Motivated for near impossibility: How task type and reward modulate task enjoyment and the striatal activation for extremely difficult task.

Economic and decision-making theories suppose that people would disengage from a task with near zero success probability, because this implicates little normative utility values. However, humans often are motivated for an extremely challenging task, even without any extrinsic incentives. The current study aimed to address the nature of this challenge-based motivation and its neural correlates. We found that, when participants played a skill-based task without extrinsic incentives, their task enjoyment increased as the chance of success decreased, even if the task was almost impossible to achieve. However, such challenge-based motivation was not observed when participants were rewarded for the task or the reward was determined in a probabilistic manner. The activation in the ventral striatum/pallidum tracked the pattern of task enjoyment. These results suggest that people are intrinsically motivated to challenge a nearly impossible task but only when the task requires certain skills and extrinsic rewards are unavailable.

A Decision-Theoretic Model of Behavior Change.

Undesirable habitual or addictive behaviors are often difficult to change. The issue of "behavior change" has long been studied in various research fields. Several models for behavior change have converged to the hypothesis that attitudes, norms, and self-efficacy are important determinants of intentions and behavior. To improve the accuracy of behavior-change models, some researchers have tried to combine behavioral economics models with existing models for behavior change. However, these attempts have failed because the existing models [e.g., Theory of Planned Behavior (TPB)] are not consistent with Expected Utility Theory (EUT), which underlies various behavioral economics models. In the present paper, we clarify the corresponding components between existing models for behavior change and EUT, and propose a new model, the Decision-Theoretic Model of behavior change (DTM), which is a natural extension of ordinary EUT.

A Biased Bayesian Inference for Decision-Making and Cognitive Control.

Although classical decision-making studies have assumed that subjects behave in a Bayes-optimal way, the sub-optimality that causes biases in decision-making is currently under debate. Here, we propose a synthesis based on exponentially-biased Bayesian inference, including various decision-making and probability judgments with different bias levels. We arrange three major parameter estimation methods in a two-dimensional bias parameter space (prior and likelihood), of the biased Bayesian inference. Then, we discuss a neural implementation of the biased Bayesian inference on the basis of changes in weights in neural connections, which we regarded as a combination of leaky/unstable neural integrator and probabilistic population coding. Finally, we discuss mechanisms of cognitive control which may regulate the bias levels.