@article {Tassinari10154,
author = {Tassinari, Hadley and Hudson, Todd E. and Landy, Michael S.},
title = {Combining Priors and Noisy Visual Cues in a Rapid Pointing Task},
volume = {26},
number = {40},
pages = {10154--10163},
year = {2006},
doi = {10.1523/JNEUROSCI.2779-06.2006},
publisher = {Society for Neuroscience},
abstract = {Statistical decision theory suggests that choosing an ideal action requires taking several factors into account: (1) prior knowledge of the probability of various world states, (2) sensory information concerning the world state, (3) the probability of outcomes given a choice of action, and (4) the loss or gain associated with those outcomes. In previous work, we found that, in many circumstances, humans act like ideal decision makers in planning a reaching movement. They select a movement aim point that maximizes expected gain, thus taking into account outcome uncertainty (motor noise) and the consequences of their actions. Here, we ask whether humans can optimally combine prior knowledge and uncertain sensory information in planning a reach. Subjects rapidly pointed at unseen targets, indicated with dots drawn from a distribution centered on the invisible target location. Target location had a prior distribution, the form of which was known to the subject. We varied the number of dots and hence target spatial uncertainty. An analysis of the sources of uncertainty impacting performance in this task indicated that the optimal strategy was to aim between the mean of the prior (the screen center) and the mean stimulus location (centroid of the dot cloud). With increased target location uncertainty, the aim point should have moved closer to the prior. Subjects used near-optimal strategies, combining stimulus uncertainty and prior information appropriately. Observer behavior was well modeled as having three additional sources of inefficiency originating in the motor system, calculation of centroid location, and calculation of aim points.},
issn = {0270-6474},
URL = {https://www.jneurosci.org/content/26/40/10154},
eprint = {https://www.jneurosci.org/content/26/40/10154.full.pdf},
journal = {Journal of Neuroscience}
}