Different Algorithmic Models Underlie Virtue and Vice Attributions

Sergio Barbosa; William Jiménez-Leal

doi:10.1525/collabra.38032

Different Algorithmic Models Underlie Virtue and Vice Attributions

Sergio Barbosa, William Jiménez-Leal

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

Resumen

Una de las cuestiones más acuciantes de la psicología social es cómo las personas actualizan las atribuciones de carácter sobre otras personas teniendo en cuenta información novedosa. Una posible forma de abordar esta cuestión es modelar algorítmicamente la actualización de las atribuciones de rasgos y confrontarla con la forma en que las personas actualizan realmente las atribuciones de carácter. Aquí presentamos, parametrizamos y probamos empíricamente varios modelos bayesianos y de promediación del juicio moral basado en el carácter sobre múltiples piezas de información moralmente relevante o distractora. En conjunto, los resultados de dos experimentos sugieren que las atribuciones de virtud y vicio siguen algoritmos diferentes. Dependiendo de la estructura de la información recibida, las atribuciones de virtud y vicio pueden seguir algoritmos bayesianos ponderados o modelos basados en promedios diferentes. Discutimos estos resultados a la luz de los hallazgos clásicos de la psicología moral y de las ciencias cognitivas en general.

Idioma original	Inglés estadounidense
Páginas (desde-hasta)	556-574
Número de páginas	19
Publicación	Collabra: Psychology
Volumen	8
N.º	1
DOI	https://doi.org/10.1525/collabra.38032
Estado	Publicada - sep. 27 2022

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Psicología General

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abstract = "One of the most pressing questions in social psychology is how people update character attributions about other people considering novel information. A possible way to tackle this question is to algorithmically model trait attribution updating and confront it to how people actually update character attributions. Here, we present, parameterize, and empirically test several Bayesian and averaging models of character-based moral judgment over multiple pieces of morally relevant or distractor information. Taken as a whole, results from two experiments suggest that virtue and vice attributions follow different algorithms. Depending on the structure of received information virtue and vice attributions can follow differently weighted Bayesian algorithms or average-based models. We discuss these results in light of both classic findings in moral psychology and cognitive sciences in general.",

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AB - One of the most pressing questions in social psychology is how people update character attributions about other people considering novel information. A possible way to tackle this question is to algorithmically model trait attribution updating and confront it to how people actually update character attributions. Here, we present, parameterize, and empirically test several Bayesian and averaging models of character-based moral judgment over multiple pieces of morally relevant or distractor information. Taken as a whole, results from two experiments suggest that virtue and vice attributions follow different algorithms. Depending on the structure of received information virtue and vice attributions can follow differently weighted Bayesian algorithms or average-based models. We discuss these results in light of both classic findings in moral psychology and cognitive sciences in general.

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Different Algorithmic Models Underlie Virtue and Vice Attributions

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