Generalization and transfer of contextual cues in motor learning

We continuously adapt our movements in daily life, forming new internal models whenever necessary and updating existing ones. Recent work has suggested that this flexibility is enabled via sensorimotor cues, serving to access the correct internal model whenever necessary and keeping new models apart from previous ones. While research to date has mainly focused on identifying the nature of such cue representations, here we investigated whether and how these cue representations generalize, interfere, and transfer within and across effector systems. Subjects were trained to make two-stage reaching movements: a premovement that served as a cue, followed by a targeted movement that was perturbed by one of two opposite curl force fields. The direction of the premovement was uniquely coupled to the direction of the ensuing force field, enabling simultaneous learning of the two respective internal models. After training, generalization of the two premovement cues' representations was tested at untrained premovement directions, within both the trained and untrained hand. We show that the individual premovement representations generalize in a Gaussian-like pattern around the trained premovement direction. When the force fields are of unequal strengths, the cue-dependent generalization skews toward the strongest field. Furthermore, generalization patterns transfer to the nontrained hand, in an extrinsic reference frame. We conclude that contextual cues do not serve as discrete switches between multiple internal models. Instead, their generalization suggests a weighted contribution of the associated internal models based on the angular separation from the trained cues to the net motor output.

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Trial-by-Trial Adaptation of Movements during Mental Practice under Force Field

Computational and Mathematical Methods in Medicine ◽

10.1155/2013/109497 ◽

2013 ◽

Vol 2013 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Muhammad Nabeel Anwar ◽

Salman Hameed Khan

Keyword(s):

Nervous System ◽

Force Field ◽

Motor Imagery ◽

Internal Model ◽

Human Movement ◽

Mental Practice ◽

Higher Learning ◽

Reaching Movements ◽

Adaptive Compensation ◽

Human Nervous System

Human nervous system tries to minimize the effect of any external perturbing force by bringing modifications in the internal model. These modifications affect the subsequent motor commands generated by the nervous system. Adaptive compensation along with the appropriate modifications of internal model helps in reducing human movement errors. In the current study, we studied how motor imagery influences trial-to-trial learning in a robot-based adaptation task. Two groups of subjects performed reaching movements with or without motor imagery in a velocity-dependent force field. The results show that reaching movements performed with motor imagery have relatively a more focused generalization pattern and a higher learning rate in training direction.

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Application of Virtual Environments to Assessment of Human Motor Learning During Reaching Movements

Presence Teleoperators & Virtual Environments ◽

10.1162/pres.18.2.112 ◽

2009 ◽

Vol 18 (2) ◽

pp. 112-124 ◽

Cited By ~ 4

Author(s):

Ali Asadi Nikooyan ◽

Amir Abbas Zadpoor

Keyword(s):

Force Field ◽

Force Fields ◽

Physical World ◽

Reaching Movements ◽

Viscous Force ◽

Virtual Object ◽

Learning Mechanisms ◽

Reaching Task ◽

Third Phase ◽

Virtual Force

This paper studies learning of reaching movements in a dynamically variable virtual environment specially designed for this purpose. Learning of reaching movements in the physical world has been extensively studied by several researchers. In most of those studies, the subjects are asked to exercise reaching movements while being exposed to real force fields exerted through a robotic manipulandum. Those studies have contributed to our understanding of the mechanisms used by the human cognitive system to learn reaching movements in the physical world. The question that remains to be answered is how the learning mechanism in the physical world relates to its counterpart in the virtual world where the real force fields are replaced by virtual force fields. A limited number of studies have already addressed this question and have shown that there are, actually, quite a number of relationships between the learning mechanisms in these two different environments. In this study, we are focused on gaining a more in-depth understanding of these relationships. In our experiments, the subjects are asked to guide a virtual object to a desired target on a computer screen using a mouse. The movement of the virtual object is affected by a viscous virtual force field that is sensed by the examinees through their visual system. Three groups of examinees are used for the experiments. All the examinees are first trained in the null-field condition. Then, the viscous force field is introduced either suddenly (for the two first groups) or gradually (for the last group). While the first and third groups of the examinees used their dominant arm to guide the virtual object in the second step, the second group used their nondominant arm. Generalization of the learning from the dominant to the nondominant arm and vice versa was studied in the third phase of the experiments. Finally, the force field was removed and the examinees were asked to repeat the reaching task to study the so-called aftereffects phenomenon. The results of the experiments are compared with the studies performed in the physical world. It is shown that the trends of learning and generalization are similar to what is observed in the physical world for a sudden application of the virtual force field. However, the generalization behavior of the examinees is somewhat different from the physical world if the force field is gradually applied.

