Limits...
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

Movement error vs. Gain. (A) Average movement errors across all participants, for each trial gain, are plotted for the Stable (blue) and Unstable (red) phases. (B) Average error in each phase (positive: rightward, negative: leftward). The Unstable phase had slightly greater leftward error. Asterisk indicates P < 0.05. Error bars represent standard error of the mean.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3750521&req=5

Figure 5: Movement error vs. Gain. (A) Average movement errors across all participants, for each trial gain, are plotted for the Stable (blue) and Unstable (red) phases. (B) Average error in each phase (positive: rightward, negative: leftward). The Unstable phase had slightly greater leftward error. Asterisk indicates P < 0.05. Error bars represent standard error of the mean.

Mentions: We began our analysis by examining the movement error for each gain. In both phases, rightward errors greater than 2.5 cm were rare. Trial movement error was grouped by gain into bins and separated by phase. The results of the linear mixed effects regression model indicated that there was a main effect of gain (P < 0.00001), and confirmed a linear relationship between movement error and gain in each phase. Stronger gains led to increasingly leftward errors, and weaker gains led to increasingly rightward errors (Figure 5A). These results support the use of gain as a proxy for error in adaptation analyses to come.


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

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

Movement error vs. Gain. (A) Average movement errors across all participants, for each trial gain, are plotted for the Stable (blue) and Unstable (red) phases. (B) Average error in each phase (positive: rightward, negative: leftward). The Unstable phase had slightly greater leftward error. Asterisk indicates P < 0.05. Error bars represent standard error of the mean.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Movement error vs. Gain. (A) Average movement errors across all participants, for each trial gain, are plotted for the Stable (blue) and Unstable (red) phases. (B) Average error in each phase (positive: rightward, negative: leftward). The Unstable phase had slightly greater leftward error. Asterisk indicates P < 0.05. Error bars represent standard error of the mean.
Mentions: We began our analysis by examining the movement error for each gain. In both phases, rightward errors greater than 2.5 cm were rare. Trial movement error was grouped by gain into bins and separated by phase. The results of the linear mixed effects regression model indicated that there was a main effect of gain (P < 0.00001), and confirmed a linear relationship between movement error and gain in each phase. Stronger gains led to increasingly leftward errors, and weaker gains led to increasingly rightward errors (Figure 5A). These results support the use of gain as a proxy for error in adaptation analyses to come.

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