scholarly journals Orbitofrontal cortex neurons code utility changes during natural reward consumption as correlates of relative reward-specific satiety

2020 ◽  
Author(s):  
Alexandre Pastor-Bernier ◽  
Arkadiusz Stasiak ◽  
Wolfram Schultz
Keyword(s):  
Orbitofrontal Cortex ◽  
Revealed Preference ◽  
Two Dimensional ◽  
Independent Manner ◽  
Preference Theory ◽  
Economic Choice ◽  
Subjective Value ◽  
Differential Reward ◽  
Specific Alteration ◽  
Differential Utility

AbstractNatural, on-going reward consumption can differentially reduce the subjective value (‘utility’) of specific rewards, which indicates relative, reward-specific satiety. Two-dimensional choice indifference curves (IC) represent the utility of choice options with two distinct reward components (‘bundles’) according to Revealed Preference Theory. We estimated two-dimensional ICs from stochastic choices and found that natural on-going consumption of two bundle rewards induced specific IC distortions that indicated differential reduction of reward utility indicative of relative reward-specific satiety. Licking changes confirmed satiety in a mechanism-independent manner. Neuronal signals in orbitofrontal cortex (OFC) that coded the value of the chosen option followed closely the consumption-induced IC distortions within recording periods of individual neurons. A neuronal classifier predicted well the changed utility inferred from the altered behavioral choices. Neuronal signals for more conventional single-reward choice options showed similar relationships to utility alterations from on-going consumption. These results demonstrate a neuronal substrate for the differential, reward-specific alteration of utility by on-going reward consumption reflecting reward-specific satiety.SignificanceRepeated delivery reduces the subjective value (‘utility’) of rewards to different degrees depending on their individual properties, a phenomenon commonly referred to as sensory-specific satiety. We tested monkeys during economic choice of two-component options. On-going consumption differentially reduced reward utility in a way that suggested relative reward-specific satiety between the two components. Neurons in the orbitofrontal cortex (OFC) changed their responses in close correspondence to the differential utility reduction, thus representing a neuronal correlate of relative reward-specific satiety. Control experiments with conventional single-component choice showed similar satiety-induced differential response reductions. These results are compatible with the notion of OFC neurons coding crucial decision variables robustly across different satiety levels.

scholarly journals Reward-specific satiety affects subjective value signals in orbitofrontal cortex during multicomponent economic choice

2021 ◽  
Vol 118 (30) ◽  
pp. e2022650118
Author(s):  
Alexandre Pastor-Bernier ◽  
Arkadiusz Stasiak ◽  
Wolfram Schultz
Keyword(s):  
Orbitofrontal Cortex ◽  
Economic Value ◽  
Revealed Preference ◽  
Value Change ◽  
Neuronal Coding ◽  
Preference Theory ◽  
Subjective Value ◽  
Reward Value ◽  
Value Loss ◽  
Reward Functions

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.

scholarly journals Orbitofrontal signals for two-component choice options comply with indifference curves of Revealed Preference Theory

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Alexandre Pastor-Bernier ◽  
Arkadiusz Stasiak ◽  
Wolfram Schultz
Keyword(s):  
Orbitofrontal Cortex ◽  
Revealed Preference ◽  
Confidence Limits ◽  
Stochastic Choice ◽  
Preference Theory ◽  
Economic Choice ◽  
Multiple Components ◽  
Economic Decision Making ◽  
Two Component ◽  
Revealed Preference Theory

Abstract Economic choice options contain multiple components and constitute vectorial bundles. The question arises how they are represented by single-dimensional, scalar neuronal signals that are suitable for economic decision-making. Revealed Preference Theory provides formalisms for establishing preference relations between such bundles, including convenient graphic indifference curves. During stochastic choice between bundles with the same two juice components, we identified neuronal signals for vectorial, multi-component bundles in the orbitofrontal cortex of monkeys. A scalar signal integrated the values from all bundle components in the structured manner of the Theory; it followed the behavioral indifference curves within their confidence limits, was indistinguishable between differently composed but equally revealed preferred bundles, predicted bundle choice and complied with an optimality axiom. Further, distinct signals in other neurons coded the option components separately but followed indifference curves as a population. These data demonstrate how scalar signals represent vectorial, multi-component choice options.

