On-Line Processing of Uncertain Information in Visuomotor Control

Abstract

Our sensory observations represent a delayed, noisy estimate of the environment. Delay causes instability and noise causes uncertainty. To deal with these problems, theory suggests that the processing of sensory information by the brain should be probabilistic: to start a movement or to alter it midflight, our brain should make predictions about the near future of sensory states and then continuously integrate the delayed sensory measures with predictions to form an estimate of the current state. To test the predictions of this theory, we asked participants to reach to the center of a blurry target. With increased uncertainty about the target, reach reaction times increased. Occasionally, we changed the position of the target or its blurriness during the reach. We found that the motor response to a given second target was influenced by the uncertainty about the first target. The specific trajectories of motor responses were consistent with predictions of a "minimum variance" state estimator. That is, the motor output that the brain programmed to start a reaching movement or correct it midflight was a continuous combination of two streams of information: a stream that predicted the near future of the state of the environment and a stream that provided a delayed measurement of that state.

Development of state estimation explains improvements in sensorimotor performance across childhood

“…Optimal dynamic state estimation is thought to be the result of combining state predictions with delayed sensory feedback (Gritsenko et al 2009;Izawa and Shadmehr 2008;Wolpert et al 1995), a process that is dependent on the posterior parietal cortex and cerebellum (Bastian 2006;Miall et al 2007;Miall and King 2008;Shadmehr and Krakauer 2008). Previous research has demonstrated structural changes in both the parietal cortex and cerebellum across the age range examined in the current study (Giedd et al 1999;Tiemeier et al 2010).…”

“…Thus predicting future states based on efference copies of motor commands can be used as an internal reference to circumvent processing delays, a finding that has been demonstrated in adults (Desmurget and Grafton 2000;Tseng et al 2007;Wagner and Smith 2008;Wolpert and Flanagan 2001). This prediction can be combined with sensory feedback to provide an up-to-date, online state estimate (i.e., dynamic state estimation) (Izawa and Shadmehr 2008;Vaziri et al 2006;Wolpert et al 1995). The development of dynamic state estimation across childhood has not, to our knowledge, been investigated.…”

Correction and suppression of reaching movements in the cerebral cortex: Physiological and neuropsychological aspects

“…After the discharge reaches a threshold, monkeys start the motor response. The hypothesis that the process of accumulating information must reach a threshold before initiation of action also accounts for the finding that human participants start their movements later when they are less certain of the exact location of the target (Izawa and Shadmehr, 2008).…”

“…An alternative view is that the new estimated target position is a weighted combination of the first and second targets. Izawa and Shadmehr (2008) tested this hypothesis by asking participants to reach toward a blurry target that occasionally jumped during the reach. Consistent with the probabilistic model, they found that the motor response to the second target was influenced by the uncertainty about the first target.…”

Sensitivity to prediction error in reach adaptation

“…However, experiments suggest that the brain alters sensitivity to error based on its uncertainty about its predictions relative to its uncertainty about observations (Burge et al 2008;Korenberg and Ghahramani 2002). For example, when visual feedback about the consequences of a movement is blurry, one is less likely to change their motor commands compared with when it is sharp (Izawa and Shadmehr 2008). Even when the quality of the sensory feedback is kept constant, the brain appears to modulate sensitivity to error as a function of error size.…”