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Internal Models and Contextual Cues: Encoding Serial Order and Direction of Movement

Journal of Neurophysiology ◽

10.1152/jn.00240.2004 ◽

2005 ◽

Vol 93 (2) ◽

pp. 786-800 ◽

Cited By ~ 33

Author(s):

Stephanie K. Wainscott ◽

Opher Donchin ◽

Reza Shadmehr

Keyword(s):

Internal Model ◽

Serial Order ◽

Internal Models ◽

General Mechanism ◽

Sources Of Information ◽

Contextual Cues ◽

Reaching Task ◽

Motor Commands ◽

Preferred Direction ◽

Direction Of Movement

During reaching, the brain may rely on internal models to transform desired sensory outcomes into motor commands. This transformation depends on both the state of the limb and the cues that can identify the context of the movement. How are contextual cues and information about state of the limb combined in the computations of internal models? We considered a reaching task where forces on the hand depended on both the direction of movement (state of the limb) and order of that movement in a predefined sequence (contextual cue). When the cue was available, the motor system formed an internal model that used both serial order and target direction to program motor commands. Assuming that the internal model was formed by a population code through a combination of unknown basis elements, the sensitivity of the bases with respect to state of the limb and contextual cue should dictate how error in one type of movement affected all other movement types. Using a state-space theory, we estimated this generalization function and identified the adaptive system from trial-by-trial changes in performance. The results implied that the basis elements were tuned to direction of movement but output of each basis at its preferred direction was multiplicatively modulated by a weak tuning with respect to the contextual cue. Activity fields that multiplicatively encode diverse sources of information may serve as a general mechanism for a single network to produce context-dependent motor output.

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Effect of muscle fatigue on internal model formation and retention during reaching with the arm

Journal of Applied Physiology ◽

10.1152/japplphysiol.00140.2005 ◽

2006 ◽

Vol 100 (2) ◽

pp. 695-706 ◽

Cited By ~ 22

Author(s):

Craig D. Takahashi ◽

Dan Nemet ◽

Christie M. Rose-Gottron ◽

Jennifer K. Larson ◽

Dan M. Cooper ◽

...

Keyword(s):

Force Field ◽

Muscle Fatigue ◽

Internal Model ◽

Motor System ◽

Arm Movement ◽

Internal Models ◽

Viscous Force ◽

Adaptive Ability ◽

Model Formation ◽

Level Of Effort

The motor system adapts to novel dynamic environments by forming internal models that predict the muscle forces needed to move skillfully. The goal of this study was to determine how muscle fatigue affects internal model formation during arm movement and whether an internal model acquired while fatigued could be recalled accurately after rest. Twelve subjects adapted to a viscous force field applied by a lightweight robot as they reached to a target. They then reached while being resisted by elastic bands until they could no longer touch the target. This protocol reduced the strength of the muscles used to resist the force field by ∼20%. The bands were removed, and subjects adapted again to the viscous force field. Their adaptive ability, quantified by the amount and time constant of adaptation, was not significantly impaired following fatigue. The subjects then rested, recovering ∼70% of their lost force-generation ability. When they reached in the force field again, their prediction of the force field strength was different than in a nonfatigued state. This alteration was consistent with the use of a higher level of effort than normally used to counteract the force field. These results suggest that recovery from fatigue can affect recall of an internal model, even when the fatigue did not substantially affect the motor system’s ability to form the model. Recovery from fatigue apparently affects recall because the motor system represents internal models as a mapping between effort and movement and relies on practice to recalibrate this mapping.

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Feed-Forward Adaptation to a Varying Dynamic Environment During Reaching Movements

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2007.p0474 ◽

2007 ◽

Vol 19 (4) ◽

pp. 474-481 ◽

Cited By ~ 3

Author(s):

Koji Ito ◽

◽

Makoto Doi ◽

Toshiyuki Kondo ◽

◽

...

Keyword(s):

Force Field ◽

Internal Model ◽

Impedance Control ◽

Dynamic Environment ◽

Internal Model Control ◽

Neural Representation ◽

Reaching Movements ◽

Feed Forward ◽

Model Control ◽

Reaction Forces

Humans must compensate for the reaction forces arising from interaction with the physical environment. Recent studies have shown that humans can acquire a neural representation of the relationship between motor commands and movement, i.e. learn an internal model of environmental dynamics. We discuss feed-forward adaptation in a varying dynamic environment during reaching movements. Subjects first learned to move in a position-dependent divergent force field (DF) and velocity-dependent force field (VF), then move in a switched force field SF1 (DF→VF) and SF2 (VF→DF). The experimental results show that adaptation to switched force fields is achieved by programming the internal model control and impedance control in a feed-forward manner.

