Gains and Losses Affect Learning Differentially at Low and High Attentional Load
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».