working memory (VWM) that match target features allow targets to reach visual awareness faster. These studies on visual awareness have often used coarse measures of awareness, such as present/absent or forced-choice judgments. This has resulted in an all-or-none conceptualization of visual awareness, wherein an item is either seen or remains "invisible". However, recent evidence from object-substitution masking paradigms (OSM) suggest that visual awareness may instead be a graded process, as masking an item decreases the quality of its perceptual representation in addition to its threshold of awareness. In the present study we examined whether items held in VWM could influence the quality with which a partially masked target reached awareness. Participants were asked to hold an oriented Landolt C in VWM across each OSM trial (set size 2 or 4). On half of the trials the orientation of the Landolt C held in VWM matched the masked target, and on the other half it did not match the target. Data were analyzed using the three-component mixture model to determine the proportion of target responses, guesses, non-target responses, and the error (standard deviation) of responses within each condition. It was found that targets matching the contents of VWM were subsequently perceived with greater precision (i.e., less error). The item held in VWM did not affect the likelihood of making a target response, guess, or non-target error. These results suggest that items held in VWM are able to tune the quality of visual representations, which is a finding that is at odds with an all-or-none conceptualization of consciousness. Meeting abstract presented at VSS 2016
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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 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".