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 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 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 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 Effects of compassion training on brain responses to suffering others

2019 ◽  
Author(s):  
Yoni K. Ashar ◽  
Jessica R. Andrews-Hanna ◽  
Joan Halifax ◽  
Sona Dimidjian ◽  
Tor D. Wager
Keyword(s):  
Orbitofrontal Cortex ◽  
Smartphone Application ◽  
Post Intervention ◽  
Placebo Condition ◽  
Demand Characteristics ◽  
Compassion Meditation ◽  
Brain Responses ◽  
Author Note ◽  
Additional Support ◽  
Research Assistants

AbstractWhat are the active ingredients and brain mechanisms of compassion training? To address these questions, we conducted a three-armed randomized trial (N = 57) of compassion meditation (CM). We compared a four-week CM program delivered by smartphone application to i) a placebo condition, in which participants inhaled sham oxytocin, which they were told would enhance compassion, and ii) a familiarity control condition, designed to control for increased familiarity with suffering others. Functional MRI was collected while participants listened to narratives describing suffering others at pre- and post-intervention. CM increased brain responses to suffering others in the medial orbitofrontal cortex (mOFC) relative to both the placebo and familiarity control conditions, and in the nucleus accumbens relative to the familiarity control condition. Results support the specific efficacy of CM beyond effects of expectancy, demand characteristics, and increased familiarity with suffering others, and implicate affective and motivational pathways as brain mechanisms of CM.Author NoteFunded by the John Templeton Foundation’s Positive Neuroscience project (PIs Wager and Dimidjian), with additional support from NIH R01 R01DA035484 (PI Wager). Gratitude to research assistants Jenifer Mutari, Robin Kay, Scott Meyers, Nicholas Peterson, and Brandin Williams for help with data collection.

scholarly journals A neuronal circuit for vector computation builds an allocentric traveling-direction signal in the Drosophila fan-shaped body

2020 ◽  
Author(s):  
Cheng Lyu ◽  
L.F. Abbott ◽  
Gaby Maimon
Keyword(s):  
Computational Models ◽  
Neuronal Population ◽  
The Body ◽  
Central Complex ◽  
Two Dimensional ◽  
Neuronal Circuit ◽  
Head Direction ◽  
Neural Signals ◽  
Shaped Body ◽  
External Cues

AbstractMany behavioral tasks require the manipulation of mathematical vectors, but, outside of computational models1–8, it is not known how brains perform vector operations. Here we show how the Drosophila central complex, a region implicated in goal-directed navigation8–14, performs vector arithmetic. First, we describe neural signals in the fan-shaped body that explicitly track a fly’s allocentric traveling direction, that is, the traveling direction in reference to external cues. Past work has identified neurons in Drosophila12,15–17 and mammals18,19 that track allocentric heading (e.g., head-direction cells), but these new signals illuminate how the sense of space is properly updated when traveling and heading angles differ. We then characterize a neuronal circuit that rotates, scales, and adds four vectors related to the fly’s egocentric traveling direction–– the traveling angle referenced to the body axis––to compute the allocentric traveling direction. Each two-dimensional vector is explicitly represented by a sinusoidal activity pattern across a distinct neuronal population, with the sinusoid’s amplitude representing the vector’s length and its phase representing the vector’s angle. The principles of this circuit, which performs an egocentric-to-allocentric coordinate transformation, may generalize to other brains and to domains beyond navigation where vector operations or reference-frame transformations are required.

scholarly journals Differential encoding of action selection by orbitofrontal and striatal population dynamics

2020 ◽  
Vol 124 (2) ◽  
pp. 634-644
Author(s):  
Long Yang ◽  
Sotiris C. Masmanidis
Keyword(s):  
Population Dynamics ◽  
Orbitofrontal Cortex ◽  
Action Selection ◽  
Choice Task ◽  
Previous Literature ◽  
Neural Signals ◽  
Population Activity ◽  
Differential Encoding ◽  
Alternative Choice ◽  
Dorsomedial Striatum

While previous literature shows that both orbitofrontal cortex (OFC) and dorsomedial striatum (DMS) represent information relevant to selecting specific actions, few studies have directly compared neural signals between these areas. Here we compared OFC and DMS dynamics in mice performing a two-alternative choice task. We found that the animal’s choice could be decoded more accurately from DMS population activity. This work provides among the first evidence that OFC and DMS differentially represent information about an animal’s selected action.

scholarly journals Normalized value coding explains dynamic adaptation in the human valuation process

2017 ◽  
Vol 114 (48) ◽  
pp. 12696-12701 ◽  
Author(s):  
Mel W. Khaw ◽  
Paul W. Glimcher ◽  
Kenway Louie
Keyword(s):  
Context Effects ◽  
Human Subjects ◽  
Neural Representation ◽  
Dynamic Adaptation ◽  
Temporal Context ◽  
Decision Variable ◽  
Average Value ◽  
Subjective Value ◽  
Valuation Process ◽  
Value Coding

The notion of subjective value is central to choice theories in ecology, economics, and psychology, serving as an integrated decision variable by which options are compared. Subjective value is often assumed to be an absolute quantity, determined in a static manner by the properties of an individual option. Recent neurobiological studies, however, have shown that neural value coding dynamically adapts to the statistics of the recent reward environment, introducing an intrinsic temporal context dependence into the neural representation of value. Whether valuation exhibits this kind of dynamic adaptation at the behavioral level is unknown. Here, we show that the valuation process in human subjects adapts to the history of previous values, with current valuations varying inversely with the average value of recently observed items. The dynamics of this adaptive valuation are captured by divisive normalization, linking these temporal context effects to spatial context effects in decision making as well as spatial and temporal context effects in perception. These findings suggest that adaptation is a universal feature of neural information processing and offer a unifying explanation for contextual phenomena in fields ranging from visual psychophysics to economic choice.

scholarly journals Two-Dimensional Multiply-Accumulator for Classification of Neural Signals

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 19714-19725 ◽  
Author(s):  
Yu-Chieh Chen ◽  
Hsin-Chi Chang ◽  
Hsin Chen
Keyword(s):  
Two Dimensional ◽  
Neural Signals
Sign in / Sign up

Export Citation Format

Share Document