Feature-based attention separately influences visual working memory resolution and encoding probability
Bibliographic record
Abstract
We use attention to select relevant portions of our environment for detailed processing-a process often directed by feature-based goals. Feature-based attention guides visual information processing by strengthening early representations (i.e., within perceptual cortex). Here we examined how feature-based goals affect the way visual information is represented at later stages of processing, namely within visual working memory (VWM). To address this question we used a continuous partial-report VWM task (i.e., a colour-wheel task) and measured the effects of attention on guess rate (the probability that an item is encoded into VWM) and standard deviation (the resolution with which an item is represented). On each trial of Experiment 1, participants remembered the colours of two squares and two circles over a delay, and then reported the colour of one probed stimulus. To manipulate feature-based attention we instructed participants that square stimuli were more likely to be probed (counterbalanced): Across four blocks, squares were probed on 60%, 70%, 80%, or 90% of trials. We found that increasing the value of a feature-based goal increases the probability that a goal-matching item will be encoded into VWM without altering its resolution. This pattern reverses, however, for non-matching stimuli: Attention affects resolution but not guess rate. In Experiment 2, we increased set size from four to six items (three circles and three squares), and observed the same effects of attention. The double dissociation observed in both experiments suggests that feature-based attention can affect both VWM encoding probability and resolution, and, for a given stimulus, these effects can emerge independently. Broadly, our findings add to recent studies investigating how the value of an attentional goal impacts VWM. Interestingly, as attention type (attend to feature vs. space) and stimulus type (report colour vs. location) change, so do the effects on VWM, suggesting a need for more research. Meeting abstract presented at VSS 2015.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".