Literature DB >> 32839233

Confluence of Timing and Reward Biases in Perceptual Decision-Making Dynamics.

Maxwell Shinn1,2, Daniel B Ehrlich1,2, Daeyeol Lee3,4,5,6,7, John D Murray8,2, Hyojung Seo8,2.   

Abstract

Although the decisions of our daily lives often occur in the context of temporal and reward structures, the impact of such regularities on decision-making strategy is poorly understood. Here, to explore how temporal and reward context modulate strategy, we trained 2 male rhesus monkeys to perform a novel perceptual decision-making task with asymmetric rewards and time-varying evidence reliability. To model the choice and response time patterns, we developed a computational framework for fitting generalized drift-diffusion models, which flexibly accommodate diverse evidence accumulation strategies. We found that a dynamic urgency signal and leaky integration, in combination with two independent forms of reward biases, best capture behavior. We also tested how temporal structure influences urgency by systematically manipulating the temporal structure of sensory evidence, and found that the time course of urgency was affected by temporal context. Overall, our approach identified key components of cognitive mechanisms for incorporating temporal and reward structure into decisions.SIGNIFICANCE STATEMENT In everyday life, decisions are influenced by many factors, including reward structures and stimulus timing. While reward and timing have been characterized in isolation, ecologically valid decision-making involves a multiplicity of factors acting simultaneously. This raises questions about whether the same decision-making strategy is used when these two factors are concurrently manipulated. To address these questions, we trained rhesus monkeys to perform a novel decision-making task with both reward asymmetry and temporal uncertainty. In order to understand their strategy and hint at its neural mechanisms, we used the new generalized drift diffusion modeling framework to model both reward and timing mechanisms. We found two of each reward and timing mechanisms are necessary to explain our data.
Copyright © 2020 the authors.

Entities:  

Keywords:  decision-making; generalized drift diffusion model; reward; strategy; temporal uncertainty; urgency

Year:  2020        PMID: 32839233      PMCID: PMC7534922          DOI: 10.1523/JNEUROSCI.0544-20.2020

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  68 in total

1.  Neural computations that underlie decisions about sensory stimuli.

Authors:  J I. Gold; M N. Shadlen
Journal:  Trends Cogn Sci       Date:  2001-01-01       Impact factor: 20.229

2.  Counting probability distributions: differential geometry and model selection.

Authors:  I J Myung; V Balasubramanian; M A Pitt
Journal:  Proc Natl Acad Sci U S A       Date:  2000-10-10       Impact factor: 11.205

Review 3.  The importance of decision onset.

Authors:  Tobias Teichert; Jack Grinband; Vincent Ferrera
Journal:  J Neurophysiol       Date:  2015-11-25       Impact factor: 2.714

4.  Bounded integration in parietal cortex underlies decisions even when viewing duration is dictated by the environment.

Authors:  Roozbeh Kiani; Timothy D Hanks; Michael N Shadlen
Journal:  J Neurosci       Date:  2008-03-19       Impact factor: 6.167

5.  The role of premature evidence accumulation in making difficult perceptual decisions under temporal uncertainty.

Authors:  Ciara A Devine; Christine Gaffney; Gerard M Loughnane; Simon P Kelly; Redmond G O'Connell
Journal:  Elife       Date:  2019-11-27       Impact factor: 8.140

6.  Reward rate optimization in two-alternative decision making: empirical tests of theoretical predictions.

Authors:  Patrick Simen; David Contreras; Cara Buck; Peter Hu; Philip Holmes; Jonathan D Cohen
Journal:  J Exp Psychol Hum Percept Perform       Date:  2009-12       Impact factor: 3.332

7.  Integration of sensory and reward information during perceptual decision-making in lateral intraparietal cortex (LIP) of the macaque monkey.

Authors:  Alan E Rorie; Juan Gao; James L McClelland; William T Newsome
Journal:  PLoS One       Date:  2010-02-19       Impact factor: 3.240

8.  Do the dynamics of prior information depend on task context? An analysis of optimal performance and an empirical test.

Authors:  Don van Ravenzwaaij; Martijn J Mulder; Francis Tuerlinckx; Eric-Jan Wagenmakers
Journal:  Front Psychol       Date:  2012-05-29

9.  Dynamic combination of sensory and reward information under time pressure.

Authors:  Shiva Farashahi; Chih-Chung Ting; Chang-Hao Kao; Shih-Wei Wu; Alireza Soltani
Journal:  PLoS Comput Biol       Date:  2018-03-27       Impact factor: 4.475

10.  Ongoing, rational calibration of reward-driven perceptual biases.

Authors:  Yunshu Fan; Joshua I Gold; Long Ding
Journal:  Elife       Date:  2018-10-10       Impact factor: 8.140

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  5 in total

1.  Accumulation of continuously time-varying sensory evidence constrains neural and behavioral responses in human collision threat detection.

Authors:  Gustav Markkula; Zeynep Uludağ; Richard McGilchrist Wilkie; Jac Billington
Journal:  PLoS Comput Biol       Date:  2021-07-15       Impact factor: 4.475

2.  Timescales of Cognition in the Brain.

Authors:  Alireza Soltani; John D Murray; Hyojung Seo; Daeyeol Lee
Journal:  Curr Opin Behav Sci       Date:  2021-03-31

3.  Transient neuronal suppression for exploitation of new sensory evidence.

Authors:  Maxwell Shinn; Daeyeol Lee; John D Murray; Hyojung Seo
Journal:  Nat Commun       Date:  2022-01-10       Impact factor: 17.694

4.  Accumulation of evidence during decision making in OCD patients.

Authors:  Yilin Chen; Ying Liu; Zhen Wang; Tianming Yang; Qing Fan
Journal:  Front Psychiatry       Date:  2022-09-23       Impact factor: 5.435

5.  A flexible framework for simulating and fitting generalized drift-diffusion models.

Authors:  Maxwell Shinn; Norman H Lam; John D Murray
Journal:  Elife       Date:  2020-08-04       Impact factor: 8.140

  5 in total

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