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Learning from the value of your mistakes: evidence for a risk-sensitive process in movement adaptation.

Trent MC, Ahmed AA - Front Comput Neurosci (2013)

Bottom Line: We found that adaptation indeed differed.Specifically, in the Unstable environment, we observed reduced adaptation to leftward errors, an appropriate strategy that reduced the chance of a penalizing rightward error.These results demonstrate that adaptation is influenced by the subjective value of error, rather than solely the magnitude of error, and therefore is risk-sensitive.

View Article: PubMed Central - PubMed

Affiliation: Neuromechanics Laboratory, Department of Integrative Physiology, University of Colorado, Boulder Boulder, CO, USA.

ABSTRACT
Risk frames nearly every decision we make. Yet, remarkably little is known about whether risk influences how we learn new movements. Risk-sensitivity can emerge when there is a distortion between the absolute magnitude (actual value) and how much an individual values (subjective value) a given outcome. In movement, this translates to the difference between a given movement error and its consequences. Surprisingly, how movement learning can be influenced by the consequences associated with an error is not well-understood. It is traditionally assumed that all errors are created equal, i.e., that adaptation is proportional to an error experienced. However, not all movement errors of a given magnitude have the same subjective value. Here we examined whether the subjective value of error influenced how participants adapted their control from movement to movement. Seated human participants grasped the handle of a force-generating robotic arm and made horizontal reaching movements in two novel dynamic environments that penalized errors of the same magnitude differently, changing the subjective value of the errors. We expected that adaptation in response to errors of the same magnitude would differ between these environments. In the first environment, Stable, errors were not penalized. In the second environment, Unstable, rightward errors were penalized with the threat of unstable, cliff-like forces. We found that adaptation indeed differed. Specifically, in the Unstable environment, we observed reduced adaptation to leftward errors, an appropriate strategy that reduced the chance of a penalizing rightward error. These results demonstrate that adaptation is influenced by the subjective value of error, rather than solely the magnitude of error, and therefore is risk-sensitive. In other words, we may not simply learn from our mistakes, we may also learn from the value of our mistakes.

No MeSH data available.


Related in: MedlinePlus

Average Hand Paths. Average hand paths across all participants to identical gains in the Stable (solid lines) and Unstable (dashed lines) phases. Participants reached from the start (green) circle to the target (red) circle. Movement errors were sampled 5 cm into the movement (horizontal black line). The unstable cliff region is depicted in red, right of center.
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Figure 4: Average Hand Paths. Average hand paths across all participants to identical gains in the Stable (solid lines) and Unstable (dashed lines) phases. Participants reached from the start (green) circle to the target (red) circle. Movement errors were sampled 5 cm into the movement (horizontal black line). The unstable cliff region is depicted in red, right of center.

Mentions: Robot handle position, handle velocity, and robot generated force were recorded at 200 Hz. Movement error was calculated as the perpendicular displacement from a line connecting the start and target circles. Movement error was measured early in the reaching movement (5 cm in the y-direction from the center of the home circle, Figure 4), in an effort to capture the error due to feedforward control based on the previous trial, and not the reactive control employed to counter the perturbation on that trial. To quantify the average movement error experienced in response to each gain, the error from each trial was grouped with trials of the same gain during each phase.


Learning from the value of your mistakes: evidence for a risk-sensitive process in movement adaptation.

Trent MC, Ahmed AA - Front Comput Neurosci (2013)

Average Hand Paths. Average hand paths across all participants to identical gains in the Stable (solid lines) and Unstable (dashed lines) phases. Participants reached from the start (green) circle to the target (red) circle. Movement errors were sampled 5 cm into the movement (horizontal black line). The unstable cliff region is depicted in red, right of center.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750521&req=5

Figure 4: Average Hand Paths. Average hand paths across all participants to identical gains in the Stable (solid lines) and Unstable (dashed lines) phases. Participants reached from the start (green) circle to the target (red) circle. Movement errors were sampled 5 cm into the movement (horizontal black line). The unstable cliff region is depicted in red, right of center.
Mentions: Robot handle position, handle velocity, and robot generated force were recorded at 200 Hz. Movement error was calculated as the perpendicular displacement from a line connecting the start and target circles. Movement error was measured early in the reaching movement (5 cm in the y-direction from the center of the home circle, Figure 4), in an effort to capture the error due to feedforward control based on the previous trial, and not the reactive control employed to counter the perturbation on that trial. To quantify the average movement error experienced in response to each gain, the error from each trial was grouped with trials of the same gain during each phase.

Bottom Line: We found that adaptation indeed differed.Specifically, in the Unstable environment, we observed reduced adaptation to leftward errors, an appropriate strategy that reduced the chance of a penalizing rightward error.These results demonstrate that adaptation is influenced by the subjective value of error, rather than solely the magnitude of error, and therefore is risk-sensitive.

View Article: PubMed Central - PubMed

Affiliation: Neuromechanics Laboratory, Department of Integrative Physiology, University of Colorado, Boulder Boulder, CO, USA.

ABSTRACT
Risk frames nearly every decision we make. Yet, remarkably little is known about whether risk influences how we learn new movements. Risk-sensitivity can emerge when there is a distortion between the absolute magnitude (actual value) and how much an individual values (subjective value) a given outcome. In movement, this translates to the difference between a given movement error and its consequences. Surprisingly, how movement learning can be influenced by the consequences associated with an error is not well-understood. It is traditionally assumed that all errors are created equal, i.e., that adaptation is proportional to an error experienced. However, not all movement errors of a given magnitude have the same subjective value. Here we examined whether the subjective value of error influenced how participants adapted their control from movement to movement. Seated human participants grasped the handle of a force-generating robotic arm and made horizontal reaching movements in two novel dynamic environments that penalized errors of the same magnitude differently, changing the subjective value of the errors. We expected that adaptation in response to errors of the same magnitude would differ between these environments. In the first environment, Stable, errors were not penalized. In the second environment, Unstable, rightward errors were penalized with the threat of unstable, cliff-like forces. We found that adaptation indeed differed. Specifically, in the Unstable environment, we observed reduced adaptation to leftward errors, an appropriate strategy that reduced the chance of a penalizing rightward error. These results demonstrate that adaptation is influenced by the subjective value of error, rather than solely the magnitude of error, and therefore is risk-sensitive. In other words, we may not simply learn from our mistakes, we may also learn from the value of our mistakes.

No MeSH data available.


Related in: MedlinePlus