Controling for Perceptual Differences in the Faces Flanker Task
Bibliographic record
Abstract
It has been suggested that negative stimuli capture attention more readily, and hold attention for longer than positive or neutral stimuli. Results consistent with this interpretation have, been found using a modified version of the Eriksen flanker task using schematic representations of emotional faces as the stimuli. Specifically, reaction times to happy faces are delayed when sad faces are presented as flankers (incongruent condition) relative to when happy faces are used as flankers (congruent condition). But this incongruent/congruent difference is not observed when the target is a sad face. It has been suggested that this effect may not be due to the valence of the stimuli and instead represent the ease of feature processing. For example, the mouth may be easier to process in a happy face than in a sad face because the contours of the lower part of the face and a happy mouth are roughly parallel. To test this hypothesis, 88 undergraduate students completed a Flanker task using schematic faces without the encompassing circle (i.e., just the eyes and mouth) as stimuli. A central target with three flankers on either side were presented in a horizontal line in the centre of the screen. As expected, a congruency effect was observed for happy faces. However, contrary to previous findings, a congruency effect was also observed for sad faces. If previous results were due to low level perceptual difference between schematic faces alone, no differences should be observed between happy and sad targets in the present study. Instead, the congruency effect was larger for happy than for sad faces. This is consistent with research suggesting negative stimuli capture attention more readily than positive stimuli, and further suggests that emotional information conveyed by the stimulus impacted responses. Meeting abstract presented at VSS 2018
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".