Literature DB >> 32989719

Response time models separate single- and dual-process accounts of memory-based decisions.

Peter M Kraemer1, Laura Fontanesi2, Mikhail S Spektor3,4, Sebastian Gluth2.   

Abstract

Human decisions often deviate from economic rationality and are influenced by cognitive biases. One such bias is the memory bias according to which people prefer choice options they have a better memory of-even when the options' utilities are comparatively low. Although this phenomenon is well supported empirically, its cognitive foundation remains elusive. Here we test two conceivable computational accounts of the memory bias against each other. On the one hand, a single-process account explains the memory bias by assuming a single biased evidence-accumulation process in favor of remembered options. On the contrary, a dual-process account posits that some decisions are driven by a purely memory-driven process and others by a utility-maximizing one. We show that both accounts are indistinguishable based on choices alone as they make similar predictions with respect to the memory bias. However, they make qualitatively different predictions about response times. We tested the qualitative and quantitative predictions of both accounts on behavioral data from a memory-based decision-making task. Our results show that a single-process account provides a better account of the data, both qualitatively and quantitatively. In addition to deepening our understanding of memory-based decision-making, our study provides an example of how to rigorously compare single- versus dual-process models using empirical data and hierarchical Bayesian parameter estimation methods.

Entities:  

Keywords:  Decision making; Judgment

Mesh:

Year:  2021        PMID: 32989719      PMCID: PMC7870645          DOI: 10.3758/s13423-020-01794-9

Source DB:  PubMed          Journal:  Psychon Bull Rev        ISSN: 1069-9384


  24 in total

1.  Visual fixations and the computation and comparison of value in simple choice.

Authors:  Ian Krajbich; Carrie Armel; Antonio Rangel
Journal:  Nat Neurosci       Date:  2010-09-12       Impact factor: 24.884

2.  The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks.

Authors:  Rafal Bogacz; Eric Brown; Jeff Moehlis; Philip Holmes; Jonathan D Cohen
Journal:  Psychol Rev       Date:  2006-10       Impact factor: 8.934

Review 3.  Cognitive and Neural Bases of Multi-Attribute, Multi-Alternative, Value-based Decisions.

Authors:  Jerome R Busemeyer; Sebastian Gluth; Jörg Rieskamp; Brandon M Turner
Journal:  Trends Cogn Sci       Date:  2019-01-07       Impact factor: 20.229

4.  Leave-One-Trial-Out, LOTO, a general approach to link single-trial parameters of cognitive models to neural data.

Authors:  Sebastian Gluth; Nachshon Meiran
Journal:  Elife       Date:  2019-02-08       Impact factor: 8.140

Review 5.  The Mythical Number Two.

Authors:  David E Melnikoff; John A Bargh
Journal:  Trends Cogn Sci       Date:  2018-03-20       Impact factor: 20.229

6.  A dynamic dual process model of risky decision making.

Authors:  Adele Diederich; Jennifer S Trueblood
Journal:  Psychol Rev       Date:  2018-03       Impact factor: 8.934

Review 7.  The relative merit of empirical priors in non-identifiable and sloppy models: Applications to models of learning and decision-making : Empirical priors.

Authors:  Mikhail S Spektor; David Kellen
Journal:  Psychon Bull Rev       Date:  2018-12

Review 8.  Dual-processing accounts of reasoning, judgment, and social cognition.

Authors:  Jonathan St B T Evans
Journal:  Annu Rev Psychol       Date:  2008       Impact factor: 24.137

9.  Joint modeling of reaction times and choice improves parameter identifiability in reinforcement learning models.

Authors:  Ian C Ballard; Samuel M McClure
Journal:  J Neurosci Methods       Date:  2019-01-18       Impact factor: 2.390

Review 10.  Decision Making and Sequential Sampling from Memory.

Authors:  Michael N Shadlen; Daphna Shohamy
Journal:  Neuron       Date:  2016-06-01       Impact factor: 17.173

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