Reduced temporal fusion in near-hand space
Bibliographic record
Abstract
Humans make several eye movements every second, and objects themselves move in the environment. This means that brain receives interrupted input, from which it is necessary to draw inferences about whether stimulation reflects a single object continuing through time, or instead reflects discrete object identities. Here we investigated whether such processes of object integration versus individuation could be influenced by the temporal resolution of encoding. To do this, we used object substitution masking (OSM), which refers to a situation in which the perception of a briefly-presented target (e.g., Landolt C) surrounded by four dots is obscured when the four-dots have a delayed offset relative to the target. OSM is thought to reflect a failure to segregate the target from mask, which means that increasing the temporal precision of the visual system should reduce OSM. In the present study, temporal precision was manipulated through the proximity of observers’ hands to visual stimuli, as near-hand space has been recently been found to enhance activity in the magnocellular visual pathway (which has high temporal resolution). Observers’ task was to identify the location of the gap in a target broken circle (left or right of the object) surrounded by four dots, which either offset simultaneously or temporally trailed for 200ms. The observers made responses via a mouse attached to either side of the screen (visual stimuli in near-hand space) and in a separate block via keys on the keyboard (visual stimuli not in near-hand space). Hand placement did affect OSM: there was significantly less masking (i.e., increased target identification accuracy) for stimuli in near-hand space. This finding demonstrates that OSM can be conceptualized as a failure of object individuation, and this process can be facilitated by increasing the temporal resolution of vision via the proximity of visual stimuli to the hands. Meeting abstract presented at VSS 2013
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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 | 0.001 |
| 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) | 0.003 | 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".