Shih-Wei Wu

Economic decision-making compared with an equivalent motor task

There is considerable evidence that human economic decision-making deviates from the predictions of expected utility theory (EUT) and that human performance conforms to EUT in many perceptual and motor decision tasks. It is possible that these results reflect a real difference in decision-making in the 2 domains but it is also possible that the observed discrepancy simply reflects typical differences in experimental design. We developed a motor task that is mathematically equivalent to choosing between lotteries and used it to compare how the same subject chose between classical economic lotteries and the same lotteries presented in equivalent motor form. In experiment 1, we found that subjects are more risk seeking in deciding between motor lotteries. In experiment 2, we used cumulative prospect theory to model choice and separately estimated the probability weighting functions and the value functions for each subject carrying out each task. We found no patterned differences in how subjects represented outcome value in the motor and the classical tasks. However, the probability weighting functions for motor and classical tasks were markedly and significantly different. Those for the classical task showed a typical tendency to overweight small probabilities and underweight large probabilities, and those for the motor task showed the opposite pattern of probability distortion. This outcome also accounts for the increased risk-seeking observed in the motor tasks of experiment 1. We conclude that the same subject distorts probability, but not value, differently in making identical decisions in motor and classical form.

Trading off speed and accuracy in rapid, goal-directed movements

Many studies have shown that humans face a trade-off between the speed and accuracy with which they can make movements. In this article, we asked whether humans choose movement time to maximize expected gain by taking into account their own speed-accuracy trade-off (SAT). We studied this question within the context of a rapid pointing task in which subjects received a reward for hitting a target on a monitor. The experimental design we used had two parts. First, we estimated individual trade-offs by motivating subjects to perform the pointing task under four different time constraints. Second, we tested whether subjects selected movement time optimally in an environment where they were rewarded for both speed and accuracy; the value of the target decreased linearly over time to zero. We ran two conditions in which the subjects faced different decay rates. Overall, the performance of 13 out of 16 subjects was indistinguishable from optimal. We concluded that in planning movements, humans take into account their own SAT to maximize expected gain.

Brain stimulation improves cognitive control by modulating medial-frontal activity and preSMA-vmPFC functional connectivity

Previous research has demonstrated that brain stimulation can improve inhibitory control. However, the neural mechanisms underlying such artificially induced improvement remain unclear. In this study, by coupling anodal transcranial direct current stimulation (atDCS) with functional MRI, we found that atDCS over preSMA effectively improved stopping speed, which was associated with increased BOLD response in the preSMA and ventromedial prefrontal cortex (vmPFC). Furthermore, such atDCS‐induced BOLD increase in vmPFC was positively correlated with participants’ improvement in stopping efficiency, and the functional connectivity between preSMA and vmPFC increased during successful stop. These results suggest that the rapid behavioral improvement from preSMA brain stimulation involves modulated medial‐frontal activity and preSMA‐vmPFC functional connectivity. Hum Brain Mapp 36:4004–4015, 2015.

Sub-Optimal allocation of time in sequential movements

The allocation of limited resources such as time or energy is a core problem that organisms face when planning complex actions. Most previous research concerning planning of movement has focused on the planning of single, isolated movements. Here we investigated the allocation of time in a pointing task where human subjects attempted to touch two targets in a specified order to earn monetary rewards. Subjects were required to complete both movements within a limited time but could freely allocate the available time between the movements. The time constraint presents an allocation problem to the subjects: the more time spent on one movement, the less time is available for the other. In different conditions we assigned different rewards to the two tokens. How the subject allocated time between movements affected their expected gain on each trial. We also varied the angle between the first and second movements and the length of the second movement. Based on our results, we developed and tested a model of speed-accuracy tradeoff for sequential movements. Using this model we could predict the time allocation that would maximize the expected gain of each subject in each experimental condition. We compared human performance with predicted optimal performance. We found that all subjects allocated time sub-optimally, spending more time than they should on the first movement even when the reward of the second target was five times larger than the first. We conclude that the movement planning system fails to maximize expected reward in planning sequences of as few as two movements and discuss possible interpretations drawn from economic theory.

Neural Mechanisms for Integrating Prior Knowledge and Likelihood in Value-Based Probabilistic Inference

In Bayesian decision theory, knowledge about the probabilities of possible outcomes is captured by a prior distribution and a likelihood function. The prior reflects past knowledge and the likelihood summarizes current sensory information. The two combined (integrated) form a posterior distribution that allows estimation of the probability of different possible outcomes. In this study, we investigated the neural mechanisms underlying Bayesian integration using a novel lottery decision task in which both prior knowledge and likelihood information about reward probability were systematically manipulated on a trial-by-trial basis. Consistent with Bayesian integration, as sample size increased, subjects tended to weigh likelihood information more compared with prior information. Using fMRI in humans, we found that the medial prefrontal cortex (mPFC) correlated with the mean of the posterior distribution, a statistic that reflects the integration of prior knowledge and likelihood of reward probability. Subsequent analysis revealed that both prior and likelihood information were represented in mPFC and that the neural representations of prior and likelihood in mPFC reflected changes in the behaviorally estimated weights assigned to these different sources of information in response to changes in the environment. Together, these results establish the role of mPFC in prior-likelihood integration and highlight its involvement in representing and integrating these distinct sources of information.

MOTOR DECISION-MAKING

How does the brain plan a movement, such as reaching for a cup of coffee, or make motor decisions, such as deciding whether to swing at an incoming baseball? How might the neural systems involved in this type of decisions differ from other types of decision-making, such as economic choice? In this article, we will review recent advances in these issues based on results from behavioral, computational modeling, electrophysiological, and neuroimaging studies. The first part of the article focuses on introducing behavioral paradigms used in studying movement planning and motor decision-making in the laboratory and the theoretical frameworks for modeling these behaviors. The second half of the article focuses on the neural and computational mechanisms underlying motor decision-making. In particular, we used key insights identified from the behavioral and modeling studies as a guide in elucidating these mechanisms and setting directions for future research.

Planning rapid movements to maximize gain in scenes with multiple regions carrying reward or penalty

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The neural correlates of subjective utility of monetary outcome and probability weight in economic and in motor decision under risk.

1 We previously[1,2] proposed a maximum expected gain model intended to predict how people plan movements in scenes where there are rewards and penalties associated with touching within overlapping colored circles. The configuration of circles is briefly presented at random locations and orientations on a touch screen. Repeated execution of a movement plan might lead to a distribution of end points whose mean end point (MEP) is the blue diamond. This plan would earn frequent

Planning multiple movements within a fixed time limit: the cost of constrained time allocation in a visuo-motor task.

1 rewards.