Attentional control strategies lead to different task performance across cognitive domains
Bibliographic record
Abstract
Performance for visual search improves when subjects use passive, intuitive attentional strategies versus actively directed ones (Smilek & Enns, 2006). It has also been suggested that visual search tasks benefit from bottom-up mechanisms (Proulx, 2005). Smilek & Enns acknowledge that the passive advantage may not transfer to other cognitive domains and Pinto et al. (2013) suggested that attentional mechanisms might be independent in both top-down and bottom-up processing. In order to test this possibility we ran a battery of executive cognitive tasks that we predicted would be impeded by the passive attentional strategies that were shown to be beneficial for visual search. Two randomly selected groups of participants completed a pre-test comprised of a backward digit span task, followed by a subset of Raven’s Progressive Matrices. Before repeating these batteries in a post-test, group 1 was instructed, in a manner similar to Smilek & Enns, to “actively direct” their attention (i.e., maintain active focus on the stimuli), and group 2 was instructed to be “as receptive as possible”, using a passive, broad focus approach (i.e., using intuition and “gut-feeling”), allowing targets and answers to “pop” into their minds. Group 1 (active) scored significantly higher on post-test items while group 2 (passive) scored significantly lower. This showed that executive cognitive tasks benefit from actively directed attentional strategies, and while passive attentional strategies may be conducive to tasks reliant on visual tasks requiring bottom-up processing, they were found to be a hindrance for those requiring top-down processing. These findings suggest that visual search tasks require distinct cognitive processes compared to complex intelligence batteries, and also provide support for claims that these distinct processes may function with different attentional mechanisms. 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".