The Missing Piece of the Puzzle: Predictive templates guide visual search by prompting the generation of target features in visual working memory.
Why this work is in the frame
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Bibliographic record
Abstract
During visual search, a representation of the search target (an attentional template) held in visual working memory (VWM) supports the automatic guidance of attention to feature-matching perceptual inputs. Traditionally, attentional templates represent the exact features of an object; however, sometimes search relies on a template that is not an exact match to the search target but instead predicts target features. For example, when doing a puzzle, the piece you are looking at is not an exact template for the target piece you are looking for. What do we represent in VWM when relying on a predictive template? We evaluated whether participants represent the predictive template in VWM to compare with perceptual inputs or if they generate the target features in VWM to guide search directly. In two experiments, participants first viewed a predictive template (a solid black puzzle piece (E1) with an arrow indicating the position of the connecting piece they were to search for). Most trials then ended in a search for the target (connecting) piece. Critically, 40% of trials instead ended in an unrelated search task that contained a puzzle-piece distractor meant to be ignored. This distractor could either match the predictive template, the target (connecting) piece, or be novel to the trial. This distractor induced memory-driven capture (a slowing of response times when an item matches the contents of VWM) only when it matched the target, suggesting that a representation of the target features was active in VWM in preparation for search. In E2, we replicated this result with puzzle-piece stimuli that had complex designs overlaid on them and thus required pattern extension in order to generate target features. We suggest that predictive templates prompt the generation of target features in VWM to directly guide search, both when the target features are simple and complex.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it