Economic value biases uncertain perceptual choices in the parietal and prefrontal cortices.
An observer detecting a noisy sensory signal is biased by the costs and benefits associated with its presence or absence. When these costs and benefits are asymmetric, sensory, and economic information must be integrated to inform the final choice. However, it remains unknown how this information is combined at the neural or computational levels. To address this question, we asked healthy human observers to judge the presence or absence of a noisy sensory signal under economic conditions that favored yes responses (liberal blocks), no responses (conservative blocks), or neither response (neutral blocks). Economic information biased fast choices more than slow choices, suggesting that value and sensory information are integrated early in the decision epoch. More formal simulation analyses using an Ornstein-Uhlenbeck process demonstrated that the influence of economic information was best captured by shifting the origin of evidence accumulation toward the more valuable bound. We then used the computational model to generate trial-by-trial estimates of decision-related evidence that were based on combined sensory and economic information (the decision variable or DV), and regressed these against fMRI activity recorded whilst participants performed the task. Extrastriate visual regions responded to the level of sensory input (momentary evidence), but fMRI signals in the parietal and prefrontal cortices responded to the decision variable. These findings support recent single-neuron data suggesting that economic information biases decision-related signals in higher cortical regions.
Department of Experimental Psychology, University of Oxford Oxford, UK.
Christopher
C
Summerfield
Etienne
E
Koechlin
J Vis. 2008 Mar 06;8(3):2.1-15
18484808
J Neurosci. 2007 Sep 12;27(37):9984-8
17855612
Nature. 1999 May 13;399(6732):148-51
10335843
Nat Neurosci. 2006 May;9(5):682-9
16604069
Nat Neurosci. 2007 Dec;10(12):1625-33
17982449
Curr Biol. 2008 Apr 22;18(8):614-8
18424143
Nature. 1995 Sep 7;377(6544):59-62
7659161
Nat Neurosci. 2009 May;12(5):535-40
19396166
J Neurophysiol. 2010 Jun;103(6):3238-47
20357071
Nature. 1999 Jul 15;400(6741):233-8
10421364
Proc Natl Acad Sci U S A. 2006 Jan 10;103(2):449-54
16407167
Neuron. 2008 Dec 26;60(6):1169-81
19109919
Neuron. 2007 Jul 19;55(2):301-12
17640530
J Neurosci. 2008 May 28;28(22):5623-30
18509023
Percept Psychophys. 2008 Feb;70(2):229-56
18372746
Neuron. 2008 Jul 31;59(2):336-47
18667160
Neuroimage. 2002 Apr;15(4):870-8
11906227
PLoS Comput Biol. 2009 Feb;5(2):e1000284
19214201
Nature. 2006 Jun 15;441(7095):876-9
16778890
Trends Cogn Sci. 2008 Aug;12(8):291-7
18614390
Annu Rev Psychol. 1999;50:243-71
10074679
Percept Psychophys. 2006 Feb;68(2):194-207
16773893
J Cogn Neurosci. 2010 May;22(5):875-87
19413475
Neuron. 2009 Jun 11;62(5):733-43
19524531
J Neurophysiol. 2001 Oct;86(4):1916-36
11600651
Neuroimage. 2001 Nov;14(5):1136-49
11697945
Psychol Rev. 2008 Jan;115(1):44-82
18211184
PLoS One. 2010 Feb 19;5(2):e9308
20174574
Nature. 1996 Jun 6;381(6582):520-2
8632824
Psychol Rev. 1993 Jul;100(3):432-59
8356185
Psychol Rev. 2006 Oct;113(4):700-65
17014301
Neuron. 2009 Sep 24;63(6):733-45
19778504
Brain Cogn. 2007 Nov;65(2):145-68
17923222
Neural Comput. 2008 Apr;20(4):873-922
18085991
J Exp Psychol Anim Behav Process. 1984 Jul;10(3):390-409
6747556
J Neurosci. 2008 Dec 17;28(51):13866-75
19091976
Spat Vis. 1997;10(4):433-6
9176952
Neuron. 2010 Mar 25;65(6):927-39
20346766
J Neurosci. 1994 Apr;14(4):2190-9
8158265
Annu Rev Neurosci. 2007;30:535-74
17600525
Neuron. 1999 Apr;22(4):751-61
10230795
Neuron. 2001 Feb;29(2):529-35
11239441