Bibliographic record
Abstract
Feature integration effects are prevalent in many visual cognition tasks that require stimulus identification. In many cases, these feature integration effects are "nuisance" effects in that they obscure the effects under study, possibly leading to invalid inferences. Eliminating these effects has proven difficult. Here, we manipulated a paradigm in which feature integration effects have been shown to be surprisingly absent in order to learn about the limits of such effects limits. In the prime-search paradigm, individuals sometimes make a response to a centrally presented prime stimulus (a colored circle). This is followed by a visual search where the target stimulus may or may not appear in a placeholder matching the prime stimulus. Typically, response are faster when the target's placeholder matches the prime than when it does not, but only when individuals respond to the prime. Across four experiments we gradually increased the overlap in stimulus location and response demands between the two tasks to determine what is necessary to cause feature integration effects to appear. We found that stimulus location overlap alone was insufficient to generate feature integration effects. Similarly insufficient was having location overlap and response overlap (i.e., both responses with the spacebar). With stimulus location overlap and a lateralized response set, however, feature integration effects appeared. This remained the case when we removed the distractor item from the second task. Interestingly, the feature integration effects were consistent with those predicted when a task switch occurs, rather than those found more commonly. This study indicates that the lack of integration effects in the prime-search task result from participants representing the two parts as separate tasks along with the lack of stimulus location and response set overlap. Furthermore, the data has implications for understanding the limits of feature integration and the interplay between task switching and feature integration. Meeting abstract presented at VSS 2018
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".