The influence of attentional interactions on perceptual processing
Bibliographic record
Abstract
Numerous studies of human attention conducted to date employed central spatially predictive arrows to measure voluntary orienting (e.g., Jonides, 1981). However, since central arrows were recently found to produce orienting even when they are spatially nonpredictive (e.g., Tipples, 2002), it became unclear whether the effects produced by the classic task reflected voluntary attention alone. Using a simple target detection cuing task, Ristic and Kingstone (2006) found that attentional effects of predictive arrows reflected an interaction between reflexive and voluntary orienting rather than voluntary orienting in isolation. However, it still remains unclear whether similar effects would emerge if participants were asked to perform a difficult target discrimination task rather then a simple detection task. To address this, we presented participants with spatially nonpredictive arrows (measuring reflexive orienting), spatially predictive arrows (measuring an interaction between reflexive and voluntary orienting), and spatially predictive shapes (measuring voluntary orienting in isolation). They were asked to discriminate a briefly presented and subsequently masked complex target as quickly and as accurately as possible. Both response time (RT) and accuracy data replicated Ristic and Kingstone (2006) results. Across both measures, predictive arrows produced orienting effects that were larger than both reflexive orienting elicited by nonpredictive arrows and voluntary orienting elicited by predictive shapes. These data solidify the past reports and further suggest that the interactions between the two attentional systems, in addition to enhancing target detection, also lead to facilitation in perception of target’s features. Meeting abstract presented at VSS 2012
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".