The personal in the political: The influence of candidate attributes on information processing and decision-making in voting choices
Notice bibliographique
Résumé
Objectives: Voting choices are fundamental to modern democracy. Political science largely studies such decisions in the aggregate, at the population level. Psychology and neuroscience study how individual brains make individual choices but have mainly focused on decision-making in economic paradigms. This thesis takes a neuroscience-informed approach to multi-attribute value-based decision-making in simulated voting choices. The guiding idea is that the value of multiple attributes may be integrated either configurally (holistically) or elementally (summed attribute by attribute), and that the nature of the information provided influences how attribute-values are combined to support choice. Inspired by the neuroscience of complex object recognition, we predicted that ‘policy and personal’ compared to ‘policy-only’ attributes would bias individuals to prioritize configural or elemental processing, respectively. Two experiments are reported here. The first drew on existing work showing distinct memory processes for configural or elemental information. We aimed to provide evidence that policy and personal information is remembered differently than policy-only information. The second experiment was grounded in the information processing decision-making literature. We used eye-tracking to assess whether information was acquired differently in policy-only compared to policy and personal conditions. Methods: Healthy eligible voters were recruited from the Montreal community. They made voting choices in the laboratory between pairs of candidates in simulated party leadership races under two conditions: a personal condition where candidates were characterized by both a policy and personal attribute and a policy condition where candidate profiles only included policy attributes. In Study 1 (N = 26), voting choices were followed by old/new and source memory tasks. In Study 2 (N = 38), information acquisition patterns were assessed with eye-tracking during pairwise voting choices. Participants subsequently rated the subjective value of each attribute. Attribute rating differences and rating interactions were used to predict choice. Generalized estimating equations and mixed effect regression models were used for the analysis. Results: The combination of policy and personal information was associated with different behavioural and eye-gaze patterns compared to policy attributes alone. Study 1 found better old/new discrimination accuracy for memory when personal characteristics were included, with significantly fewer false alarms compared to the policy-only condition. There was no difference in source memory accuracy across conditions. In Study 2, decisions between candidates had longer reaction times and the information acquisition pattern was more option-based in the policy-only condition. Subjective value differences between attributes predicted choice in both conditions. Conclusions: These findings provide evidence that memory and information processing in voting decisions are influenced by inclusion of personal attributes about candidates, compared to policy information alone. Across both studies, including personal information about candidates led to behavioral patterns more in keeping with configural processing. This work provides preliminary support for a novel framework for understanding how multiple attributes are combined in voting choices, influenced by the neuroscience of how complex information is represented and remembered in the brain
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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 | 0,009 |
| 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 | 0,001 | 0,001 |
| Communication savante | 0,002 | 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) | 0,004 | 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 ».