The snooze of lose: Rapid reaching reveals that losses are processed more slowly than gains.
Bibliographic record
Abstract
Decision making revolves around weighing potential gains and losses. Research in economic decision making has emphasized that humans exercise disproportionate caution when making explicit choices involving loss. By comparison, research in perceptual decision making has revealed a processing advantage for targets associated with potential gain, though the effects of loss have been explored less systematically. Here, we use a rapid reaching task to measure the relative sensitivity (Experiment 1) and the time course (Experiments 2 and 3) of rapid actions with regard to the reward valence and probability of targets. We show that targets linked to a high probability of gain influence actions about 100 ms earlier than targets associated with equivalent probability and value of loss. These findings are well accounted for by a model of stimulus response in which reward modulates the late, postpeak phase of the activity. We interpret our results within a neural framework of biased competition that is resolved in spatial maps of behavioral relevance. As implied by our model, all visual stimuli initially receive positive activation. Gain stimuli can build off of this initial activation when selected as a target, whereas loss stimuli have to overcome this initial activation in order to be avoided, accounting for the observed delay between valences. Our results bring clarity to the perceptual effects of losses versus gains and highlight the importance of considering the timeline of different biasing factors that influence decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".