scholarly journals Single-dimensional human brain signals for two-dimensional economic choice options

2020 ◽  
Author(s):  
Leo Chi U Seak ◽  
Konstantin Volkmann ◽  
Alexandre Pastor-Bernier ◽  
Fabian Grabenhorst ◽  
Wolfram Schultz
Keyword(s):  
Orbitofrontal Cortex ◽  
Brain Structures ◽  
Two Dimensional ◽  
Neural Signals ◽  
Economic Choice ◽  
Multiple Components ◽  
Brain Responses ◽  
Subjective Value ◽  
Behavioral Preferences ◽  
Value Coding

AbstractRewarding choice options typically contain multiple components, but neural signals in single brain voxels are scalar and primarily vary up or down. In a previous study, we had designed reward bundles that contained the same two milkshakes with independently set amounts; we had used psychophysics and rigorous economic concepts to estimate two-dimensional choice indifference curves (IC) that represented revealed stochastic preferences for these bundles in a systematic, integrated manner. All bundles on the same ICs were equally revealed preferred (and thus had same utility, as inferred from choice indifference); bundles on higher ICs (higher utility) were preferred to bundles on lower ICs (lower utility). In the current study, we used the established behavior for testing with functional magnetic resonance imaging (fMRI). We now demonstrate neural responses in reward-related brain structures of human female and male participants, including striatum, midbrain and medial orbitofrontal cortex that followed the characteristic pattern of ICs: similar responses along ICs (same utility despite different bundle composition), but monotonic change across ICs (different utility). Thus, these brain structures integrated multiple reward components into a scalar signal, well beyond the known subjective value coding of single-component rewards.Significance StatementRewards have several components, like the taste and size of an apple, but it is unclear how each component contributes to the overall value of the reward. While choice indifference curves of economic theory provide behavioural approaches to this question, it is unclear whether brain responses capture the preference and utility integrated from multiple components. We report activations in striatum, midbrain and orbitofrontal cortex that follow choice indifference curves representing behavioral preferences over and above variations of individual reward components. In addition, the concept-driven approach encourages future studies on natural, multi-component rewards that are prone to irrational choice of normal and brain-damaged individuals.

scholarly journals Summarized distributions of mass: a statistical approach to consumers’ consumption spaces

2021 ◽  
pp. 1-13
Author(s):  
Pierpaolo Angelini ◽  
Fabrizio Maturo
Keyword(s):  
Real Number ◽  
Convex Set ◽  
Research Work ◽  
Weighted Average ◽  
Revealed Preference ◽  
Linear Measure ◽  
Two Dimensional ◽  
Preference Theory ◽  
States Of Nature ◽  
Revealed Preference Theory

This paper focuses on logical aspects of choices being made by the consumer under conditions of uncertainty or certainty. Such logical aspects are found out to be the same. Choices being made by the consumer that should maximize her subjective utility are decisions studied by revealed preference theory. A finite number of possible alternatives is considered. They are mutually exclusive propositions identifying all quantitative states of nature of a consumption plan. Each proposition of it is expressed by a real number. This research work distinguishes it from its temporary truth value depending on the state of information and knowledge of the consumer. Since each point of the consumption space of the consumer belongs to a two-dimensional convex set, this article focuses on conjoint distributions of mass. Indeed, the consumption space of the consumer is generated by all coherent summaries of a conjoint distribution of mass. Each point of her consumption space is connected with a weighted average of states of nature of two consumption plans jointly studied. They give rise to a conjoint distribution of mass. The consumer chooses a point of a two-dimensional convex set representing that bundle of goods actually demanded by her inside of her consumption space. This paper innovatively shows that it is nothing but a bilinear and disaggregate measure. It is decomposed into two real numbers, where each real number is a linear measure. In this paper, different measures are obtained. They can be disaggregate or aggregate measures, where the latter are independent of the notion of ordered pair of consumption plans.

