Proactive Information Sampling in Value-Based Decision-Making: Deciding When and Where to Saccade

被引:13
作者
Song, Mingyu [1 ,2 ,3 ]
Wang, Xingyu [1 ,2 ,4 ]
Zhang, Hang [1 ,2 ,5 ,6 ]
Li, Jian [1 ,2 ]
机构
[1] Peking Univ, Sch Psychol & Cognit Sci, Beijing, Peoples R China
[2] Peking Univ, Beijing Key Lab Behav & Mental Hlth, Beijing, Peoples R China
[3] Princeton Univ, Princeton Neurosci Inst, Princeton, NJ 08544 USA
[4] Northwestern Univ, Dept Ind Engn & Management Sci, Evanston, IL 60208 USA
[5] Peking Univ, PKU IDG, McGovern Inst Brain Res, Beijing, Peoples R China
[6] Peking Tsinghua Ctr Life Sci, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
decision-making; eye-tracking; information sampling; Bayesian inference; drift-diffusion model; VISUAL FIXATIONS; CHOICE; NEUROBIOLOGY; COMPUTATION; SPEED; BIAS;
D O I
10.3389/fnhum.2019.00035
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Evidence accumulation has been the core component in recent development of perceptual and value-based decision-making theories. Most studies have focused on the evaluation of evidence between alternative options. What remains largely unknown is the process that prepares evidence: how may the decision-maker sample different sources of information sequentially, if they can only sample one source at a time? Here we propose a theoretical framework in prescribing how different sources of information should be sampled to facilitate the decision process: beliefs for different noisy sources are updated in a Bayesian manner and participants can proactively allocate resource for sampling (i.e., saccades) among different sources to maximize the information gain in such process. We show that our framework can account for human participants' actual choice and saccade behavior in a two-alternative value-based decision-making task. Moreover, our framework makes novel predictions about the empirical eye movement patterns.
引用
收藏
页数:10
相关论文
共 36 条
[1]   Cost-sensitive Bayesian control policy in human active sensing [J].
Ahmad, Sheeraz ;
Huang, He ;
Yu, Angela J. .
FRONTIERS IN HUMAN NEUROSCIENCE, 2014, 8
[2]  
[Anonymous], 2012, BAYESIAN STAT INTRO
[3]  
Armel KC, 2008, JUDGM DECIS MAK, V3, P396
[4]   Perceptual decision making: drift-diffusion model is equivalent to a Bayesian model [J].
Bitzer, Sebastian ;
Park, Hame ;
Blankenburg, Felix ;
Kiebel, Stefan J. .
FRONTIERS IN HUMAN NEUROSCIENCE, 2014, 8
[5]   Short-term memory traces for action bias in human reinforcement learning [J].
Bogacz, Rafal ;
McClure, Samuel M. ;
Li, Jian ;
Cohen, Jonathan D. ;
Montague, P. Read .
BRAIN RESEARCH, 2007, 1153 :111-121
[6]   Optimal decision-making theories: linking neurobiology with behaviour [J].
Bogacz, Rafal .
TRENDS IN COGNITIVE SCIENCES, 2007, 11 (03) :118-125
[7]   The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced-choice tasks [J].
Bogacz, Rafal ;
Brown, Eric ;
Moehlis, Jeff ;
Holmes, Philip ;
Cohen, Jonathan D. .
PSYCHOLOGICAL REVIEW, 2006, 113 (04) :700-765
[8]   Reminders of past choices bias decisions for reward in humans [J].
Bornstein, Aaron M. ;
Khaw, Mel W. ;
Shohamy, Daphna ;
Daw, Nathaniel D. .
NATURE COMMUNICATIONS, 2017, 8
[9]   Infomax Control of Eye Movements [J].
Butko, Nicholas J. ;
Movellan, Javier R. .
IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT, 2010, 2 (02) :91-107
[10]   Adaptive Sampling of Information in Perceptual Decision-Making [J].
Cassey, Thomas C. ;
Evens, David R. ;
Bogacz, Rafal ;
Marshall, James A. R. ;
Ludwig, Casimir J. H. .
PLOS ONE, 2013, 8 (11)