Rapid Visuomotor Integration of flanking valenced objects
Bibliographic record
Abstract
Significant neurobehavioral evidence suggests a discrete segregation between the pathways associated with visual perception (i.e., ventral projections) and those ascribed visuo-motor functions (i.e., dorsal projections; in humans, see Milner & Goodale 2008; in non-human primates see Ungerleider & Mishkin 1982). In general the dorsal stream appears to be specialized for processing veridical and egocentrically coded cues in a fashion that is independent of conscious awareness (e.g., Binsted et al. 2007). Conversely, the ventral stream considers the relational characteristics of visual objects and scenes to arrive at a richly detailed percept. However, demonstrations of dorsal insensitivity to perceptually driven object features have failed to address valence as an action moderator despite its apparent evolutionary relevance. Moreover, valenced cues have been observed to modify motor behavior in non-human primates (fear conditioning; Mineka et al. 1984). Thus, it follows that the human visuo-motor system should rapidly integrate abstract scene cues (e.g., valence) to reach a goal while avoiding potential dangers (e.g., predation). To examine this we asked participants to point to visual targets that were randomly flanked by valenced images chosen from the International Affective Picture System (IAPS: e.g., bear cub, gun). All pointing movements had 50 cm amplitude; the target was withdrawn upon movement initiation while the valenced flanker remained. Participant endpoint position was driven towards negatively valenced objects and driven away from positively valenced objects. Thus, it appears the visuomotor system does not restrict its visual set. Rather, it appears to rapidly integrate perceptual interpretations of abstract and contextual cues for movement adaptation.
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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 | 0.000 |
| 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".