Investigating how the modularity of visuospatial attention shapes conscious perception using type I and type II signal detection theory.
Bibliographic record
Abstract
Attention abilities rest on the coordinated interplay of multiple components. One consequence to this multifaceted account is that selection processes likely intersect with perception at various junctures. Drawing from this overarching view, the current research examines how different forms of visuospatial attention influence various aspects of conscious perception, including signal detection, signal discrimination, visual awareness, and metacognition. In this effort, we combined a double spatial cueing approach, where stimulus- and goal-driven orienting were concurrently engaged via separate cues, with Type I and Type II signal detection theoretic frameworks through five experiments. Consistent with the modular view of visuospatial attention, our comprehensive assessment reveals that stimulus- and goal-driven orienting operate independently of each other for increasing perceptual sensitivity and reducing the decision bound. Conversely, however, our study shows that both forms of orienting hardly influence visual awareness and metacognition once perceptual sensitivity is accounted for. Our results therefore undermine the idea that attention directly interfaces with subjective aspects of perception. Instead, our findings submit a general framework whereby these attention modules indirectly impact visual awareness and metacognition by increasing perceptual evidence and decreasing the decision bound. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".