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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. Gain. (A) Average adaptation for all participants vs. gain is plotted for the Stable (blue) and Unstable (red) phases. For clarity, arrows are used to indicate gains resulting in increasingly leftward or rightward errors. (B) Slopes of the adaptation curves for all gains (left) and only gains −36 through 0 Ns/m (right). Asterisks indicate P < 0.05. Error bars represent standard error of the mean.
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Figure 6: Adaptation vs. Gain. (A) Average adaptation for all participants vs. gain is plotted for the Stable (blue) and Unstable (red) phases. For clarity, arrows are used to indicate gains resulting in increasingly leftward or rightward errors. (B) Slopes of the adaptation curves for all gains (left) and only gains −36 through 0 Ns/m (right). Asterisks indicate P < 0.05. Error bars represent standard error of the mean.

Mentions: In the Stable phase, the average adaptation plotted as a function of the gain on the previous trial displays a linear relationship (Figure 6A). The slope of this line, fit using a linear mixed effects regression model, was found to be significant (P < 0.00001, k = −2.24 m2/Ns). There was also a significant effect of gain on adaptation in the Unstable phase (P < 0.00001; Figure 6A). The slope of the adaptation curve for the Unstable phase (k = −1.72 cm2/Ns) was significantly different from that of the adaptation curve in the Stable phase (P = 0.0043; Figure 6B), indicating a significant effect of phase. The cliff by gain interaction term was also significant (P = 0.0306), indicating that the presence of instability led to greater differences in adaptation at some gains more than others. Indeed, the adaptation vs. gain relationship for the Unstable phase (Figure 6A) qualitatively displays a notable non-linearity at the strongest gain, but otherwise is similar to the adaptation observed in the Stable phase. To further quantify this difference, planned comparisons were performed and revealed a significant reduction in adaptation to the strongest gain (P = 0.005), and similar adaptation to all other gains (P > 0.05). This is in line with the model predictions presented in Figure 3. Specifically, similar results are prediction by a model exhibiting reduced adaptation to leftward errors only as the strongest gain led to the greatest leftward errors. We chose to quantify adaptation over the final 350 trials in the Unstable phase to maximize the data included in the analysis. Nevertheless, the changes observed between phases appear to be long-lasting; there was no difference in adaptation between the final 200 trials in the Unstable phase compared to the final 350 trials (P = 0.600, linear mixed effects regression).


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. Gain. (A) Average adaptation for all participants vs. gain is plotted for the Stable (blue) and Unstable (red) phases. For clarity, arrows are used to indicate gains resulting in increasingly leftward or rightward errors. (B) Slopes of the adaptation curves for all gains (left) and only gains −36 through 0 Ns/m (right). Asterisks indicate 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 6: Adaptation vs. Gain. (A) Average adaptation for all participants vs. gain is plotted for the Stable (blue) and Unstable (red) phases. For clarity, arrows are used to indicate gains resulting in increasingly leftward or rightward errors. (B) Slopes of the adaptation curves for all gains (left) and only gains −36 through 0 Ns/m (right). Asterisks indicate P < 0.05. Error bars represent standard error of the mean.
Mentions: In the Stable phase, the average adaptation plotted as a function of the gain on the previous trial displays a linear relationship (Figure 6A). The slope of this line, fit using a linear mixed effects regression model, was found to be significant (P < 0.00001, k = −2.24 m2/Ns). There was also a significant effect of gain on adaptation in the Unstable phase (P < 0.00001; Figure 6A). The slope of the adaptation curve for the Unstable phase (k = −1.72 cm2/Ns) was significantly different from that of the adaptation curve in the Stable phase (P = 0.0043; Figure 6B), indicating a significant effect of phase. The cliff by gain interaction term was also significant (P = 0.0306), indicating that the presence of instability led to greater differences in adaptation at some gains more than others. Indeed, the adaptation vs. gain relationship for the Unstable phase (Figure 6A) qualitatively displays a notable non-linearity at the strongest gain, but otherwise is similar to the adaptation observed in the Stable phase. To further quantify this difference, planned comparisons were performed and revealed a significant reduction in adaptation to the strongest gain (P = 0.005), and similar adaptation to all other gains (P > 0.05). This is in line with the model predictions presented in Figure 3. Specifically, similar results are prediction by a model exhibiting reduced adaptation to leftward errors only as the strongest gain led to the greatest leftward errors. We chose to quantify adaptation over the final 350 trials in the Unstable phase to maximize the data included in the analysis. Nevertheless, the changes observed between phases appear to be long-lasting; there was no difference in adaptation between the final 200 trials in the Unstable phase compared to the final 350 trials (P = 0.600, linear mixed effects regression).

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