Gains and Losses Affect Learning Differentially at Low and High Attentional Load
Bibliographic record
Abstract
Abstract Prospective gains and losses modulate cognitive processing, but it is unresolved whether gains and losses can facilitate flexible learning in changing environments. The prospect of gains might enhance flexible learning through prioritized processing of reward-predicting stimuli but is unclear how far this learning benefit extends when task demands increase. Similarly, experiencing losses might facilitate learning when they trigger attentional re-orienting away from loss-inducing stimuli, but losses may also impair learning by reducing the precise encoding of loss-inducing stimuli. To clarify these divergent views, we tested how varying magnitudes of gains and losses affect the flexible learning of object values in environments that varied attentional load by increasing the number of interfering object features during learning. With this task design we found that larger prospective gains improved learning efficacy and learning speed, but only when attentional load was low. In contrast, expecting losses generally impaired learning efficacy and this impairment was larger at higher attentional load. These findings functionally dissociate the contributions of prospective gains and losses on flexible learning, suggesting they operate via separate control mechanisms. One process is triggered by experiencing loss and seems to disrupt the encoding of specific loss-inducing features which leads to less efficient exploration during learning. The second process is triggered by experiencing gains which enhances learning through a more efficient prioritizing of reward-predicting stimulus features as long as the interference of distracting information is limited. These results demonstrate strengths and limitations of motivational regulation of learning efficacy in multidimensional environments having variable attentional loads. Significance statement Increasing the prospective gains is assumed to enhance flexible learning, but there is no consensus on whether imposing losses enhances or impairs flexible learning. We show that anticipating loss of already attained assets generally reduced learning changes in the relevance of visual objects and that this learning impediment is more pronounced when learning demands higher attentional control of interference from distracting object features. Moreover, we show that increasing the prospective gains indeed facilitates learning, but only when the learning problem has intermediate or low attentional demands. These findings document that the beneficial effects of gains hit a limit when task demands increase, and that prospective losses reduce cognitive flexibility already at low task demands which is exacerbated when task demands increase. These findings provide novel insight into the strengths and limitations of gains and of losses to support flexible learning in multidimensional environments imposing variable attentional loads.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".