» Articles » PMID: 26518307

Of Monkeys and Men: Impatience in Perceptual Decision-making

Overview
Specialty Psychology
Date 2015 Nov 1
PMID 26518307
Citations 9
Authors
Affiliations
Soon will be listed here.
Abstract

For decades sequential sampling models have successfully accounted for human and monkey decision-making, relying on the standard assumption that decision makers maintain a pre-set decision standard throughout the decision process. Based on the theoretical argument of reward rate maximization, some authors have recently suggested that decision makers become increasingly impatient as time passes and therefore lower their decision standard. Indeed, a number of studies show that computational models with an impatience component provide a good fit to human and monkey decision behavior. However, many of these studies lack quantitative model comparisons and systematic manipulations of rewards. Moreover, the often-cited evidence from single-cell recordings is not unequivocal and complimentary data from human subjects is largely missing. We conclude that, despite some enthusiastic calls for the abandonment of the standard model, the idea of an impatience component has yet to be fully established; we suggest a number of recently developed tools that will help bring the debate to a conclusive settlement.

Citing Articles

Different Forms of Variability Could Explain a Difference Between Human and Rat Decision Making.

Nguyen Q, Reinagel P Front Neurosci. 2022; 16:794681.

PMID: 35273473 PMC: 8902138. DOI: 10.3389/fnins.2022.794681.


Seven steps toward more transparency in statistical practice.

Wagenmakers E, Sarafoglou A, Aarts S, Albers C, Algermissen J, Bahnik S Nat Hum Behav. 2021; 5(11):1473-1480.

PMID: 34764461 DOI: 10.1038/s41562-021-01211-8.


Time perception and timed decision task performance during passive heat stress.

Kingma B, Roijendijk L, Van Maanen L, van Rijn H, Van Beurden M Temperature (Austin). 2021; 8(1):53-63.

PMID: 33553505 PMC: 7849768. DOI: 10.1080/23328940.2020.1776925.


A new model of decision processing in instrumental learning tasks.

Miletic S, Boag R, Trutti A, Stevenson N, Forstmann B, Heathcote A Elife. 2021; 10.

PMID: 33501916 PMC: 7880686. DOI: 10.7554/eLife.63055.


ChaRTr: An R toolbox for modeling choices and response times in decision-making tasks.

Chandrasekaran C, Hawkins G J Neurosci Methods. 2019; 328:108432.

PMID: 31586868 PMC: 6980795. DOI: 10.1016/j.jneumeth.2019.108432.


References
1.
Hawkins G, Brown S, Steyvers M, Wagenmakers E . Decision speed induces context effects in choice. Exp Psychol. 2012; 59(4):206-15. DOI: 10.1027/1618-3169/a000145. View

2.
Ratcliff R, McKoon G . The diffusion decision model: theory and data for two-choice decision tasks. Neural Comput. 2007; 20(4):873-922. PMC: 2474742. DOI: 10.1162/neco.2008.12-06-420. View

3.
Ratcliff R, Smith P . A comparison of sequential sampling models for two-choice reaction time. Psychol Rev. 2004; 111(2):333-67. PMC: 1440925. DOI: 10.1037/0033-295X.111.2.333. View

4.
van Vugt M, Simen P, Nystrom L, Holmes P, Cohen J . EEG oscillations reveal neural correlates of evidence accumulation. Front Neurosci. 2012; 6:106. PMC: 3398314. DOI: 10.3389/fnins.2012.00106. View

5.
Ratcliff R, Philiastides M, Sajda P . Quality of evidence for perceptual decision making is indexed by trial-to-trial variability of the EEG. Proc Natl Acad Sci U S A. 2009; 106(16):6539-44. PMC: 2672543. DOI: 10.1073/pnas.0812589106. View