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Article: Reaching Movements With Limb-Based Visual Feedback.

Zahed, Fatemeh / Berniker, Max

Motor control

2022 Volume 26, Issue 3, Page(s) 430–444

Abstract: Reaches in experimental settings are commonly found to be straight. This straightness is robust to physical, but not visual, perturbations. Here, we question whether typical visual feedback contributes to this finding by implicitly promoting straight ... ...

Abstract	Reaches in experimental settings are commonly found to be straight. This straightness is robust to physical, but not visual, perturbations. Here, we question whether typical visual feedback contributes to this finding by implicitly promoting straight movements. To do so, we replaced the conventional feedback depicting the hand's location with feedback depicting the limb's orientation. Reaching movements with three different visual feedback conditions were examined. In the final condition, the subject's arm was depicted as two rotating links, and targets were depicted as two links indicating a desired arm posture. We found that by replacing standard cursor feedback, reaches became curved and arched to the target. Our findings further demonstrate that depicted feedback influences movements, and feedback depicting the limb, in particular, may elicit curved reaches.
MeSH term(s)	Feedback, Sensory ; Hand ; Humans ; Movement ; Posture ; Psychomotor Performance
Language	English
Publishing date	2022-05-04
Publishing country	United States
Document type	Journal Article
ISSN	1087-1640
ISSN	1087-1640
DOI	10.1123/mc.2021-0070
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Learning vs. minding: How subjective costs can mask motor learning.

Healy, Chadwick M / Berniker, Max / Ahmed, Alaa A

PloS one

2023 Volume 18, Issue 3, Page(s) e0282693

Abstract: When learning new movements some people make larger kinematic errors than others, interpreted as a reduction in motor-learning ability. Consider a learning task where error-cancelling strategies incur higher effort costs, specifically where subjects ... ...

Abstract	When learning new movements some people make larger kinematic errors than others, interpreted as a reduction in motor-learning ability. Consider a learning task where error-cancelling strategies incur higher effort costs, specifically where subjects reach to targets in a force field. Concluding that those with greater error have learned less has a critical assumption: everyone uses the same error-canceling strategy. Alternatively, it could be that those with greater error may be choosing to sacrifice error reduction in favor of a lower effort movement. Here, we test this hypothesis in a dataset that includes both younger and older adults, where older adults exhibited greater kinematic errors. Utilizing the framework of optimal control theory, we infer subjective costs (i.e., strategies) and internal model accuracy (i.e., proportion of the novel dynamics learned) by fitting a model to each population's trajectory data. Our results demonstrate trajectories are defined by a combination of the amount learned and strategic differences represented by relative cost weights. Based on the model fits, younger adults could have learned between 65-90% of the novel dynamics. Critically, older adults could have learned between 60-85%. Each model fit produces trajectories that match the experimentally observed data, where a lower proportion learned in the model is compensated for by increasing costs on kinematic errors relative to effort. This suggests older and younger adults could be learning to the same extent, but older adults have a higher relative cost on effort compared to younger adults. These results call into question the proposition that older adults learn less than younger adults and provide a potential explanation for the equivocal findings in the literature. Importantly, our findings suggest that the metrics commonly used to probe motor learning paint an incomplete picture, and that to accurately quantify the learning process the subjective costs of movements should be considered.
MeSH term(s)	Humans ; Aged ; Learning ; Movement ; Psychomotor Performance
Language	English
Publishing date	2023-03-16
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0282693
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Article ; Online: Correction: Force field generalization and the internal representation of motor learning.

Rezazadeh, Alireza / Berniker, Max

PloS one

2020 Volume 15, Issue 1, Page(s) e0227963

Abstract: This corrects the article DOI: 10.1371/journal.pone.0225002.]. ...

Abstract	[This corrects the article DOI: 10.1371/journal.pone.0225002.].
Language	English
Publishing date	2020-01-16
Publishing country	United States
Document type	Published Erratum
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0227963
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Article ; Online: A Probabilistic Approach to Surgical Tasks and Skill Metrics.

