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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

Control Experiment. (A) Movement error vs. Gain. Error from the Early Stable phase is shown in blue, while error from the Late Stable phase is shown in red. There was no difference in movement errors between phases. (B) Adaptation vs. Gain. Adaptation for all participants vs. gain for the Early Stable phase and Late Stable phase. No significant changes in adaptation were found to any gain. Error bars represent standard error of the mean.
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Figure 10: Control Experiment. (A) Movement error vs. Gain. Error from the Early Stable phase is shown in blue, while error from the Late Stable phase is shown in red. There was no difference in movement errors between phases. (B) Adaptation vs. Gain. Adaptation for all participants vs. gain for the Early Stable phase and Late Stable phase. No significant changes in adaptation were found to any gain. Error bars represent standard error of the mean.

Mentions: To ensure that the changes in adaptation between the phases were not simply the result of prolonged exposure to the viscous curl field, a control experiment was conducted in which participants made 650 reaching movements without the presence of the unstable cliff region. Similar to the main experiment, we compared movement error and gain-based behavioral adaptation between phases. These data were also analyzed using the linear mixed effects regression model with gain and phase included as factors, and a gain by phase interaction term. As expected, there was a main effect of gain in both the error and adaptation analyses (both P's < 0.00001). We found there was no difference in movement error between the Early Stable Phase and the Late Stable Phase (Early Stable: −0.43 ± 0.96; Late Stable: −0.44 ± 0.78 cm; P = 0.872; Figure 10A). Similarly, no difference in adaptation was found between the Early Stable and Late Stable Phases in the control experiment (P = 0.5576; Figure 10B). It is also important to note the consistency in the magnitude of adaptation observed in response the largest gain in the Control experiment (Figure 10B: gain = −40 Ns/m), where the environment is always Stable, and the adaptation to the largest gain observed in the Stable phase of the main experiment (Figure 6A: gain = −40 Ns/m). This strengthens our finding that adaptation to the largest gain was indeed reduced in the Unstable phase of the Main experiment.


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

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

Control Experiment. (A) Movement error vs. Gain. Error from the Early Stable phase is shown in blue, while error from the Late Stable phase is shown in red. There was no difference in movement errors between phases. (B) Adaptation vs. Gain. Adaptation for all participants vs. gain for the Early Stable phase and Late Stable phase. No significant changes in adaptation were found to any gain. 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 10: Control Experiment. (A) Movement error vs. Gain. Error from the Early Stable phase is shown in blue, while error from the Late Stable phase is shown in red. There was no difference in movement errors between phases. (B) Adaptation vs. Gain. Adaptation for all participants vs. gain for the Early Stable phase and Late Stable phase. No significant changes in adaptation were found to any gain. Error bars represent standard error of the mean.
Mentions: To ensure that the changes in adaptation between the phases were not simply the result of prolonged exposure to the viscous curl field, a control experiment was conducted in which participants made 650 reaching movements without the presence of the unstable cliff region. Similar to the main experiment, we compared movement error and gain-based behavioral adaptation between phases. These data were also analyzed using the linear mixed effects regression model with gain and phase included as factors, and a gain by phase interaction term. As expected, there was a main effect of gain in both the error and adaptation analyses (both P's < 0.00001). We found there was no difference in movement error between the Early Stable Phase and the Late Stable Phase (Early Stable: −0.43 ± 0.96; Late Stable: −0.44 ± 0.78 cm; P = 0.872; Figure 10A). Similarly, no difference in adaptation was found between the Early Stable and Late Stable Phases in the control experiment (P = 0.5576; Figure 10B). It is also important to note the consistency in the magnitude of adaptation observed in response the largest gain in the Control experiment (Figure 10B: gain = −40 Ns/m), where the environment is always Stable, and the adaptation to the largest gain observed in the Stable phase of the main experiment (Figure 6A: gain = −40 Ns/m). This strengthens our finding that adaptation to the largest gain was indeed reduced in the Unstable phase of the Main experiment.

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