P-1.43

Approximate Bayesian Inference captures differential effects of value confidence on obligatory and voluntary choices

Joonhwa Kim, Romy Frömer, Xiamin Leng, Amitai Shenhav, Brown University, United States

Session:
Posters 1 Poster

Track:
Cognitive science

Location:
Pacific Ballroom H-O

Presentation Time:
Thu, 25 Aug, 19:30 - 21:30 Pacific Time (UTC -7)

Abstract:
People weigh their options differently depending on (a) how confident they are in their valuation of each, and (b) whether they must select one option over another. To explore the interaction between the two, we used a paradigm in which following initial obligatory choices, participants made subsequent voluntary choices in which they could choose (or choose not to choose) more additional items from the set. We tested how the value a person assigned to each option interacted with their confidence in those values to shape initial and subsequent choices. When their confidence in the values was higher, participants' initial choices were faster and more accurate and their subsequent choices were more sensitive to the options' values. We show that these effects can be captured by a modified leaky competing accumulator model that approximates Bayesian inference and accounts for the obligatory vs. voluntary nature of the choice.

Manuscript:
License:
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI:
10.32470/CCN.2022.1278-0
Publication:
2022 Conference on Cognitive Computational Neuroscience
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