To look or not to like: Oculomotor-control mechanisms alter stimulus-value representations.
Bibliographic record
Abstract
Cognitive-control mechanisms that determine which information becomes the focus of our attention and actions have affective consequences for associated visual stimuli. Ignoring or withholding a response from a stimulus, for example, can negatively impact its perceived value. Such stimulus devaluation is thought to be due to negative affect elicited by attention- and response-related inhibition. Using head-stabilized screen-based eye-tracking, we searched for similar effects in the oculomotor domain by combining tasks involving inhibitory control over eye-movements with affective evaluations of stimuli. Art-like patterns were first centrally presented in an oculomotor Go/No-go task. A central cue then prompted participants to either Go (look at an abrupt-onset stimulus appearing to the left or right of the pattern) or No-go (avoid making any eye-movements and instead maintain fixation on the pattern). Liking ratings obtained after each Go/No-go trial revealed that No-go-trial patterns were evaluated more negatively than Go-trial patterns, despite any fluency-related enhancement from longer foveal processing. Previously-unseen novel patterns were also disliked if rated shortly after a No-go trial than after a Go trial, suggesting lingering impacts of oculomotor inhibition on the coding of stimulus value. Similar results from experiments that interspersed anti-saccade or selective-looking trials with affective-evaluation trials suggest the mechanisms underlying ‘distractor devaluation’ and ‘No-go devaluation’ effects in other selective-attention and motor-response control domains may be similar to those influencing stimulus value in the oculomotor domain. Ongoing analysis of eye-movement data from selective-looking tasks will further reveal whether the trial-by-trial fluctuations in distractor suppression reflected in deviations in saccade trajectory can predict the magnitude of oculomotor distractor devaluation. This research underscores the potential significance of the link between inhibition and aversive response as a manifestation of the interaction between emotion and oculomotor-control systems as they work together to ensure that distracting or otherwise-problematic stimuli can be effectively avoided in the future.
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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) | 0.002 | 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".