Berniker, Max / Bhattacharyya, Kiran D / Brown, Kristen C / Jarc, Anthony

IEEE transactions on bio-medical engineering

2022 Volume 69, Issue 7, Page(s) 2212–2219

Abstract: Identifying and quantifying the activities that compose surgery is essential for effective interventions, computer-aided analyses and the advancement of surgical data science. For example, recent studies have shown that objective metrics (referred to as ... ...

Abstract	Identifying and quantifying the activities that compose surgery is essential for effective interventions, computer-aided analyses and the advancement of surgical data science. For example, recent studies have shown that objective metrics (referred to as objective performance indicators, OPIs) computed during key surgical tasks correlate with surgeon skill and clinical outcomes. Unambiguous identification of these surgical tasks can be particularly challenging for both human annotators and algorithms. Each surgical procedure has multiple approaches, each surgeon has their own level of skill, and the initiation and termination of surgical tasks can be subject to interpretation. As such, human annotators and machine learning models face the same basic problem, accurately identifying the boundaries of surgical tasks despite variable and unstructured information. For use in surgeon feedback, OPIs should also be robust to the variability and diversity in this data. To mitigate this difficulty, we propose a probabilistic approach to surgical task identification and calculation of OPIs. Rather than relying on tasks that are identified by hard temporal boundaries, we demonstrate an approach that relies on distributions of start and stop times, for a probabilistic interpretation of when the task was performed. We first use hypothetical data to outline how this approach is superior to other conventional approaches. Then we present similar analyses on surgical data. We find that when surgical tasks are identified by their individual probabilities, the resulting OPIs are less sensitive to noise in the identification of the start and stop times. These results suggest that this probabilistic approach holds promise for the future of surgical data science.
MeSH term(s)	Benchmarking ; Clinical Competence ; Feedback ; Humans ; Machine Learning ; Surgeons
Language	English
Publishing date	2022-06-17
Publishing country	United States
Document type	Journal Article
ZDB-ID	160429-6
ISSN	1558-2531 ; 0018-9294
ISSN (online)	1558-2531
ISSN	0018-9294
DOI	10.1109/TBME.2021.3139538
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Article ; Online: Force field generalization and the internal representation of motor learning.

Rezazadeh, Alireza / Berniker, Max

PloS one

2019 Volume 14, Issue 11, Page(s) e0225002

Abstract: When learning a new motor behavior, e.g. reaching in a force field, the nervous system builds an internal representation. Examining how subsequent reaches in unpracticed directions generalize reveals this representation. Although often studied, it is not ...

Abstract	When learning a new motor behavior, e.g. reaching in a force field, the nervous system builds an internal representation. Examining how subsequent reaches in unpracticed directions generalize reveals this representation. Although often studied, it is not known how this representation changes across training directions, or how changes in reach direction and the corresponding changes in limb impedance, influence these measurements. We ran a force field adaptation experiment using eight groups of subjects each trained on one of eight standard directions and then tested for generalization in the remaining seven directions. Generalization in all directions was local and asymmetric, providing limited and unequal transfer to the left and right side of the trained target. These asymmetries were not consistent in either magnitude or direction, even after correcting for changes in limb impedance. Relying on a standard model for generalization the inferred representations inconsistently shifted to one side or the other of their respective training direction. A second model that accounted for limb impedance and variations in baseline trajectories explained more data and the inferred representations were centered on their respective training directions. Our results highlight the influence of limb mechanics and impedance on psychophysical measurements and their interpretations for motor learning.
MeSH term(s)	Adult ; Behavior ; Electric Impedance ; Extremities/physiology ; Female ; Generalization, Psychological ; Humans ; Learning ; Male ; Motor Activity
Language	English
Publishing date	2019-11-19
Publishing country	United States
Document type	Journal Article
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0225002
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Article ; Online: Visual feedback of hand and target location does not explain the tendency for straight adapted reaches.

Zahed, Fatemeh / Berniker, Max

PloS one

2018 Volume 13, Issue 10, Page(s) e0206116

Abstract: Subjects in laboratory settings exhibit straight hand paths-typified by the minimum jerk path-even in the presence of a learned but disturbing force field. At the same time it is known that in this setting, visual feedback strongly influences reaches, ... ...

