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Motivated Reasoning in a Causal Explore-Exploit Task

Caddick, Zachary A. (2020) Motivated Reasoning in a Causal Explore-Exploit Task. Master's Thesis, University of Pittsburgh. (Unpublished)

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The current research investigates how prior preferences affect causal learning. Participants were tasked with repeatedly choosing policies (e.g., increase vs. decrease border security funding) in order to maximize the economic output of an imaginary country, and inferred the influence of the policies on the economy. The task was challenging and ambiguous, allowing participants to interpret the relations between the policies and the economy in multiple ways. In three studies, we found evidence of motivated reasoning despite financial incentives for accuracy. For example, participants who believed that border security funding should be increased were more likely to conclude that increasing border security funding actually caused a better economy in the task. In Study 2, we hypothesized that having neutral preferences (e.g., preferring neither increased nor decreased spending on border security) would lead to more accurate assessments overall compared to having a strong initial preference, however, we did not find evidence for such an effect. In Study 3, we tested whether providing participants with possible functional forms of the policies (e.g., the policy takes some time to work, or initially has a negative influence but eventually a positive influence) would lead to a smaller influence of motivated reasoning, but found little evidence for this effect. This research advances the field of causal learning by studying the role of prior preferences, and in doing so, integrates the fields of causal learning and motivated reasoning using a novel explore-exploit task.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Caddick, Zachary A.zac21@pitt.eduzac210000-0002-3369-7727
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairRottman, Benjamin M.rottman@pitt.edu0000-0002-4718-3970
Committee MemberNokes-Malach, Timothy J.nokes@pitt.edu0000-0001-9707-1726
Committee MemberBinning, Kevin R.kbinning@pitt.edu0000-0002-5396-4183
Date: 8 June 2020
Date Type: Publication
Defense Date: 17 March 2020
Approval Date: 8 June 2020
Submission Date: 18 March 2020
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 88
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Psychology
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: motivated reasoning, causal learning, decision-making, reasoning, politics
Date Deposited: 08 Jun 2020 15:04
Last Modified: 08 Jun 2020 15:04


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