Accounting for Taste: A Multi-Attribute Neurocomputational Model Explains the Neural Dynamics of Choices for Self and Others
Notice bibliographique
Résumé
How do we make choices for others with different preferences from our own? Although neuroimaging studies implicate similar circuits in representing preferences for oneself and others, some models propose that additional corrective mechanisms come online when choices for others diverge from one9s own preferences. Here we used event-related potentials (ERPs) in humans, in combination with computational modeling, to examine how social information is integrated in the time leading up to choices for oneself and others. Hungry male and female participants with unrestricted diets selected foods for themselves, a similar unrestricted eater, and a dissimilar, self-identified healthy eater. Across choices for both oneself and others, ERP value signals emerged within the same time window but differentially reflected taste and health attributes based on the recipient9s preferences. Choices for the dissimilar recipient were associated with earlier activity localized to brain regions implicated in social cognition, including temporoparietal junction. Finally, response-locked analysis revealed a late ERP component specific to choices for the similar recipient, localized to the parietal lobe, that appeared to reflect differences in the response threshold based on uncertainty. A multi-attribute computational model supported the link between specific ERP components and distinct model parameters, and was not significantly improved by adding time-dependent dual processes. Model simulations suggested that longer response times previously associated with effortful correction may alternatively arise from higher choice uncertainty. Together, these results provide a parsimonious neurocomputational mechanism for social decision-making, additionally explaining divergent patterns of choice and response time data in decisions for oneself and others. SIGNIFICANCE STATEMENT How do we choose for others, particularly when they have different preferences? Whereas some studies suggest that similar neural circuits underlie decision-making for oneself and others, others argue for additional, slower perspective-taking mechanisms. Combining event-related potentials with computational modeling, we found that integration of others9 preferences occurs over the same timescale as for oneself while differentially tracking recipient-relevant attributes. Although choosing for others took longer and produced differences in late-emerging neural responses, computational modeling attributed these patterns to greater response caution rather than egocentric bias correction. Computational simulations also correctly predicted when and why choosing differently for others takes longer, suggesting that a model incorporating value integration and evidence accumulation can parsimoniously account for complex patterns in social decision-making.
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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 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,004 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 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 ».