Abstract	Subjects in laboratory settings exhibit straight hand paths-typified by the minimum jerk path-even in the presence of a learned but disturbing force field. At the same time it is known that in this setting, visual feedback strongly influences reaches, biasing them to be straight. Here we examine whether or not this bias can account for the straightness of movements made in a force field. We ran three curl field experiments to investigate how the lack of visual feedback influences adapted reaches. In a first experiment, hand position was displayed at the beginning and at the end of each trial, but extinguished during movement, and the hand was passively brought back to the home location. In the second experiment, visual feedback of neither the hand nor the target was provided, and targets were haptically rendered as "dimples." In order to provide extended practice, a third experiment was run with a single target and an active reach back to the home location. In all three cases we found minor changes in the adapted reaches relative to control groups that had full visual feedback. Our subjects adopted trajectories that were better explained by minimum jerk paths over those that minimize effort. The results indicate that for point-to-point reaching movements the visual feedback, or lack there of, cannot explain why reaches appear to be straight, even after adapting to a perturbing force field.
MeSH term(s)	Adaptation, Physiological ; Adult ; Algorithms ; Computer Simulation ; Feedback, Sensory/physiology ; Female ; Hand/physiology ; Humans ; Male ; Models, Neurological ; Movement/physiology ; Psychomotor Performance/physiology ; Visual Perception/physiology ; Young Adult
Language	English
Publishing date	2018-10-24
Publishing country	United States
Document type	Journal Article
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0206116
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Article ; Online: A normative approach to neuromotor control.

Berniker, Max / Penny, Steven

Biological cybernetics

2018 Volume 113, Issue 1-2, Page(s) 83–92

Abstract: While we can readily observe and model the dynamics of our limbs, analyzing the neurons that drive movement is not nearly as straightforward. As a result, their role in motor behavior (e.g., forward models, state estimators, controllers, etc.) remains ... ...

Abstract	While we can readily observe and model the dynamics of our limbs, analyzing the neurons that drive movement is not nearly as straightforward. As a result, their role in motor behavior (e.g., forward models, state estimators, controllers, etc.) remains elusive. Computational explanations of electrophysiological data often rely on firing rate models or deterministic spiking models. Yet neither can accurately describe the interactions of neurons that issue spikes, probabilistically. Here we take a normative approach by designing a probabilistic spiking network to implement LQR control for a limb model. We find typical results: cosine tuning curves, population vectors that correlate with reaching directions, low-dimensional oscillatory activity for reaches that have no oscillatory movement, and changes in neuron's tuning curves after force field adaptation. Importantly, while the model is consistent with these empirically derived correlations, we can also analyze it in terms of the known causal mechanism: an LQR controller and the probability distributions of the neurons that encode it. Redesigning the system under a different set of assumptions (e.g. a different controller, or network architecture) would yield a new set of testable predictions. We suggest this normative approach can be a framework for examining the motor system, providing testable links between observed neural activity and motor behavior.
MeSH term(s)	Action Potentials/physiology ; Animals ; Computer Simulation ; Extremities/physiology ; Humans ; Models, Neurological ; Movement/physiology ; Muscle, Skeletal/physiology ; Nerve Net/physiology ; Neurons/physiology ; Nonlinear Dynamics ; User-Computer Interface
Language	English
Publishing date	2018-09-03
Publishing country	Germany
Document type	Journal Article
ZDB-ID	220699-7
ISSN	1432-0770 ; 0340-1200
ISSN (online)	1432-0770
ISSN	0340-1200
DOI	10.1007/s00422-018-0777-7
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Book ; Online: Force Field Generalization and the Internal Representation of Motor Learning

