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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
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Los sesgos cognitivos como ponderación de probabilidad bayesiana en el contexto

Bruno Kopp1

  • 1Cognitive Neuropsychology, Department of Neurology, Hannover Medical School, Hannover, Germany.

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Resumen

Los sesgos cognitivos como el conservadurismo y el descuido de la tasa base dependen del contexto. El modelo de Cognición Bayesiana Adaptativa (ABC) explica estos sesgos como estrategias adaptativas, no errores, en la toma de decisiones bajo incertidumbre.

Palabras clave:
Inferencia Bayesianasesgos cognitivosModelos computacionalestoma de decisionesapoyo a la decisiónAprendizaje del contexto

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Área de la Ciencia:

  • Psicología cognitiva
  • Economía del comportamiento
  • Modelado computacional

Sus antecedentes:

  • Los juicios humanos bajo incertidumbre a menudo muestran sesgos sistemáticos, incluido el sesgo de conservadurismo y la negligencia de la tasa base.
  • Comprender la naturaleza dependiente del contexto de estos sesgos es crucial para las teorías cognitivas y económicas.

Objetivo del estudio:

  • Investigar cómo el contexto de la tarea influye en los sesgos sistemáticos en los juicios de probabilidad humana.
  • Explorar el papel de las probabilidades previas y las probabilidades de evidencia en la toma de decisiones bajo incertidumbre.
  • Proponer y validar un modelo computacional que explique estos sesgos dependientes del contexto.

Principales métodos:

  • Cuarenta y ocho participantes completaron 12 escenarios que requieren juicios de probabilidad subjetivos.
  • Los escenarios involucraban la integración de probabilidades previas con probabilidades de evidencia en diferentes contextos (mundo pequeño vs. mundo grande).
  • La información probabilística se presentó tanto como probabilidades como frecuencias relativas.

Principales resultados:

  • El contexto de la tarea medió significativamente la ponderación de los antecedentes y las pruebas.
  • Los escenarios de mundo pequeño amplificaron el sesgo del conservadurismo al enfatizar los priores.
  • Los escenarios de mundo grande aumentaron la sensibilidad a la evidencia, lo que condujo a la negligencia de la tasa base; las frecuencias relativas no mitigaron los sesgos.

Conclusiones:

  • El modelo de Cognición Bayesiana Adaptativa (ABC) explica los sesgos cognitivos como estrategias adaptativas influenciadas por el contexto, las limitaciones de capacidad y el metaaprendizaje.
  • Los hallazgos conectan la psicología cognitiva, la economía del comportamiento y el modelado computacional para una comprensión unificada de la toma de decisiones bajo incertidumbre.
  • El estudio informa el diseño de los sistemas de apoyo a la toma de decisiones destacando la naturaleza adaptativa de los sesgos cognitivos.