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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

Experimental Setup. Seated participants grasped the handle of a robotic arm and made 15 cm reaching movements from a start circle to a target circle. The forearm was supported by a cradle attached to the robotic arm. Visual feedback of the start, target, and cursor were provided on a monitor mounted at eye level. The location of the “cliff” was indicated by the line 2.5 cm to the right of the start and target circles.
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Figure 1: Experimental Setup. Seated participants grasped the handle of a robotic arm and made 15 cm reaching movements from a start circle to a target circle. The forearm was supported by a cradle attached to the robotic arm. Visual feedback of the start, target, and cursor were provided on a monitor mounted at eye level. The location of the “cliff” was indicated by the line 2.5 cm to the right of the start and target circles.

Mentions: To examine the influence of the subjective value of movement error on adaptation, we created a task in which we modulated the subjective value associated with a movement error of a given magnitude. Participants made reaching movements while holding the handle of a force-generating robot arm (Figure 1) in two novel dynamic environments that imposed different consequences on the same magnitude of movement error.


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

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

Experimental Setup. Seated participants grasped the handle of a robotic arm and made 15 cm reaching movements from a start circle to a target circle. The forearm was supported by a cradle attached to the robotic arm. Visual feedback of the start, target, and cursor were provided on a monitor mounted at eye level. The location of the “cliff” was indicated by the line 2.5 cm to the right of the start and target circles.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Experimental Setup. Seated participants grasped the handle of a robotic arm and made 15 cm reaching movements from a start circle to a target circle. The forearm was supported by a cradle attached to the robotic arm. Visual feedback of the start, target, and cursor were provided on a monitor mounted at eye level. The location of the “cliff” was indicated by the line 2.5 cm to the right of the start and target circles.
Mentions: To examine the influence of the subjective value of movement error on adaptation, we created a task in which we modulated the subjective value associated with a movement error of a given magnitude. Participants made reaching movements while holding the handle of a force-generating robot arm (Figure 1) in two novel dynamic environments that imposed different consequences on the same magnitude of movement error.

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