Target Discrimination at Different Viewing Distances: The Role of Expectancy for Target Configurations
Bibliographic record
Abstract
The distribution of attention across depth has recently attracted much research. Research from detection or localization tasks has suggested that attention is distributed more strongly toward near space than far space in the 3D environment. Such 'Near Advantage' has been demonstrated through faster reaction times in localizing near than far targets. However, when the task requires a discrimination response, the results for the depth effect were mixed in the literature. The current experiments sought to examine one potential moderating factor for the effect of depth: expectancy for the target configuration over trials. Recent research has led us to believe that whether participants can predict the task-prioritized target feature on a trial-to-trial basis may result in attention being allocated differently across depth. To investigate this question, using a simulated 3D environment, we implemented an orientation discrimination task where target stimuli were presented pseudorandomly at either the near or far depth plane and either left or right hemifield. In Experiment 1, the magnitude of the orientation differences were randomly selected from three values on every trial. In Experiment 2, the same three magnitudes of orientation differences were implemented between participants, with only one magnitude for a given participant; thus, participants could anticipate the magnitude of orientation difference in the upcoming trials. The results showed that when participants were unable to predict the configuration of the upcoming target stimuli, there was a null effect of depth (Experiment 1). However, when participants could form an expectation pertaining to the upcoming orientation differences, a far advantage was revealed (Experiment 2). These findings reveal that target expectancy could differentially impact attention distribution in a 3D discrimination task. The findings in this study could provide insights into learning in attentional allocation and the possible involvement of dorsal/ventral visual pathways in processing stimuli across 3D space.
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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 | 0.004 | 0.045 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".