Bibliographic record
Abstract
Attentional selection is influenced by the reliability of the cue in signaling events in the environment (i.e., alertness), its utility in indicating the spatial location of events (i.e., spatial predictability), and its utility in indicating the timing of events in the environment (i.e., temporal predictability). We investigated the role of each of these components in social orienting. Participants were presented with a central eye gaze cue and were asked to detect peripheral targets. Alertness was manipulated by altering the cue’s reliability in signaling the appearance of a target (low reliability; high reliability). Spatial utility was manipulated by altering the cue’s predictiveness of the target’s location (nonpredictive; predictive). Temporal utility was manipulated by altering the cue’s predictiveness of when within a trial the target will appear (nonpredictive; predictive). This design allowed us to measure the isolated and combined contributions of alertness, spatial predictability, and temporal predictability on the magnitude of social orienting. We found that attentional effects were enhanced under conditions of high alertness, regardless of the cue’s spatial or temporal utility. Cue’s spatial predictiveness also led to the enhancement of the attentional effect, however the level of alertness modulated this result. Finally, the manipulation of the cue’s temporal utility did not affect the magnitude of social orienting and furthermore did not interact with the cue’s spatial utility or its alertness. Together, these data suggest additive effects of spatial and temporal orienting, and point to the critical role of alertness in social attention. Meeting abstract presented at VSS 2012
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".