Values encoded in orbitofrontal cortex are causally related to economic choices.

In the eighteenth century, Daniel Bernoulli, Adam Smith and Jeremy Bentham proposed that economic choices rely on the computation and comparison of subjective values1. This hypothesis continues to inform modern economic theory2 and research in behavioural economics3, but behavioural measures are ultimately not sufficient to verify the proposal4. Consistent with the hypothesis, when agents make choices, neurons in the orbitofrontal cortex (OFC) encode the subjective value of offered and chosen goods5. Value-encoding cells integrate multiple dimensions6-9, variability in the activity of each cell group correlates with variability in choices10,11 and the population dynamics suggests the formation of a decision12. However, it is unclear whether these neural processes are causally related to choices. More generally, the evidence linking economic choices to value signals in the brain13-15 remains correlational16. Here we show that neuronal activity in the OFC is causal to economic choices. We conducted two experiments using electrical stimulation in rhesus monkeys (Macaca mulatta). Low-current stimulation increased the subjective value of individual offers and thus predictably biased choices. Conversely, high-current stimulation disrupted both the computation and the comparison of subjective values, and thus increased choice variability. These results demonstrate a causal chain linking subjective values encoded in OFC to valuation and choice.

Partial Adaptation to the Value Range in the Macaque Orbitofrontal Cortex.

Values available for choice in different behavioral contexts can vary immensely. To compensate for this variability, neuronal circuits underlying economic decisions undergo adaptation. In orbitofrontal cortex (OFC), neurons encode the subjective value of offered and chosen goods in a quasilinear way. Previous experiments found that the gain of the encoding is lower when the value range is wider. However, the parameters OFC neurons adapted to remained unclear. Furthermore, previous studies did not examine additive changes in neuronal responses. Computational considerations indicate that these factors can directly impact choice behavior. Here we investigated how OFC neurons adapt to changes in the value range. We recorded from two male rhesus monkeys during a juice choice task. Each session was divided into two blocks of trials. In each block, juices were offered within a set range of values, and ranges changed between blocks. Across blocks, neuronal responses adapted to both the maximum and the minimum value, but only partially. As a result, the minimum neural activity was elevated in some value ranges relative to others. Through simulation of a linear decision model, we showed that increasing the minimum response increases choice variability, lowering the expected payoff. This effect is modulated by the balance between cells with positive and negative encoding. The presence of these two populations induces a non-monotonic relationship between the value range and choice efficacy, such that the expected payoff is highest for decisions in an intermediate value range.SIGNIFICANCE STATEMENT Economic decisions are thought to rely on the orbitofrontal cortex (OFC). The values available for choice vary enormously in different contexts. Previous work showed that neurons in OFC encode values in a linear way, and that the gain of encoding is inversely related to the range of available values. However, the specific parameters driving adaptation remained unclear. Here we show that OFC neurons adapt to both the maximum and minimum value in the current context. However, adaptation is partial, leading to contextual changes in the response offset. Interestingly, increasing the activity offset negatively affects choices in a simulated network. Partial adaptation may allow the circuit to maintain information about context value at the cost of slightly reduced payoff.

Neuronal variability in orbitofrontal cortex during economic decisions.

Neuroeconomic models assume that economic decisions are based on the activity of offer value cells in the orbitofrontal cortex (OFC), but testing this assertion has proven difficult. In principle, the decision made on a given trial should correlate with the stochastic fluctuations of these cells. However, this correlation, measured as a choice probability (CP), is small. Importantly, a neuron's CP reflects not only its individual contribution to the decision (termed readout weight), but also the intensity and the structure of correlated variability across the neuronal population (termed noise correlation). A precise mathematical relation between CPs, noise correlations, and readout weights was recently derived by Haefner and colleagues (Haefner RM, Gerwinn S, Macke JH, Bethge M. Nat Neurosci 16: 235-242, 2013) for a linear decision model. In this framework, concurrent measurements of noise correlations and CPs can provide quantitative information on how a population of cells contributes to a decision. Here we examined neuronal variability in the OFC of rhesus monkeys during economic decisions. Noise correlations had similar structure but considerably lower strength compared with those typically measured in sensory areas during perceptual decisions. In contrast, variability in the activity of individual cells was high and comparable to that recorded in other cortical regions. Simulation analyses based on Haefner's equation showed that noise correlations measured in the OFC combined with a plausible readout of offer value cells reproduced the experimental measures of CPs. In other words, the results obtained for noise correlations and those obtained for CPs taken together support the hypothesis that economic decisions are primarily based on the activity of offer value cells.

Unpacking self-ordered sequences.

In this issue of Neuron, Chiang et al. examine population coding of self-ordered sequences in prefrontal cortex. They find better decoding, more distributed information, and less variability when order is consistent. Consistent ordering produces reliable population response patterns that may aid planning and memory.

Genetic Control of Collective Behavior in Zebrafish.

Many animals, including humans, have evolved to live and move in groups. In humans, disrupted social interactions are a fundamental feature of many psychiatric disorders. However, we know little about how genes regulate social behavior. Zebrafish may serve as a powerful model to explore this question. By comparing the behavior of wild-type fish with 90 mutant lines, we show that mutations of genes associated with human psychiatric disorders can alter the collective behavior of adult zebrafish. We identify three categories of behavioral variation across mutants: "scattered," in which fish show reduced cohesion; "coordinated," in which fish swim more in aligned schools; and "huddled," in which fish form dense but disordered groups. Changes in individual interaction rules can explain these differences. This work demonstrates how emergent patterns in animal groups can be altered by genetic changes in individuals and establishes a framework for understanding the fundamentals of social information processing.

Orbitofrontal Cortex: A Neural Circuit for Economic Decisions.

Economic choice behavior entails the computation and comparison of subjective values. A central contribution of neuroeconomics has been to show that subjective values are represented explicitly at the neuronal level. With this result at hand, the field has increasingly focused on the difficult question of where in the brain and how exactly subjective values are compared to make a decision. Here, we review a broad range of experimental and theoretical results suggesting that good-based decisions are generated in a neural circuit within the orbitofrontal cortex (OFC). The main lines of evidence supporting this proposal include the fact that goal-directed behavior is specifically disrupted by OFC lesions, the fact that different groups of neurons in this area encode the input and the output of the decision process, the fact that activity fluctuations in each of these cell groups correlate with choice variability, and the fact that these groups of neurons are computationally sufficient to generate decisions. Results from other brain regions are consistent with the idea that good-based decisions take place in OFC and indicate that value signals inform a variety of mental functions. We also contrast the present proposal with other leading models for the neural mechanisms of economic decisions. Finally, we indicate open questions and suggest possible directions for future research.

Optimal coding and neuronal adaptation in economic decisions.

During economic decisions, offer value cells in orbitofrontal cortex (OFC) encode the values of offered goods. Furthermore, their tuning functions adapt to the range of values available in any given context. A fundamental and open question is whether range adaptation is behaviorally advantageous. Here we present a theory of optimal coding for economic decisions. We propose that the representation of offer values is optimal if it ensures maximal expected payoff. In this framework, we examine offer value cells in non-human primates. We show that their responses are quasi-linear even when optimal tuning functions are highly non-linear. Most importantly, we demonstrate that for linear tuning functions range adaptation maximizes the expected payoff. Thus value coding in OFC is functionally rigid (linear tuning) but parametrically plastic (range adaptation with optimal gain). Importantly, the benefit of range adaptation outweighs the cost of functional rigidity. While generally suboptimal, linear tuning may facilitate transitive choices.