Masahiko Haruno

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Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and often uncertain environmental conditions. We previously proposed a new modular architecture, the modular selection and identification for control (MOSAIC) model, for motor learning and control based on multiple pairs of forward (predictor)(More)
To select appropriate behaviors leading to rewards, the brain needs to learn associations among sensory stimuli, selected behaviors, and rewards. Recent imaging and neural-recording studies have revealed that the dorsal striatum plays an important role in learning such stimulus-action-reward associations. However, the putamen and caudate nucleus are(More)
Humans can acquire appropriate behaviors that maximize rewards on a trial-and-error basis. Recent electrophysiological and imaging studies have demonstrated that neural activity in the midbrain and ventral striatum encodes the error of reward prediction. However, it is yet to be examined whether the striatum is the main locus of reward-based behavioral(More)
A fundamental challenge in social cognition is how humans learn another person's values to predict their decision-making behavior. This form of learning is often assumed to require simulation of the other by direct recruitment of one's own valuation process to model the other's process. However, the cognitive and neural mechanism of simulation learning is(More)
An important question in motor neuroscience is how the nervous system controls the spatiotemporal activation patterns of redundant muscles in generating accurate movements. The redundant muscles may not only underlie the flexibility of our movements but also pose the challenging problem of how to select a specific sequence of muscle activation from the huge(More)
'Social value orientation' characterizes individual differences in anchoring attitudes toward the division of resources. Here, by contrasting people with prosocial and individualistic orientations using functional magnetic resonance imaging, we demonstrate that degree of inequity aversion in prosocials is predictable from amygdala activity and unaffected by(More)
The intention behind another's action and the impact of the outcome are major determinants of human economic behavior. It is poorly understood, however, whether the two systems share a core neural computation. Here, we investigated whether the two systems are causally dissociable in the brain by integrating computational modeling, functional magnetic(More)
Midbrain dopamine neurons signal reward value, their prediction error, and the salience of events. If they play a critical role in achieving specific distant goals, long-term future rewards should also be encoded as suggested in reinforcement learning theories. Here, we address this experimentally untested issue. We recorded 185 dopamine neurons in three(More)
The brain's most difficult computation in decision-making learning is searching for essential information related to rewards among vast multimodal inputs and then integrating it into beneficial behaviors. Contextual cues consisting of limbic, cognitive, visual, auditory, somatosensory, and motor signals need to be associated with both rewards and actions by(More)