Preferences for nutrients and sensory food qualities identify biological sources of economic values in monkeys.

Value is a foundational concept in reinforcement learning and economic choice theory. In these frameworks, individuals choose by assigning values to objects and learn by updating values with experience. These theories have been instrumental for revealing influences of probability, risk, and delay on choices. However, they do not explain how values are shaped by intrinsic properties of the choice objects themselves. Here, we investigated how economic value derives from the biologically critical components of foods: their nutrients and sensory qualities. When monkeys chose nutrient-defined liquids, they consistently preferred fat and sugar to low-nutrient alternatives. Rather than maximizing energy indiscriminately, they seemed to assign subjective values to specific nutrients, flexibly trading them against offered reward amounts. Nutrient-value functions accurately modeled these preferences, predicted choices across contexts, and accounted for individual differences. The monkeys' preferences shifted their daily nutrient balance away from dietary reference points, contrary to ecological foraging models but resembling human suboptimal eating in free-choice situations. To identify the sensory basis of nutrient values, we developed engineering tools that measured food textures on biological surfaces, mimicking oral conditions. Subjective valuations of two key texture parameters-viscosity and sliding friction-explained the monkeys' fat preferences, suggesting a texture-sensing mechanism for nutrient values. Extended reinforcement learning and choice models identified candidate neuronal mechanisms for nutrient-sensitive decision-making. These findings indicate that nutrients and food textures constitute critical reward components that shape economic values. Our nutrient-choice paradigm represents a promising tool for studying food-reward mechanisms in primates to better understand human-like eating behavior and obesity.

Functions of primate amygdala neurons in economic decisions and social decision simulation.

Long implicated in aversive processing, the amygdala is now recognized as a key component of the brain systems that process rewards. Beyond reward valuation, recent findings from single-neuron recordings in monkeys indicate that primate amygdala neurons also play an important role in decision-making. The reward value signals encoded by amygdala neurons constitute suitable inputs to economic decision processes by being sensitive to reward contingency, relative reward quantity and temporal reward structure. During reward-based decisions, individual amygdala neurons encode both the value inputs and corresponding choice outputs of economic decision processes. The presence of such value-to-choice transitions in single amygdala neurons, together with other well-defined signatures of decision computation, indicate that a decision mechanism may be implemented locally within the primate amygdala. During social observation, specific amygdala neurons spontaneously encode these decision signatures to predict the choices of social partners, suggesting neural simulation of the partner's decision-making. The activity of these 'simulation neurons' could arise naturally from convergence between value neurons and social, self-other discriminating neurons. These findings identify single-neuron building blocks and computational architectures for decision-making and social behavior in the primate amygdala. An emerging understanding of the decision function of primate amygdala neurons can help identify potential vulnerabilities for amygdala dysfunction in human conditions afflicting social cognition and mental health.

Single-Dimensional Human Brain Signals for Two-Dimensional Economic Choice Options.

Rewarding 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 (ICs) 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 (mid-OFC) 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 STATEMENT Rewards 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 (ICs) of economic theory provide behavioral 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 (OFC) that follow choice ICs representing behavioral preferences over and above variations of individual reward components. In addition, the concept-driven approach encourages future studies on natural, multicomponent rewards that are prone to irrational choice of normal and brain-damaged individuals.

Experimentally revealed stochastic preferences for multicomponent choice options.

