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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

Adaptation vs. Movement Error. Adaptation for each trial was binned by the magnitude of the error experienced on that trial. Each data point represents the the average adaptation and the average error across participants for a given error bin, for the Stable (blue) and Unstable (red) phases. Asterisks indicate P < 0.05. Error bars indicate standard error of the mean.
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Figure 8: Adaptation vs. Movement Error. Adaptation for each trial was binned by the magnitude of the error experienced on that trial. Each data point represents the the average adaptation and the average error across participants for a given error bin, for the Stable (blue) and Unstable (red) phases. Asterisks indicate P < 0.05. Error bars indicate standard error of the mean.

Mentions: Finally, we performed an error-based analysis. Although the experimental design did not explicitly control for error, this analysis would provide confirmation, albeit inherently variable, that adaptation to a given error differed between phases. Despite the increased variability in the results, the analysis supported the findings of the gain-based analysis. Trials were sorted by the magnitude of movement error into 11 bins, each 0.5 cm wide, ranging from −3.0 cm through 1.5 cm. The same bins were used for all participants. Similar to the gain-based analysis, effect of bin and phase were observed, as well as a bin by phase interaction (P < 0.00005, P = 0.0363, and P = 0.0322, respectively). In the Unstable phase participants significantly reduced adaptation in response to the largest leftward movement errors (P = 0.0471; Figure 8). These findings are also in line with the predictions in Figure 3 in the case of reduced adaptation to leftward errors only. It is interesting that participants also slightly reduced adaptation in response to small rightward movement errors between 0.5 and 1.0 cm, although this was not significant when corrected for multiple comparisons (P = 0.0483; Figure 8). Thus, this error-based adaptation reveals weaker adaptation to leftward errors and also suggests that the reference line that defined whether an error was non-threatening (away from the cliff) or threatening (toward the cliff), may not have been strictly defined by a straight line toward between the home and the target. It is reasonable that participants may have considered small rightward errors as non-threatening. A slight rightward offset in the reference line could still lead to more leftward errors on average and trajectories that avoided the cliff, which is what was observed (Figure 5B). It would be interesting in future investigations to understand what factors drive this distinction.


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

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

Adaptation vs. Movement Error. Adaptation for each trial was binned by the magnitude of the error experienced on that trial. Each data point represents the the average adaptation and the average error across participants for a given error bin, for the Stable (blue) and Unstable (red) phases. Asterisks indicate P < 0.05. Error bars indicate standard error of the mean.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: Adaptation vs. Movement Error. Adaptation for each trial was binned by the magnitude of the error experienced on that trial. Each data point represents the the average adaptation and the average error across participants for a given error bin, for the Stable (blue) and Unstable (red) phases. Asterisks indicate P < 0.05. Error bars indicate standard error of the mean.
Mentions: Finally, we performed an error-based analysis. Although the experimental design did not explicitly control for error, this analysis would provide confirmation, albeit inherently variable, that adaptation to a given error differed between phases. Despite the increased variability in the results, the analysis supported the findings of the gain-based analysis. Trials were sorted by the magnitude of movement error into 11 bins, each 0.5 cm wide, ranging from −3.0 cm through 1.5 cm. The same bins were used for all participants. Similar to the gain-based analysis, effect of bin and phase were observed, as well as a bin by phase interaction (P < 0.00005, P = 0.0363, and P = 0.0322, respectively). In the Unstable phase participants significantly reduced adaptation in response to the largest leftward movement errors (P = 0.0471; Figure 8). These findings are also in line with the predictions in Figure 3 in the case of reduced adaptation to leftward errors only. It is interesting that participants also slightly reduced adaptation in response to small rightward movement errors between 0.5 and 1.0 cm, although this was not significant when corrected for multiple comparisons (P = 0.0483; Figure 8). Thus, this error-based adaptation reveals weaker adaptation to leftward errors and also suggests that the reference line that defined whether an error was non-threatening (away from the cliff) or threatening (toward the cliff), may not have been strictly defined by a straight line toward between the home and the target. It is reasonable that participants may have considered small rightward errors as non-threatening. A slight rightward offset in the reference line could still lead to more leftward errors on average and trajectories that avoided the cliff, which is what was observed (Figure 5B). It would be interesting in future investigations to understand what factors drive this distinction.

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