Near-space advantage in a simulated 3D environment: an inhibition of return (IOR) study
Bibliographic record
Abstract
Previous research suggested that the dorsal cortical system (vision for action) involves stimulus processing in peripersonal space while the ventral cortical system (vision for perception) processes extrapersonal space. Additionally, it has been suggested that the dorsal and ventral systems control target detection and identification, respectively. Little research has examined the possible near-advantage in target detection as opposed to discrimination tasks. Our previous studies (Li et al, Neuropsychologia, 2011; Song et al, JoV, 2021) demonstrated a near-advantage in target detection but not in discrimination when a single target was presented in depth. In the current study, we modified the spatial cueing paradigm in a 3D environment (with monocular depth cues) to examine how observers orient their spatial attention from the cue to target across depths. Cue and target stimuli appeared in the same or different depths but with matching eccentricity and retinal size. For the target detection task, robust IOR magnitudes were revealed when orienting within both of the same-depth plane conditions (near-near, far-far). Conversely, IOR was attenuated for far-to-near attention orienting, however, IOR increased for near-to-far orienting. Such a pattern of results was not seen for discrimination task. In these experiments, the viewpoint of the observers remained stationary. The distinction between near and far implies viewer-centered coding. To test this, for the target detection task, we created two experimental conditions in which the cue and target were presented at different depths relative to the environment, but not relative to the viewers. To do this, we introduced self-motion along the z-axis in the virtual environment during the time interval between the cue and target appearance. The results showed a disappearance of the IOR asymmetry across the two depth-orienting directions with a stable viewpoint. Overall, these results support the notions of a near-advantage in target detection and the dorsal prioritization of near-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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".