What’s that smile worth? Social reward influences spatial orienting
Bibliographic record
Abstract
A wealth of recent research indicates that rewarded singletons capture attention. However, it is unclear whether reward may also lead to the development of spatial associations between neutral cues and corresponding target locations, resulting in spatial orienting. Furthermore, reward-mediated capture has mostly been demonstrated using monetary reward, opening up the possibility that this finding may not generalize to other types of rewards. To test this, we used a social reward and examined performance in Baseline, Learning, and Test conditions. Participants performed a four location cuing task, in which one of two bicolored circles (orange-yellow; blue-green) shown at fixation indicated a target on the left, right, top, or bottom with equal probability. In the Baseline condition, participants completed a target detection task. In the Learning condition, they performed a target identification task. Implicit social reward (i.e., “points” towards the researcher’s project) was administered for correct identification of a target that was spatially congruent with one half of one bicolored cue (e.g., orange left, target left; +5 points). Incorrect identification of a target that was congruent with the other half of the bicolored cue accrued negative points (e.g., yellow left; target left; -5 points). No reward was given for the other cue. Rewarded cue type was counterbalanced between participants. In the Test condition conducted one day later, participants performed the same task as Baseline. The data indicated that social reward influenced learning of spatial relations. In contrast to Baseline, where no facilitation of RTs was found, in the Test condition, participants were significantly faster at detecting targets congruent with the rewarded color. This shows that in addition to modulating attention to singletons, reward also modulates the learning of spatial associations, and furthermore suggests an important role of social reward for the development of human social attention. Meeting abstract presented at VSS 2015
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".