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I Meant to Do That: Determining the Intentions of Action in the Face of Disturbances.

Horowitz J, Patton J - PLoS ONE (2015)

Bottom Line: Our actions often do not match our intentions when there are external disturbances such as turbulence.Knowing such an intent signal is broadly applicable: enhanced human-machine interaction, the study of impaired intent in neural disorders, the real-time determination (and manipulation) of error in training, and complex systems that embody planning such as brain machine interfaces, team sports, crowds, or swarms.In addition, observing intent as it changes might act as a window into the mechanisms of planning, correction, and learning.

View Article: PubMed Central - PubMed

Affiliation: Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States of America.

ABSTRACT
Our actions often do not match our intentions when there are external disturbances such as turbulence. We derived a novel modeling approach for determining this motor intent from targeted reaching motions that are disturbed by an unexpected force. First, we demonstrated how to mathematically invert both feedforward (predictive) and feedback controls to obtain an intended trajectory. We next examined the model's sensitivity to a realistic range of parameter uncertainties, and found that the expected inaccuracy due to all possible parameter mis-estimations was less than typical movement-to-movement variations seen when humans reach to similar targets. The largest sensitivity arose mainly from uncertainty in joint stiffnesses. Humans cannot change their intent until they acquire sensory feedback, therefore we tested the hypothesis that a straight-line intent should be evident for at least the first 120 milliseconds following the onset of a disturbance. As expected, the intended trajectory showed no change from undisturbed reaching for more than 150 milliseconds after the disturbance onset. Beyond this point, however, we detected a change in intent in five out of eight subjects, surprisingly even when the hand is already near the target. Knowing such an intent signal is broadly applicable: enhanced human-machine interaction, the study of impaired intent in neural disorders, the real-time determination (and manipulation) of error in training, and complex systems that embody planning such as brain machine interfaces, team sports, crowds, or swarms. In addition, observing intent as it changes might act as a window into the mechanisms of planning, correction, and learning.

No MeSH data available.


Related in: MedlinePlus

Simulated data illustrating tautology of extraction across pulse and filtered Gaussian noise disturbance types.Intent is modeled as a minimum jerk, 5th order polynomial. Forces experienced are combined with intent via Burdet et al.’s [6] model to produce the simulated arm trajectory. Extraction to recover intention from arm and force trajectory follows. Parameter errors are introduced into the extraction and varied according to Table 1 to estimate sensitivity. Examining the distribution of deviation in trajectories from this analysis reveals that sensitivity is not uniformly distributed throughout a reach.
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pone.0137289.g001: Simulated data illustrating tautology of extraction across pulse and filtered Gaussian noise disturbance types.Intent is modeled as a minimum jerk, 5th order polynomial. Forces experienced are combined with intent via Burdet et al.’s [6] model to produce the simulated arm trajectory. Extraction to recover intention from arm and force trajectory follows. Parameter errors are introduced into the extraction and varied according to Table 1 to estimate sensitivity. Examining the distribution of deviation in trajectories from this analysis reveals that sensitivity is not uniformly distributed throughout a reach.

Mentions: As expected, the model was able to recover the original intended trajectory even when disturbed (Fig 1). However this idealized analysis cannot reveal any vulnerabilities to unexamined model parameters or to inaccuracy in the structure of the model itself as discussed in the next sections.


I Meant to Do That: Determining the Intentions of Action in the Face of Disturbances.

Horowitz J, Patton J - PLoS ONE (2015)

Simulated data illustrating tautology of extraction across pulse and filtered Gaussian noise disturbance types.Intent is modeled as a minimum jerk, 5th order polynomial. Forces experienced are combined with intent via Burdet et al.’s [6] model to produce the simulated arm trajectory. Extraction to recover intention from arm and force trajectory follows. Parameter errors are introduced into the extraction and varied according to Table 1 to estimate sensitivity. Examining the distribution of deviation in trajectories from this analysis reveals that sensitivity is not uniformly distributed throughout a reach.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4556620&req=5

pone.0137289.g001: Simulated data illustrating tautology of extraction across pulse and filtered Gaussian noise disturbance types.Intent is modeled as a minimum jerk, 5th order polynomial. Forces experienced are combined with intent via Burdet et al.’s [6] model to produce the simulated arm trajectory. Extraction to recover intention from arm and force trajectory follows. Parameter errors are introduced into the extraction and varied according to Table 1 to estimate sensitivity. Examining the distribution of deviation in trajectories from this analysis reveals that sensitivity is not uniformly distributed throughout a reach.
Mentions: As expected, the model was able to recover the original intended trajectory even when disturbed (Fig 1). However this idealized analysis cannot reveal any vulnerabilities to unexamined model parameters or to inaccuracy in the structure of the model itself as discussed in the next sections.

Bottom Line: Our actions often do not match our intentions when there are external disturbances such as turbulence.Knowing such an intent signal is broadly applicable: enhanced human-machine interaction, the study of impaired intent in neural disorders, the real-time determination (and manipulation) of error in training, and complex systems that embody planning such as brain machine interfaces, team sports, crowds, or swarms.In addition, observing intent as it changes might act as a window into the mechanisms of planning, correction, and learning.

View Article: PubMed Central - PubMed

Affiliation: Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States of America.

ABSTRACT
Our actions often do not match our intentions when there are external disturbances such as turbulence. We derived a novel modeling approach for determining this motor intent from targeted reaching motions that are disturbed by an unexpected force. First, we demonstrated how to mathematically invert both feedforward (predictive) and feedback controls to obtain an intended trajectory. We next examined the model's sensitivity to a realistic range of parameter uncertainties, and found that the expected inaccuracy due to all possible parameter mis-estimations was less than typical movement-to-movement variations seen when humans reach to similar targets. The largest sensitivity arose mainly from uncertainty in joint stiffnesses. Humans cannot change their intent until they acquire sensory feedback, therefore we tested the hypothesis that a straight-line intent should be evident for at least the first 120 milliseconds following the onset of a disturbance. As expected, the intended trajectory showed no change from undisturbed reaching for more than 150 milliseconds after the disturbance onset. Beyond this point, however, we detected a change in intent in five out of eight subjects, surprisingly even when the hand is already near the target. Knowing such an intent signal is broadly applicable: enhanced human-machine interaction, the study of impaired intent in neural disorders, the real-time determination (and manipulation) of error in training, and complex systems that embody planning such as brain machine interfaces, team sports, crowds, or swarms. In addition, observing intent as it changes might act as a window into the mechanisms of planning, correction, and learning.

No MeSH data available.


Related in: MedlinePlus