Distribution of Attention in Three-Dimensional Space
Bibliographic record
Abstract
The distribution of spatial attention has mostly been studied for visual events presented within a two-dimensional space. In this study, we examined the distribution of spatial attention in a three-dimensional space (i.e., across the z-axis). Much previous research suggests that attention is universally biased toward stimuli appearing in near space compared to far space. However, the results of some studies suggest this ‘near advantage’ is task-specific, with some tasks instead producing an attention bias toward stimuli in far space. The current study investigated whether two tasks that differ in attentional priorities (i.e., target localization vs target discrimination) differentially bias attention across near and far depth. Across three experiments, we compared target localization and target discrimination tasks when a single target appeared as the stimulus (Experiment 1) and then, for a cue-target task, compared target localization (Experiment 2A) and target discrimination tasks (Experiment 2B). Our results support the proposal that the near advantage is task-specific. For target localization, reaction times (RTs) were shorter for near-targets than for far-targets, however, for target discrimination, RTs were shorter for far-targets than for near-targets. This result was revealed in both uncued and cue-target paradigms. The cue-target paradigm also showed that relative to same-depth conditions, the cueing effect pointed to greater facilitation when orienting attention from far-to-near space for target localization but from near-to-far space for target discrimination. These findings argue against a universal near advantage. Overall, the results were consistent with the notion that different task demands can differentially bias the distribution of attention across near and far depth, a proposal that has implications for the potential involvement of the dorsal and ventral visual processing streams.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".