scholarly journals The Architecture of Reward Value Coding in the Human Orbitofrontal Cortex

2010 ◽  
Vol 30 (39) ◽  
pp. 13095-13104 ◽  
Author(s):  
G. Sescousse ◽  
J. Redoute ◽  
J.-C. Dreher
2020 ◽  
Vol 393 ◽  
pp. 112792
Author(s):  
Sanja Klein ◽  
Onno Kruse ◽  
Charlotte Markert ◽  
Isabell Tapia León ◽  
Jana Strahler ◽  
...  

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Yang Xie ◽  
Chechang Nie ◽  
Tianming Yang

During value-based decision making, we often evaluate the value of each option sequentially by shifting our attention, even when the options are presented simultaneously. The orbitofrontal cortex (OFC) has been suggested to encode value during value-based decision making. Yet it is not known how its activity is modulated by attention shifts. We investigated this question by employing a passive viewing task that allowed us to disentangle effects of attention, value, choice and eye movement. We found that the attention modulated OFC activity through a winner-take-all mechanism. When we attracted the monkeys’ attention covertly, the OFC neuronal activity reflected the reward value of the newly attended cue. The shift of attention could be explained by a normalization model. Our results strongly argue for the hypothesis that the OFC neuronal activity represents the value of the attended item. They provide important insights toward understanding the OFC’s role in value-based decision making.


2007 ◽  
Vol 66 (1) ◽  
pp. 96-112 ◽  
Author(s):  
Edmund T. Rolls

Complementary neurophysiological recordings in rhesus macaques (Macaca mulatta) and functional neuroimaging in human subjects show that the primary taste cortex in the rostral insula and adjoining frontal operculum provides separate and combined representations of the taste, temperature and texture (including viscosity and fat texture) of food in the mouth independently of hunger and thus of reward value and pleasantness. One synapse on, in the orbitofrontal cortex, these sensory inputs are for some neurons combined by learning with olfactory and visual inputs. Different neurons respond to different combinations, providing a rich representation of the sensory properties of food. In the orbitofrontal cortex feeding to satiety with one food decreases the responses of these neurons to that food, but not to other foods, showing that sensory-specific satiety is computed in the primate (including the human) orbitofrontal cortex. Consistently, activation of parts of the human orbitofrontal cortex correlates with subjective ratings of the pleasantness of the taste and smell of food. Cognitive factors, such as a word label presented with an odour, influence the pleasantness of the odour, and the activation produced by the odour in the orbitofrontal cortex. Food intake is thus controlled by building a multimodal representation of the sensory properties of food in the orbitofrontal cortex and gating this representation by satiety signals to produce a representation of the pleasantness or reward value of food that drives food intake. Factors that lead this system to become unbalanced and contribute to overeating and obesity are described.


2010 ◽  
Vol 104 (6) ◽  
pp. 3424-3432 ◽  
Author(s):  
Maria A. Bermudez ◽  
Wolfram Schultz

Animals assess the values of rewards to learn and choose the best possible outcomes. We studied how single neurons in the primate amygdala coded reward magnitude, an important variable determining the value of rewards. A single, Pavlovian-conditioned visual stimulus predicted fruit juice to be delivered with one of three equiprobable volumes ( P = 1/3). A population of amygdala neurons showed increased activity after reward delivery, and almost one half of these responses covaried with reward magnitude in a monotonically increasing or decreasing fashion. A subset of the reward responding neurons were tested with two different probability distributions of reward magnitude; the reward responses in almost one half of them adapted to the predicted distribution and thus showed reference-dependent coding. These data suggest parametric reward value coding in the amygdala as a characteristic component of its function in reinforcement learning and economic decision making.


2021 ◽  
Author(s):  
Vincent B. McGinty ◽  
Shira M. Lupkin

ABSTRACTNeuroeconomics seeks to explain how neural activity contributes to decision behavior. For value-based decisions, the primate orbitofrontal cortex (OFC) is thought to have a key role; however, the mechanism by which single OFC cells contribute to choices is still unclear. Here, we show for the first time a trial-to-trial relationship between choices and population-level value representations in OFC, defined by the weighted sum of activity from many individual value-coding neurons.


2021 ◽  
Vol 118 (30) ◽  
pp. e2022650118
Author(s):  
Alexandre Pastor-Bernier ◽  
Arkadiusz Stasiak ◽  
Wolfram Schultz

Sensitivity to satiety constitutes a basic requirement for neuronal coding of subjective reward value. Satiety from natural ongoing consumption affects reward functions in learning and approach behavior. More specifically, satiety reduces the subjective economic value of individual rewards during choice between options that typically contain multiple reward components. The unconfounded assessment of economic reward value requires tests at choice indifference between two options, which is difficult to achieve with sated rewards. By conceptualizing choices between options with multiple reward components (“bundles”), Revealed Preference Theory may offer a solution. Despite satiety, choices against an unaltered reference bundle may remain indifferent when the reduced value of a sated bundle reward is compensated by larger amounts of an unsated reward of the same bundle, and then the value loss of the sated reward is indicated by the amount of the added unsated reward. Here, we show psychophysically titrated choice indifference in monkeys between bundles of differently sated rewards. Neuronal chosen value signals in the orbitofrontal cortex (OFC) followed closely the subjective value change within recording periods of individual neurons. A neuronal classifier distinguishing the bundles and predicting choice substantiated the subjective value change. The choice between conventional single rewards confirmed the neuronal changes seen with two-reward bundles. Thus, reward-specific satiety reduces subjective reward value signals in OFC. With satiety being an important factor of subjective reward value, these results extend the notion of subjective economic reward value coding in OFC neurons.


Neuron ◽  
2013 ◽  
Vol 80 (6) ◽  
pp. 1519-1531 ◽  
Author(s):  
Peter H. Rudebeck ◽  
Andrew R. Mitz ◽  
Ravi V. Chacko ◽  
Elisabeth A. Murray

2020 ◽  
pp. 192-216
Author(s):  
Edmund T. Rolls

Information is represented in taste regions up to and including the insular primary taste system of what the taste is independent of its reward value and pleasantness with a sparse distributed representation of sweet, salt, bitter, sour and umami inputs. The texture of food in the mouth, including fat texture, is also represented in these areas. The insular taste cortex then projects to the orbitofrontal cortex, in which the reward value and pleasantness of the taste and flavour are represented, with olfactory components included.


2020 ◽  
pp. 379-446
Author(s):  
Edmund T. Rolls

The orbitofrontal cortex receives from the ends of all sensory processing systems, and converts these representations of what the stimulus is into representations of their reward value. The orbitofrontal cortex is therefore a key brain region in emotions, which can be defined as states elicited by rewards and punishers. Indeed, orbitofrontal cortex activations are linearly related to the subjectively reported pleasantness of stimuli. The orbitofrontal cortex then projects this reward value information to other structures, which implement behavioural output, such as the anterior cingulate cortex, and the basal ganglia. A key computational capacity of the orbitofrontal cortex is one-trial object-reward associations, which are rule-based, and enable primates including humans to change their rewarded behaviour very rapidly. Decision-making using attractor neural networks is described.


Brain ◽  
2016 ◽  
Vol 139 (4) ◽  
pp. 1295-1309 ◽  
Author(s):  
Yansong Li ◽  
Giovanna Vanni-Mercier ◽  
Jean Isnard ◽  
François Mauguière ◽  
Jean-Claude Dreher

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