scholarly journals Neuronal Adaptation to the Value Range in the Macaque Orbitofrontal Cortex

2018 ◽  
Author(s):  
Katherine E. Conen ◽  
Camillo Padoa-Schioppa
Keyword(s):  
Orbitofrontal Cortex ◽  
Behavioral Context ◽  
Minimum Value ◽  
Economic Choice ◽  
Maximum Value ◽  
Baseline Activity ◽  
Subjective Value ◽  
Baseline Response ◽  
Value Range ◽  
Decision Circuit

AbstractEconomic choice involves computing and comparing the subjective values of different options. The magnitude of these values can vary immensely in different situations. To compensate for this variability, decision-making neural circuits adapt to the current behavioral context. In orbitofrontal cortex (OFC), neurons encode the subjective value of offered and chosen goods in a quasi-linear way. Previous work found that the gain of the encoding is lower when the value range is wider. However, previous studies did not disambiguate between neurons adapting to the value range or to the maximum value. Furthermore, they did not examine changes in baseline activity. Here we investigated how neurons in the macaque OFC adapt to changes in the value distribution. We found that neurons adapt to both the maximum and the minimum value, but only partially. Concurrently, the baseline response is higher when the minimum value is larger. Using a simulated decision circuit, we showed that higher baseline activity increases choice variability, and thus lowers the expected payoff in high value contexts.

scholarly journals Mechanisms Underlying Economic Revealed Preference Of Multi-Component Bundles In Primate Orbitofrontal Cortex

2021 ◽  
Author(s):  
Alexandre Pastor-Bernier ◽  
Wolfram Schultz
Keyword(s):  
Orbitofrontal Cortex ◽  
Neural Coding ◽  
Revealed Preference ◽  
Population Level ◽  
Random Utility ◽  
Preference Theory ◽  
Fundamental Properties ◽  
Partial Compliance ◽  
Neurophysiological Data ◽  
Choice Processing

ABSTRACTClassic neuro-economic studies suggest that people and animals alike assign values to individual attributes arriving at a total summed valuation and therefore overall appraisal of the offer (Kahnt et al., 2011; Lak et al., 2014). However, further work is needed to determine whether such individual attribute appraisals can predict choice between multi-component bundles. In this paper we show the importance and applications of economic theory to decision neurophysiology concerning multi-component attribute options or bundles. We applied random utility modelling (McFadden, 1973) to choices amongst 10 different two-component bundles in non-human primates (Macacca mulatta). We found that behavioural random utility (RUM) complies well with revealed preference theory and recapitulates the fundamental properties of empirically obtained indifference curves (IC) revealing the synergetic interactions between components. We extended RUM to neurophysiological data and focused our investigation to orbitofrontal cortex A13. Neuronal RUM complied with behavioural RUM at single neuron and population level (N:54). Neural coding of utility was present at target onset, choice, and reward. However, a distinct and separate group of neurons (N:26) showed partial compliance with utility in either the chosen or the unchosen bundle options and correlated with both value and probability of choice. We show that relative chosen-utility coding constitutes a separate type of computation and suggest a role for this type of neuron in value-to-choice processing in OFC.

Revealed preference, belief, and game theory

2000 ◽  
Vol 16 (1) ◽  
pp. 99-115 ◽  
Author(s):  
Daniel M. Hausman
Keyword(s):  
Game Theory ◽  
Revealed Preference ◽  
Preference Theory ◽  
Revealed Preference Theory ◽  
Subjective States

The notion of ‘revealed preference’ is unclear and should be abandoned. Defenders of the theory of revealed preference have misinterpreted legitimate concerns about the testability of economics as the demand that economists eschew reference to (unobservable) subjective states. As attempts to apply revealed-preference theory to game theory illustrate with particular vividness, this demand is mistaken.

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