A neural correlate of reward-based behavioral learning in caudate nucleus: a functional magnetic resonance imaging study of a stochastic decision task.

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 learning. To address this, we conducted functional magnetic resonance imaging (fMRI) of a stochastic decision task involving monetary rewards, in which subjects had to learn behaviors involving different task difficulties that were controlled by probability. We performed a correlation analysis of fMRI data by using the explanatory variables derived from subject behaviors. We found that activity in the caudate nucleus was correlated with short-term reward and, furthermore, paralleled the magnitude of a subject's behavioral change during learning. In addition, we confirmed that this parallelism between learning and activity in the caudate nucleus is robustly maintained even when we vary task difficulty by controlling the probability. These findings suggest that the caudate nucleus is one of the main loci for reward-based behavioral learning.

Functional magnetic resonance imaging examination of two modular architectures for switching multiple internal models.

An internal model is a neural mechanism that can mimic the input-output properties of a controlled object such as a tool. Recent research interests have moved on to how multiple internal models are learned and switched under a given context of behavior. Two representative computational models for task switching propose distinct neural mechanisms, thus predicting different brain activity patterns in the switching of internal models. In one model, called the mixture-of-experts architecture, switching is commanded by a single executive called a "gating network," which is different from the internal models. In the other model, called the MOSAIC (MOdular Selection And Identification for Control), the internal models themselves play crucial roles in switching. Consequently, the mixture-of-experts model predicts that neural activities related to switching and internal models can be temporally and spatially segregated, whereas the MOSAIC model predicts that they are closely intermingled. Here, we directly examined the two predictions by analyzing functional magnetic resonance imaging activities during the switching of one common tool (an ordinary computer mouse) and two novel tools: a rotated mouse, the cursor of which appears in a rotated position, and a velocity mouse, the cursor velocity of which is proportional to the mouse position. The switching and internal model activities temporally and spatially overlapped each other in the cerebellum and in the parietal cortex, whereas the overlap was very small in the frontal cortex. These results suggest that switching mechanisms in the frontal cortex can be explained by the mixture-of-experts architecture, whereas those in the cerebellum and the parietal cortex are explained by the MOSAIC model.

Modular organization of internal models of tools in the human cerebellum.

Human capabilities in manipulating many different tools with dexterity suggest modular neural organization at functional levels, but anatomical modularity underlying the capabilities has yet to be demonstrated. Although modularity in phylogenetically older parts of the cerebellum is well known, comparable modularity in the lateral cerebellum for cognitive functions remains unknown. We investigated these issues by functional MRI (fMRI) based on our previous findings of a cerebellar internal model of a tool. After subjects intensively learned to manipulate two novel tools (the rotated mouse whose cursor appeared at a rotated position, and the velocity mouse whose cursor velocity was proportional to the mouse position), they could easily switch between the two. The lateral and posterior cerebellar activities for the two different tools were spatially segregated, and their overlaps were <10%, even at low statistical thresholds. Activities of the rotated mouse were more anterior and lateral than the velocity mouse activities. These results were consistent with predictions by the MOdular Selection And Identification Controller (MOSAIC) model that multiple internal models compete to partition sensory-motor experiences and their outputs are linearly combined for a particular context.

Internal forward models in the cerebellum: fMRI study on grip force and load force coupling.

Internal models are neural mechanisms that can mimic the input-output or output-input properties of the motor apparatus and external objects. Forward internal models predict sensory consequences from efference copies of motor commands. There is growing acceptance of the idea that forward models are important in sensorimotor integration as well as in higher cognitive function, but their anatomical loci and neural mechanisms are still largely unknown. Some of the most convincing evidence that the central nervous system (CNS) makes use of forward models in sensory motor control comes from studies on grip force-load force coupling. We first present a brief review of recent computational and behavioral studies that provide decisive evidence for the utilization of forward models in grip force-load force coupling tasks. Then, we used functional magnetic resonance imaging (fMRI) to measure the brain activity related to this coupling and demonstrate that the cerebellum is the most likely site for forward models to be stored.