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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 Protocol. (A) Illustration of the forces produced by the robot during reaching. Leftward arrows (filled) in both panels represent the viscous curl field forces. Rightward (empty) arrows in the rightmost panel represent position dependent divergent forces. (B) The experiment consisted of four phases: Baseline (50 no-force trials), Stable (200 trials with changing curl-field dynamics), Unstable (400 trials with changing curl-field dynamics identical to the Stable trials, but with penalties associated with large rightward movement errors), and Washout (50 no-force trials).
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Figure 2: Experimental Protocol. (A) Illustration of the forces produced by the robot during reaching. Leftward arrows (filled) in both panels represent the viscous curl field forces. Rightward (empty) arrows in the rightmost panel represent position dependent divergent forces. (B) The experiment consisted of four phases: Baseline (50 no-force trials), Stable (200 trials with changing curl-field dynamics), Unstable (400 trials with changing curl-field dynamics identical to the Stable trials, but with penalties associated with large rightward movement errors), and Washout (50 no-force trials).

Mentions: In the second dynamic environment, Unstable, movement errors in the right half of the screen were heavily penalized, thereby altering the subjective value of an error of a given magnitude relative to the Stable environment. The Unstable environment was identical to the Stable environment, except that we imposed a boundary on the right side of the screen, simulating a virtual cliff. Errors to the right of this boundary would lead to instability: large rightward perturbing forces that participants could not compensate for within that trial (Figure 2). On trials with such an error, it was not possible for the participants to reach the target after the cursor had crossed the boundary. Here rightward errors were considerably less desirable than leftward errors. Compared to the Stable environment, a rightward error of the same magnitude in the Unstable environment had a less desirable consequence, and therefore a different subjective value.


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 Protocol. (A) Illustration of the forces produced by the robot during reaching. Leftward arrows (filled) in both panels represent the viscous curl field forces. Rightward (empty) arrows in the rightmost panel represent position dependent divergent forces. (B) The experiment consisted of four phases: Baseline (50 no-force trials), Stable (200 trials with changing curl-field dynamics), Unstable (400 trials with changing curl-field dynamics identical to the Stable trials, but with penalties associated with large rightward movement errors), and Washout (50 no-force trials).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Experimental Protocol. (A) Illustration of the forces produced by the robot during reaching. Leftward arrows (filled) in both panels represent the viscous curl field forces. Rightward (empty) arrows in the rightmost panel represent position dependent divergent forces. (B) The experiment consisted of four phases: Baseline (50 no-force trials), Stable (200 trials with changing curl-field dynamics), Unstable (400 trials with changing curl-field dynamics identical to the Stable trials, but with penalties associated with large rightward movement errors), and Washout (50 no-force trials).
Mentions: In the second dynamic environment, Unstable, movement errors in the right half of the screen were heavily penalized, thereby altering the subjective value of an error of a given magnitude relative to the Stable environment. The Unstable environment was identical to the Stable environment, except that we imposed a boundary on the right side of the screen, simulating a virtual cliff. Errors to the right of this boundary would lead to instability: large rightward perturbing forces that participants could not compensate for within that trial (Figure 2). On trials with such an error, it was not possible for the participants to reach the target after the cursor had crossed the boundary. Here rightward errors were considerably less desirable than leftward errors. Compared to the Stable environment, a rightward error of the same magnitude in the Unstable environment had a less desirable consequence, and therefore a different subjective value.

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