Pay more attention to the positives, your brain already does it anyways
Bibliographic record
Abstract
Value positive incentives (high vs. low values) have been shown to have an effect on attention when performing simple motor tasks (Anderson, Laurent, & Yantis, 2011). During a training phase, participants learned to associate stimuli with value (e.g., monetary reward). Following training, the presence of these value associated stimuli serve as distractors increasing response time compared to when there were no value associated stimuli in the environment. Little research, however, has yet been done regarding the impact of negative outcomes on attention capture and response times. The purpose of this study was then to determine whether stimuli associated with negative value would affect response time. The study was broken into three separate experiments. The first experiment was a replicate of original experiment (only positive outcomes) and was used as a manipulation check to verify that the original study could be replicated in our lab. The second experiment incorporates positive and negative outcomes, used to compare the effects of the different forms of value. The third experiment included varying values of negative outcomes (high and low penalties). All three experiments used response times as the determinant of performance. Results indicated that high-value positive outcomes caused more attentional capture, causing participants to have increased response times in the positive only experiment. However, when negative outcomes were introduced, the value associated stimuli (positive or negative) had no effect on response time. These results indicate that positive and negatively associated stimuli have differential effects on attention capture.
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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 | 0.000 | 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) | 0.018 | 0.002 |
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".