Masayuki Matsumoto’s research while affiliated with University of Tsukuba and other places

Prospect theory, arguably the most prominent theory of choice, is an obvious candidate for neural valuation models. How the activity of individual neurons, a possible computational unit, reflects prospect theory remains unknown. Here, we show with theoretical accuracy equivalent to that of human neuroimaging studies that single-neuron activity in four core reward-related cortical and subcortical regions represents the subjective valuation of risky gambles in monkeys. The activity of individual neurons in monkeys passively viewing a lottery reflects the desirability of probabilistic rewards, parameterized as a multiplicative combination of a utility and probability weighting functions in the prospect theory framework. The diverse patterns of valuation signals were not localized but distributed throughout most parts of the reward circuitry. A network model aggregating these signals reliably reconstructed risk preferences and subjective probability perceptions revealed by the animals' choices. Thus, distributed neural coding explains the computation of subjective valuations under risk.

Non-human primates (NHPs) have been widely used as a species model in studies to understand higher brain functions in health and disease. These studies employ specifically designed behavioral tasks in which animal behavior is well-controlled, and record neuronal activity at high spatial and temporal resolutions while animals are performing the tasks. Here, we present a detailed procedure to conduct single-unit recording, which fulfils high spatial and temporal resolutions while macaque monkeys (i.e., widely used NHPs) perform behavioral tasks in a well-controlled manner. This procedure was used in our previous study to investigate the dynamics of neuronal activity during economic decision-making by the monkeys. Monkeys' behavior was quantitated by eye position tracking and button press/release detection. By inserting a microelectrode into the brain, with a grid system in reference to magnetic resonance imaging, we precisely recorded the brain regions. Our experimental system permits rigorous investigation of the link between neuronal activity and behavior.

Computation of expected values (i.e., probability × magnitude) seems to be a dynamic integrative process performed by the brain for efficient economic behavior. However, neural dynamics underlying this computation is largely unknown. Using lottery tasks in monkeys ( Macaca mulatta , male; Macaca fuscata , female), we examined (1) whether four core reward-related brain regions detect and integrate probability and magnitude cued by numerical symbols and (2) whether these brain regions have distinct dynamics in the integrative process. Extraction of the mechanistic structure of neural population signals demonstrated that expected value signals simultaneously arose in the central orbitofrontal cortex (cOFC; medial part of area 13) and ventral striatum (VS). Moreover, these signals were incredibly stable compared with weak and/or fluctuating signals in the dorsal striatum and medial OFC. Temporal dynamics of these stable expected value signals were unambiguously distinct: sharp and gradual signal evolutions in the cOFC and VS, respectively. These intimate dynamics suggest that the cOFC and VS compute the expected values with unique time constants, as distinct, partially overlapping processes. SIGNIFICANCE STATEMENT Our results differ from those of earlier studies suggesting that many reward-related regions in the brain signal probability and/or magnitude and provide a mechanistic structure for expected value computation employed in multiple neural populations. A central part of the orbitofrontal cortex (cOFC) and ventral striatum (VS) can simultaneously detect and integrate probability and magnitude into an expected value. Our empirical study on these neural population dynamics raises a possibility that the cOFC and VS cooperate on this computation with unique time constants as distinct, partially overlapping processes.

Appropriate actions are taken based on the values of future rewards. The phasic activity of midbrain dopamine neurons signals these values. Because reward values often change over time, even on a subsecond-by-subsecond basis, appropriate action selection requires continuous value monitoring. However, the phasic dopamine activity, which is sporadic and has a short duration, likely fails continuous monitoring. Here, we demonstrate a tonic firing mode of dopamine neurons that effectively tracks changing reward values. We recorded dopamine neuron activity in monkeys during a Pavlovian procedure in which the value of a cued reward gradually increased or decreased. Dopamine neurons tonically increased and decreased their activity as the reward value changed. This tonic activity was evoked more strongly by non-burst spikes than burst spikes producing a conventional phasic activity. Our findings suggest that dopamine neurons change their firing mode to effectively signal reward values, which could underlie action selection in changing environments.

When we make economic choices, the brain first evaluates available options and then decides whether to choose them. Midbrain dopamine neurons are known to reinforce economic choices through their signal evoked by outcomes after decisions are made. However, although critical internal processing is executed while decisions are being made, little is known about the role of dopamine neurons during this period. We found that dopamine neurons exhibited dynamically changing signals related to the internal processing while rhesus monkeys were making decisions. These neurons encoded the value of an option immediately after it was offered and then gradually changed their activity to represent the animal’s upcoming choice. Similar dynamics were observed in the orbitofrontal cortex, a center for economic decision-making, but the value-to-choice signal transition was completed earlier in dopamine neurons. Our findings suggest that dopamine neurons are a key component of the neural network that makes choices from values during ongoing decision-making processes.

Computation of expected values, i.e., probability times magnitude, seems to be a dynamic integrative process performed in the brain for efficient economic behavior. However, neural dynamics underlying this computation remain largely unknown. We examined (1) whether four core reward-related regions detect and integrate the probability and magnitude cued by numerical symbols and (2) whether these regions have different dynamics in the integrative process. Extractions of mechanistic structure of neural population signal demonstrated that expected-value signals simultaneously arose in central part of orbitofrontal cortex (cOFC, area 13m) and ventral striatum (VS). These expected-value signals were incredibly stable in contrast to weak and unstable signals in dorsal striatum and medial OFC. Notably, temporal dynamics of these stable expected-value signals were unambiguously distinct: sharp and gradual signal evolutions in cOFC and VS, respectively. These intimate dynamics suggest that cOFC and VS compute the expected-values with unique time constants, as distinct, partially overlapping processes.

Animals need to inhibit inappropriate actions that would lead to unwanted outcomes. Although this ability, called response inhibition, is impaired in neurological/psychiatric disorders with dopaminergic dysfunctions, how dopamine regulates response inhibition remains unclear. Here we investigated neuronal signals of the nigrostriatal dopamine system in monkeys performing a saccadic countermanding task. Subsets of dopamine neurons in the substantia nigra and striatal neurons receiving the dopaminergic input were activated when the monkey was required to cancel a planned saccadic eye movement. These activations were stronger when canceling the eye movements was successful compared with failed and were enhanced in demanding trials. The activated dopamine neurons were distributed mainly in the dorsolateral, but not in the ventromedial, part of the nigra. Furthermore, pharmacological blockade of dopaminergic neurotransmission in the striatum dampened the performance of canceling saccadic eye movements. The present findings indicate that disruption of nigrostriatal dopamine signaling causes impairments in response inhibition.