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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

Reverse Experiment. Adaptation vs. Gain. 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. Asterisks indicate P < 0.05. Error bars represent standard error of the mean.
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Figure 9: Reverse Experiment. Adaptation vs. Gain. 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. Asterisks indicate P < 0.05. Error bars represent standard error of the mean.

Mentions: In the main experiment, the cliff was located to right, increasing the subjective value of rightward errors compared with leftward errors. In the Reverse experiment, we reflected the cliff location so that leftward errors had a greater subjective value than rightward errors. As in the main experiment, we compared both movement error and adaptation between the Stable and Unstable phases. As expected, there was a main effect of gain in both the error and adaptation analyses (both P's < 0.00001). Similar to the main experiment, movement error differed significantly between Stable and Unstable phases (Stable: 0.89 ± 0.96 cm; Unstable: 1.05 ± 1.22 cm; P = 0.0209), Movement errors were more rightward in the Unstable phase indicative of a desire to avoid the cliff. Turning to the adaptation results, there was a significant phase by gain interaction (P = 0.0121). We observed that adaptation to the strongest rightward gains, leading to the largest rightward errors, was significantly reduced in the Unstable phase compared with the Stable phase (Figure 9). Specifically, a planned comparison revealed that adaptation to the strongest rightward gain of B = 40 Ns/m was significantly reduced in the Unstable compared with the Stable phase (P = 0.016). There was also a trend toward reduced adaptation to the gain of B = 28 Ns/m, however this was not significant when corrected for multiple comparisons (P = 0.011). Such reduced adaptation to the strongest rightward gain is in direct contrast to the findings of the main experiment where adaptation to the strongest leftward gains, leading to the largest leftward errors, was reduced. However, this reversal is precisely what we expected to occur given that the location of the cliff and subjective value of the movement error was also reversed.


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

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

Reverse Experiment. Adaptation vs. Gain. 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. 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 9: Reverse Experiment. Adaptation vs. Gain. 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. Asterisks indicate P < 0.05. Error bars represent standard error of the mean.
Mentions: In the main experiment, the cliff was located to right, increasing the subjective value of rightward errors compared with leftward errors. In the Reverse experiment, we reflected the cliff location so that leftward errors had a greater subjective value than rightward errors. As in the main experiment, we compared both movement error and adaptation between the Stable and Unstable phases. As expected, there was a main effect of gain in both the error and adaptation analyses (both P's < 0.00001). Similar to the main experiment, movement error differed significantly between Stable and Unstable phases (Stable: 0.89 ± 0.96 cm; Unstable: 1.05 ± 1.22 cm; P = 0.0209), Movement errors were more rightward in the Unstable phase indicative of a desire to avoid the cliff. Turning to the adaptation results, there was a significant phase by gain interaction (P = 0.0121). We observed that adaptation to the strongest rightward gains, leading to the largest rightward errors, was significantly reduced in the Unstable phase compared with the Stable phase (Figure 9). Specifically, a planned comparison revealed that adaptation to the strongest rightward gain of B = 40 Ns/m was significantly reduced in the Unstable compared with the Stable phase (P = 0.016). There was also a trend toward reduced adaptation to the gain of B = 28 Ns/m, however this was not significant when corrected for multiple comparisons (P = 0.011). Such reduced adaptation to the strongest rightward gain is in direct contrast to the findings of the main experiment where adaptation to the strongest leftward gains, leading to the largest leftward errors, was reduced. However, this reversal is precisely what we expected to occur given that the location of the cliff and subjective value of the movement error was also reversed.

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