Realistic, everyday rewards contain multiple components. An apple has taste and size. However, we choose in single dimensions, simply preferring some apples to others. How can such single-dimensional preference relationships refer to multicomponent choice options? Here, we measured how stochastic choices revealed preferences for 2-component milkshakes. The preferences were intuitively graphed as indifference curves that represented the orderly integration of the 2 components as trade-off: parts of 1 component were given up for obtaining 1 additional unit of the other component without a change in preference. The well-ordered, nonoverlapping curves satisfied leave-one-out tests, followed predictions by machine learning decoders and correlated with single-dimensional Becker-DeGroot-Marschak (BDM) auction-like bids for the 2-component rewards. This accuracy suggests a decision process that integrates multiple reward components into single-dimensional estimates in a systematic fashion. In interspecies comparisons, human performance matched that of highly experienced laboratory monkeys, as measured by accuracy of the critical trade-off between bundle components. These data describe the nature of choices of multicomponent choice options and attest to the validity of the rigorous economic concepts and their convenient graphic schemes for explaining choices of human and nonhuman primates. The results encourage formal behavioral and neural investigations of normal, irrational, and pathological economic choices. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Primate prefrontal neurons signal economic risk derived from the statistics of recent reward experience.

Risk derives from the variation of rewards and governs economic decisions, yet how the brain calculates risk from the frequency of experienced events, rather than from explicit risk-descriptive cues, remains unclear. Here, we investigated whether neurons in dorsolateral prefrontal cortex process risk derived from reward experience. Monkeys performed in a probabilistic choice task in which the statistical variance of experienced rewards evolved continually. During these choices, prefrontal neurons signaled the reward-variance associated with specific objects ('object risk') or actions ('action risk'). Crucially, risk was not derived from explicit, risk-descriptive cues but calculated internally from the variance of recently experienced rewards. Support-vector-machine decoding demonstrated accurate neuronal risk discrimination. Within trials, neuronal signals transitioned from experienced reward to risk (risk updating) and from risk to upcoming choice (choice computation). Thus, prefrontal neurons encode the statistical variance of recently experienced rewards, complying with formal decision variables of object risk and action risk.

Author Correction: A dynamic code for economic object valuation in prefrontal cortex neurons.

This corrects the article DOI: 10.1038/ncomms12554.

Primate amygdala neurons evaluate the progress of self-defined economic choice sequences.

The amygdala is a prime valuation structure yet its functions in advanced behaviors are poorly understood. We tested whether individual amygdala neurons encode a critical requirement for goal-directed behavior: the evaluation of progress during sequential choices. As monkeys progressed through choice sequences toward rewards, amygdala neurons showed phasic, gradually increasing responses over successive choice steps. These responses occurred in the absence of external progress cues or motor preplanning. They were often specific to self-defined sequences, typically disappearing during instructed control sequences with similar reward expectation. Their build-up rate reflected prospectively the forthcoming choice sequence, suggesting adaptation to an internal plan. Population decoding demonstrated a high-accuracy progress code. These findings indicate that amygdala neurons evaluate the progress of planned, self-defined behavioral sequences. Such progress signals seem essential for aligning stepwise choices with internal plans. Their presence in amygdala neurons may inform understanding of human conditions with amygdala dysfunction and deregulated reward pursuit.

Neural Basis for Economic Saving Strategies in Human Amygdala-Prefrontal Reward Circuits.

Economic saving is an elaborate behavior in which the goal of a reward in the future directs planning and decision-making in the present. Here, we measured neural activity while subjects formed simple economic saving strategies to accumulate rewards and then executed their strategies through choice sequences of self-defined lengths. Before the initiation of a choice sequence, prospective activations in the amygdala predicted subjects' internal saving plans and their value up to two minutes before a saving goal was achieved. The valuation component of this planning activity persisted during execution of the saving strategy and predicted subjects' economic behavior across different tasks and testing days. Functionally coupled amygdala and prefrontal cortex activities encoded distinct planning components that signaled the transition from saving strategy formation to execution and reflected individual differences in saving behavior. Our findings identify candidate neural mechanisms for economic saving in amygdala and prefrontal cortex and suggest a novel planning function for the human amygdala in directing strategic behavior toward self-determined future rewards.

A dynamic code for economic object valuation in prefrontal cortex neurons.

