Shih-Wei Wu, PhD
Postdoctoral fellow
Division of Humanities and Social Sciences
California Institute of Technology
shihwei[at]caltech.edu
Click here for
my CV [pdf version]
We investigated how humans trade off between speed and accuracy in tasks where
the monetary gains for hitting the target drastically decreased over time.
To plan the movements optimally, the subjects not only had to take into account
the decreasing rewards, but the knowledge of his/her own speed-accuracy tradeoff.
We found that humans typically plan movement time that came close to maximizing
the expected gain. While subjects selected near-optimal performance, we also
observed slight but consistent slowness in their movement compared to optimal.
Dean,
M., Wu, S-W., & Maloney, L. T. (2007). Trading off speed and accuracy in
rapid, goal-directed movements. Journal of Vision, 7(5):10, 1-12.[pdf]
Wu,
S.-W., Trommershauser, J., Maloney, L. T., & Landy, M. S. (2006). Limits
to human movement planning in tasks with asymmetric gain landscapes. Journal
of Vision, 6, 53-63.[pdf]
Movement planning
under risk
It
has long been thought that the goal of movement planning is to minimize variability
or cost of the movements. While variability minimization is crucial, the costs
and benefits associted with actions is another important variable to consider.
How humans take into account sensorimotor uncertainty and the gain-loss structure
in the environment when planning movements and whether they can do so optimally
have been the focus of my research.
Research Interests
Movement planning, decision under risk, neuroeconomics
The
neural correlates of probability distortion

We
have shown that in motor tasks where explicit costs and benefits are implemented
the subjects essentially are choosing among lotteries (See Maloney et al.,
2007 for extended discussion). In other words, it is a form of decision
making under risk. What distinguishes motor from 'classical' decision task
is how the probability information is obtained. While probability is explicitly
given in classical lottery tasks, this is not the case in motor lottery
tasks. Facing a motor lottery, subjects have to infer probability by taking
into account his/her sensorimotor uncertainty.
When
facing equivlant tasks, how does the brain represent probability information
and use it to guide choice? In an ongoing study (with my advisor Larry Maloney
and Mauricio Delgado), we explicitly estimate the distortions of probability
in both motor lottery tasks and classical lottery tasks. Those estimates
would allow us to better map out the neural correlates involved in subjective
probability transformation. We seek to address whether the distortion or
transformation of probability information between different choice modalities
arises from a common neural system.
Last
updated: 5/2008.
This is a study where we implemented asymmetric loss structure to the environment.
One of the task configurations is shown here. In a rapid pointing task, subjects
earned 40 points if he hit within the green circle, lost 10 points for hitting
the blue, and lost 50 for hitting the red. The expected gain landscape (filled
contour) is superimposed. The maximum expected gain (organge dot) is located
within the intersection of the reward and one of the penalty regions.
Wu, S-W., Dal Martello, M. F. & Maloney, L. T. (under review). Suboptimal
tradeoff of time in sequential, visually guided movements.

(.27,$24;.60,$20)
Wu,
S-W., Delgado, M. R., & Maloney, L. T. (2009). Economic decision making under risk compared with an equivalent motor task. Proceedings of the National Academy of Sciences USA. [pdf] [supplement]
In this study, we compared people's decision in motor lottery tasks (where probability is implicit in subject's motor uncertainty) with their choices in economic lottery task (where probabilities are explicitly provided). We found that the way probability information is distorted is significantly different between the two: Subjects in economic task overweight small probabilities but underweight moderate to large probabilities as previous studies have shown. On the other hand, subjects overweight small probabilities and underweight large probabilities in the motor task. This result is consistent with the findings on probability distortion in experience-based decision making and further suggests that there might be a global feature of how probability information is distorted in decision from experience that is distinct from more traditional forms of decision making.

One of the sigatures of sensorimotor tasks is that it often rewards both speed
and accuracy. In a sequential pointing task where there are multiple rewarding
targets present and only a very limited time to attempt them, what should
the movement planner do? We proposed a simple model based on the intuition
that the planning problem is essentially a decision to allocate limited time
resource. The more time spent on attempting one target, the higher the probability
of hitting it but the less time left for the others. Based on the rewards
assigned to each target, the optimization problem is to divide the time such
that it maximizes expected reward. Contrary to the near optimal performance
in motor tasks we have reported in earlier papers, subjects were clearly suboptimal
in allocating time in the sequential task where they often spent too much
time than predicted on the first target they hit, even when its reward was much smaller
than the later targets.
Neural representation
of probability information in decision under risk
The
proposal of prospect theory (Kahneman & Tversky, 1979) marked a major
theoretical attempt to incoporate empirical regularities of choice inconsistent
with standard economic theory. I am particularly interested in the distortion
of probability information, which in prospect theory was characterized by
a probability weighting function.
Animation of the sequential task
Probability
distortion under gain and loss scenarios
Probably
the most elegant generalization of prospect theory is how the value function
and the probability weighting function together predict the fourfold risk
attitudes. My interest in this topic is how the brain transforms probability
information under gain and loss scenarios. Is probability transformation governed
by a single neural system that is independent of reward information? This
work is under the guidance of Larry Maloney and Paul Glimcher.