City Research Online

MEM-EX: An exemplar memory model of decisions from experience

Hotaling, J., Donkin, C., Jarvstad, A. ORCID: 0000-0002-3175-8733 and Newell, B. R. (2020). MEM-EX: An exemplar memory model of decisions from experience (10.31234/osf.io/fjhr9). .

Abstract

Many real-world decisions must be made on basis of experienced outcomes. However, there is little consensus about the mechanisms by which people make these decisions from experience (DfE). Across five experiments, we identified several factors influencing DfE. We also introduce a novel computational modeling framework, the memory for exemplars model (MEM-EX), which posits that decision makers rely on memory for previously experienced outcomes to make choices. Using MEM-EX, we demonstrate how several cognitive mechanisms provide intuitive and parsimonious explanations for the effects of value-ignorance, salience, outcome order, and sample size. We also conduct a cross-validation analysis of several models within the MEM-EX framework, as well as a baseline model built on principles of reinforcement-learning. We find that MEM-EX consistently outperforms this baseline, demonstrating its value as a tool for making quantitative predictions without overfitting. We discuss the implications of these findings on our understanding of the interplay between attention, memory, and experience-based choice.

Publication Type: Monograph (Working Paper)
Additional Information: Copyright, the authors, 2020.
Subjects: B Philosophy. Psychology. Religion > BF Psychology
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Departments: School of Arts & Social Sciences > Psychology
Date available in CRO: 05 Mar 2021 15:40
Date deposited: 5 March 2021
URI: https://openaccess.city.ac.uk/id/eprint/25754
[img]
Preview
Text - Pre-print
Available under License Creative Commons: Attribution International Public License 4.0.

Download (1MB) | Preview

Export

Downloads

Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login