Rezazadeh, Alireza / Berniker, Max

2019

Abstract	When learning a new motor behavior, e.g. reaching in a force field, the nervous system builds an internal representation. Examining how subsequent reaches in unpracticed directions generalize reveals this representation. Though it is the subject of frequent studies, it is not known how this representation changes across training directions, or how changes in reach direction and the corresponding changes in limb impedance, influence measurements of it. We ran a force field adaptation experiment using eight groups of subjects each trained on one of eight standard directions and then tested for generalization in the remaining seven directions. Generalization in all directions was local and asymmetric, providing limited and unequal transfer to the left and right side of the trained target. These asymmetries were not consistent in either magnitude or direction even after correcting for changes in limb impedance, at odds with previous explanations. Relying on a standard model for generalization the inferred representations inconsistently shifted to one side or the other of their respective training direction. A second model that accounted for limb impedance and variations in baseline trajectories explained more data and the inferred representations were centered on their respective training directions. Our results highlight the influence of limb mechanics and impedance on psychophysical measurements and their interpretations for motor learning. Comment: Accepted for Publication in PLoS One Journal
Keywords	Computer Science - Robotics
Subject code	796
Publishing date	2019-10-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Planned Straight or Biased to Be So? The Influence of Visual Feedback on Reaching Movements.

Vaidyanathan, Natarajan / Penny, Steven / Berniker, Max

Journal of motor behavior

2019 Volume 52, Issue 2, Page(s) 236–248

Abstract: Behavioral studies consistently find that subjects move their hand along straight paths despite considerations that suggest reaches should be curved. Literature on this topic makes it clear that the experimentally displayed feedback influences how ... ...

Abstract	Behavioral studies consistently find that subjects move their hand along straight paths despite considerations that suggest reaches should be curved. Literature on this topic makes it clear that the experimentally displayed feedback influences how subjects reach. Could the standard visual feedback, a displayed cursor, explain the lack of path curvature in experimental results? To address this question, we conducted three experiments to examine reach behavior in the absence of the standard visual feedback. In the first experiment, we found significant increases in curvature as visual feedback was progressively extinguished across groups. A second experiment revealed that practiced reaches became curved after the standard visual feedback was removed. A final experiment found that subjects' reaches made before and after a brief display of visual feedback were similar, indicating a preference for specific curved trajectories. Our results suggest that the consistently straight reaches often observed could be due to a bias to move the displayed cursor straight, which when removed reveal subject-specific preferences for reaches that are often curved.
MeSH term(s)	Feedback, Sensory/physiology ; Female ; Hand/physiology ; Humans ; Male ; Movement/physiology ; Visual Perception ; Young Adult
Language	English
Publishing date	2019-05-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	2066611-1
ISSN	1940-1027 ; 0022-2895
ISSN (online)	1940-1027
ISSN	0022-2895
DOI	10.1080/00222895.2019.1609409
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Article: Deep networks for motor control functions.

Berniker, Max / Kording, Konrad P

Frontiers in computational neuroscience

2015 Volume 9, Page(s) 32

Abstract: The motor system generates time-varying commands to move our limbs and body. Conventional descriptions of motor control and learning rely on dynamical representations of our body's state (forward and inverse models), and control policies that must be ... ...

Abstract	The motor system generates time-varying commands to move our limbs and body. Conventional descriptions of motor control and learning rely on dynamical representations of our body's state (forward and inverse models), and control policies that must be integrated forward to generate feedforward time-varying commands; thus these are representations across space, but not time. Here we examine a new approach that directly represents both time-varying commands and the resulting state trajectories with a function; a representation across space and time. Since the output of this function includes time, it necessarily requires more parameters than a typical dynamical model. To avoid the problems of local minima these extra parameters introduce, we exploit recent advances in machine learning to build our function using a stacked autoencoder, or deep network. With initial and target states as inputs, this deep network can be trained to output an accurate temporal profile of the optimal command and state trajectory for a point-to-point reach of a non-linear limb model, even when influenced by varying force fields. In a manner that mirrors motor babble, the network can also teach itself to learn through trial and error. Lastly, we demonstrate how this network can learn to optimize a cost objective. This functional approach to motor control is a sharp departure from the standard dynamical approach, and may offer new insights into the neural implementation of motor control.
Language	English
Publishing date	2015-03-19
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2452964-3
ISSN	1662-5188
ISSN	1662-5188
DOI	10.3389/fncom.2015.00032
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