Neuronal reward valuations provide the physiological basis for economic behaviour. Yet, how such valuations are converted to economic decisions remains unclear. Here we show that the dorsolateral prefrontal cortex (DLPFC) implements a flexible value code based on object-specific valuations by single neurons. As monkeys perform a reward-based foraging task, individual DLPFC neurons signal the value of specific choice objects derived from recent experience. These neuronal object values satisfy principles of competitive choice mechanisms, track performance fluctuations and follow predictions of a classical behavioural model (Herrnstein's matching law). Individual neurons dynamically encode both, the updating of object values from recently experienced rewards, and their subsequent conversion to object choices during decision-making. Decoding from unselected populations enables a read-out of motivational and decision variables not emphasized by individual neurons. These findings suggest a dynamic single-neuron and population value code in DLPFC that advances from reward experiences to economic object values and future choices.

Food labels promote healthy choices by a decision bias in the amygdala.

Food labeling is the major health policy strategy to counter rising obesity rates. Based on traditional economic theory, such strategies assume that detailed nutritional information will necessarily help individuals make better, healthier choices. However, in contrast to the well-known utility of labels in food marketing, evidence for the efficacy of nutritional labeling is mixed. Psychological and behavioral economic theories suggest that successful marketing strategies activate automatic decision biases and emotions, which involve implicit emotional brain systems. Accordingly, simple, intuitive food labels that engage these neural systems could represent a promising approach for promoting healthier choices. Here we used functional MRI to investigate this possibility. Healthy, mildly hungry subjects performed a food evaluation task and a food choice task. The main experimental manipulation was to pair identical foods with simple labels that emphasized either taste benefits or health-related food properties. We found that such labels biased food evaluations in the amygdala, a core emotional brain system. When labels biased the amygdala's evaluations towards health-related food properties, the strength of this bias predicted behavioral shifts towards healthier choices. At the time of decision-making, amygdala activity encoded key decision variables, potentially reflecting active amygdala participation in food choice. Our findings underscore the potential utility of food labeling in health policy and indicate a principal role for emotional brain systems when labels guide food choices.

Prediction of economic choice by primate amygdala neurons.

The amygdala is a key structure of the brain's reward system. Existing theories view its role in decision-making as restricted to an early valuation stage that provides input to decision mechanisms in downstream brain structures. However, the extent to which the amygdala itself codes information about economic choices is unclear. Here, we report that individual neurons in the primate amygdala predict behavioral choices in an economic decision task. We recorded the activity of amygdala neurons while monkeys chose between saving liquid reward with interest and spending the accumulated reward. In addition to known value-related responses, we found that activity in a group of amygdala neurons predicted the monkeys' upcoming save-spend choices with an average accuracy of 78%. This choice-predictive activity occurred early in trials, even before information about specific actions associated with save-spend choices was available. For a substantial number of neurons, choice-differential activity was specific for free, internally generated economic choices and not observed in a control task involving forced imperative choices. A subgroup of choice-predictive neurons did not show relationships to value, movement direction, or visual stimulus features. Choice-predictive activity in some amygdala neurons was preceded by transient periods of value coding, suggesting value-to-choice transitions and resembling decision processes in other brain systems. These findings suggest that the amygdala might play an active role in economic decisions. Current views of amygdala function should be extended to incorporate a role in decision-making beyond valuation.

Value, pleasure and choice in the ventral prefrontal cortex.

Rapid advances have recently been made in understanding how value-based decision-making processes are implemented in the brain. We integrate neuroeconomic and computational approaches with evidence on the neural correlates of value and experienced pleasure to describe how systems for valuation and decision-making are organized in the prefrontal cortex of humans and other primates. We show that the orbitofrontal and ventromedial prefrontal (VMPFC) cortices compute expected value, reward outcome and experienced pleasure for different stimuli on a common value scale. Attractor networks in VMPFC area 10 then implement categorical decision processes that transform value signals into a choice between the values, thereby guiding action. This synthesis of findings across fields provides a unifying perspective for the study of decision-making processes in the brain.