How Does Spatial Attention Influence the Probability and Fidelity of Colour Perception?
Bibliographic record
Abstract
Existing research has found that spatial attention alters how various stimulus properties are perceived (e.g., luminance, saturation), but few have explored whether it improves the accuracy of perception. To address this question, we performed two experiments using modified Posner cueing tasks, wherein participants made speeded detection responses to peripheral colour targets and then indicated their perceived colours on a colour wheel. In E1, cues were central and endogenous (i.e., prompted voluntary attention) and the interval between cues and targets (stimulus onset asynchrony, or SOA) was always 800 ms. In E2, cues were peripheral and exogenous (i.e., captured attention involuntarily) and the SOA varied between short (100 ms) and long (800 ms). A Bayesian mixed-model analysis was used to isolate the effects of attention on the probability and the fidelity of colour encoding. Both endogenous and short-SOA exogenous spatial cueing improved the probability of encoding the colour of targets. Improved fidelity of encoding was observed in the endogenous but not in the exogenous cueing paradigm. With exogenous cues, inhibition of return (IOR) was observed in both RT and probability at the long SOA. Overall, our findings reinforce the utility of continuous response variables in the research of attention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".