Health beliefs and decision making

Micah B. Goldwater, Amy Perfors, Zachary Horne, Cristine H. Legare, Ellen Markman

Research output: Contribution to conferenceAbstractpeer-review

Abstract / Description of output

We make decisions that affect our short-term and long-term health several times every day. We all have beliefs about what is good for us and what is bad for us (and we at least sometimes act in accordance in those beliefs). How we form these beliefs, and how these beliefs do or do not affect our future behavior is a domain ripe for cognitive science research. Given the importance of topics such as causal reasoning, reinforcement learning, and decision-making to our field, one might expect direct applications to health to be at the forefront. However for the sake of experimental and formal control, researchers typically conduct experiments with materials disconnected from real world beliefs and design experimental tasks that may not reflect the true structure of the domain. The goal of this symposium is to show how the domain of health is perfect for a more tightly coupled exchange between investigating real-world beliefs and behavior with controlled experimental tasks ready for formal explanation. The research we will present both advances our basic cognitive science theories and are key pieces of applied health interventions. The work uses a variety of empirical methods, sampling populations from across the globe.
Original languageEnglish
Pages234-235
Number of pages2
Publication statusE-pub ahead of print - 1 Aug 2020
Event42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020 - Virtual, Online
Duration: 29 Jul 20201 Aug 2020

Conference

Conference42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020
CityVirtual, Online
Period29/07/201/08/20

Keywords / Materials (for Non-textual outputs)

  • black swans
  • causal reasoning
  • cross-cultural psychology
  • decision-making
  • health
  • machine learning
  • rituals

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