Social risk coding by amygdala activity and connectivity with dorsal anterior cingulate cortex.

Risk is a fundamental factor affecting individual and social economic decisions, but its neural correlates are largely unexplored in the social domain. The amygdala, together with the dorsal anterior cingulate cortex (dACC), is thought to play a central role in risk taking. Here, we investigated in human volunteers (n=20; 11 females) how risk (defined as variance of reward probability distributions) in a social situation affects decisions and concomitant neural activity as measured with fMRI.

A Neural Mechanism in the Human Orbitofrontal Cortex for Preferring High-Fat Foods Based on Oral Texture.

Although overconsumption of high-fat foods is a major driver of weight gain, the neural mechanisms that link the oral sensory properties of dietary fat to reward valuation and eating behavior remain unclear. Here we combine novel food-engineering approaches with functional neuroimaging to show that the human orbitofrontal cortex (OFC) translates oral sensations evoked by high-fat foods into subjective economic valuations that guide eating behavior. Male and female volunteers sampled and evaluated nutrient-controlled liquid foods that varied in fat and sugar ("milkshakes").

A view-based decision mechanism for rewards in the primate amygdala.

Primates make decisions visually by shifting their view from one object to the next, comparing values between objects, and choosing the best reward, even before acting. Here, we show that when monkeys make value-guided choices, amygdala neurons encode their decisions in an abstract, purely internal representation defined by the monkey's current view but not by specific object or reward properties. Across amygdala subdivisions, recorded activity patterns evolved gradually from an object-specific value code to a transient, object-independent code in which currently viewed and last-viewed objects competed to reflect the emerging view-based choice.

Mechanisms of adjustments to different types of uncertainty in the reward environment across mice and monkeys.

Despite being unpredictable and uncertain, reward environments often exhibit certain regularities, and animals navigating these environments try to detect and utilize such regularities to adapt their behavior. However, successful learning requires that animals also adjust to uncertainty associated with those regularities. Here, we analyzed choice data from two comparable dynamic foraging tasks in mice and monkeys to investigate mechanisms underlying adjustments to different types of uncertainty.

Nutrient-Sensitive Reinforcement Learning in Monkeys.

In reinforcement learning (RL), animals choose by assigning values to options and learn by updating these values from reward outcomes. This framework has been instrumental in identifying fundamental learning variables and their neuronal implementations. However, canonical RL models do not explain how reward values are constructed from biologically critical intrinsic reward components, such as nutrients.

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.

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.

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).

Nonhuman Primates Satisfy Utility Maximization in Compliance with the Continuity Axiom of Expected Utility Theory.

Expected Utility Theory (EUT), the first axiomatic theory of risky choice, describes choices as a utility maximization process: decision makers assign a subjective value (utility) to each choice option and choose the one with the highest utility. The continuity axiom, central to Expected Utility Theory and its modifications, is a necessary and sufficient condition for the definition of numerical utilities. The axiom requires decision makers to be indifferent between a gamble and a specific probabilistic combination of a more preferred and a less preferred gamble.

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.

Neural activity in human ventromedial prefrontal cortex reflecting the intention to save reward.

Saving behavior usually requires individuals to perform several consecutive choices before collecting the final reward. The overt behavior is preceded by an intention to perform an appropriate choice sequence. We studied saving sequences for which each participant rated the intention numerically as willingness to save.

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.

Neural Mechanisms for Accepting and Rejecting Artificial Social Partners in the Uncanny Valley.

Artificial agents are becoming prevalent across human life domains. However, the neural mechanisms underlying human responses to these new, artificial social partners remain unclear. The uncanny valley (UV) hypothesis predicts that humans prefer anthropomorphic agents but reject them if they become too humanlike-the so-called UV reaction.

Primate Amygdala Neurons Simulate Decision Processes of Social Partners.

By observing their social partners, primates learn about reward values of objects. Here, we show that monkeys' amygdala neurons derive object values from observation and use these values to simulate a partner monkey's decision process. While monkeys alternated making reward-based choices, amygdala neurons encoded object-specific values learned from observation.

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

This corrects the article DOI: 10.1038/ncomms12554.

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.

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.

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.

Planning activity for internally generated reward goals in monkey amygdala neurons.

The best rewards are often distant and can only be achieved by planning and decision-making over several steps. We designed a multi-step choice task in which monkeys followed internal plans to save rewards toward self-defined goals. During this self-controlled behavior, amygdala neurons showed future-oriented activity that reflected the animal's plan to obtain specific rewards several trials ahead.

Attention-dependent modulation of cortical taste circuits revealed by Granger causality with signal-dependent noise.

We show, for the first time, that in cortical areas, for example the insular, orbitofrontal, and lateral prefrontal cortex, there is signal-dependent noise in the fMRI blood-oxygen level dependent (BOLD) time series, with the variance of the noise increasing approximately linearly with the square of the signal. Classical Granger causal models are based on autoregressive models with time invariant covariance structure, and thus do not take this signal-dependent noise into account. To address this limitation, here we describe a Granger causal model with signal-dependent noise, and a novel, likelihood ratio test for causal inferences.

The representation of oral fat texture in the human somatosensory cortex.

How fat is sensed in the mouth and represented in the brain is important in relation to the pleasantness of food, appetite control, and the design of foods that reproduce the mouthfeel of fat yet have low energy content. We show that the human somatosensory cortex (SSC) is involved in oral fat processing via functional coupling to the orbitofrontal cortex (OFC), where the pleasantness of fat texture is represented. Using functional MRI, we found that activity in SSC was more strongly correlated with the OFC during the consumption of a high fat food with a pleasant (vanilla) flavor compared to a low fat food with the same flavor.

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.

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.

Componential Granger causality, and its application to identifying the source and mechanisms of the top-down biased activation that controls attention to affective vs sensory processing.

We describe a new measure of Granger causality, componential Granger causality, and show how it can be applied to the identification of the directionality of influences between brain areas with functional neuroimaging data. Componential Granger causality measures the effect of y on x, but allows interaction effects between y and x to be measured. In addition, the terms in componential Granger causality sum to 1, allowing causal effects to be directly compared between systems.