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Lack of Adaptation to Random Conflicting Force Fields of Variable Magnitude

Journal of Neurophysiology ◽

10.1152/jn.00773.2006 ◽

2007 ◽

Vol 97 (1) ◽

pp. 738-745 ◽

Cited By ~ 12

Author(s):

Rahul Gupta ◽

James Ashe

Keyword(s):

Motor Behavior ◽

Force Fields ◽

Human Subjects ◽

Large Body ◽

Internal Models ◽

Limited Capacity ◽

Contextual Cues ◽

Motor Behaviors ◽

Extended Practice ◽

The Brain

The concept of internal models has been used to explain how the brain learns and stores a variety of motor behaviors. A large body of work has shown that conflicting internal models could not be learned simultaneously; this suggests either a limited capacity or the unstable nature of short-term motor memories. However, it has been recently shown that multiple conflicting internal models of motor behavior could be acquired simultaneously if associated with appropriate contextual cues and random presentations. We re-examined this issue in a more complex environment in which the magnitude of the conflicting fields could vary randomly. Human subjects failed to show any evidence of learning the force fields themselves or the magnitude of the forces experienced, even with extended practice. Subjects did adapt to the applied perturbation when the field strength was kept constant but still did not form internal models. Our results show that neither random presentation nor specific contextual cues are sufficient for learning conflicting internal models when the magnitude of the forces is also unpredictable. The data suggest that multiple conflicting internal models cannot be learned in all environments, and provide support for the unstable nature or limited capacity of motor memories.

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Impedance Control Balances Stability With Metabolically Costly Muscle Activation

Journal of Neurophysiology ◽

10.1152/jn.00364.2004 ◽

2004 ◽

Vol 92 (5) ◽

pp. 3097-3105 ◽

Cited By ~ 75

Author(s):

David W. Franklin ◽

Udell So ◽

Mitsuo Kawato ◽

Theodore E. Milner

Keyword(s):

Force Field ◽

Muscle Activation ◽

Force Fields ◽

Impedance Control ◽

Reaching Movements ◽

The Novel ◽

Minimum Level ◽

Stable Direction ◽

Endpoint Stiffness ◽

Unstable Dynamics

Humans are able to stabilize their movements in environments with unstable dynamics by selectively modifying arm impedance independently of force and torque. We further investigated adaptation to unstable dynamics to determine whether the CNS maintains a constant overall level of stability as the instability of the environmental dynamics is varied. Subjects performed reaching movements in unstable force fields of varying strength, generated by a robotic manipulator. Although the force fields disrupted the initial movements, subjects were able to adapt to the novel dynamics and learned to produce straight trajectories. After adaptation, the endpoint stiffness of the arm was measured at the midpoint of the movement. The stiffness had been selectively modified in the direction of the instability. The stiffness in the stable direction was relatively unchanged from that measured during movements in a null force field prior to exposure to the unstable force field. This impedance modification was achieved without changes in force and torque. The overall stiffness of the arm and environment in the direction of instability was adapted to the force field strength such that it remained equivalent to that of the null force field. This suggests that the CNS attempts both to maintain a minimum level of stability and minimize energy expenditure.

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Hydration Free Energies of Organic Molecules in the FreeSolv Database Calculated with Polarized Atom In Molecules Atomic Charges and the GAFF Force Field.

10.26434/chemrxiv.6015611.v1 ◽

2018 ◽

Author(s):

Maximiliano Riquelme ◽

Alejandro Lara ◽

David L. Mobley ◽

Toon Vestraelen ◽

Adelio R Matamala ◽

...

Keyword(s):

Force Field ◽

Functional Groups ◽

Electrostatic Interactions ◽

Organic Molecules ◽

Force Fields ◽

Chemical Elements ◽

Atomic Charges ◽

Free Energies ◽

Molecular Systems ◽

Solvent Model

<div>Computer simulations of bio-molecular systems often use force fields, which are combinations of simple empirical atom-based functions to describe the molecular interactions. Even though polarizable force fields give a more detailed description of intermolecular interactions, nonpolarizable force fields, developed several decades ago, are often still preferred because of their reduced computation cost. Electrostatic interactions play a major role in bio-molecular systems and are therein described by atomic point charges.</div><div>In this work, we address the performance of different atomic charges to reproduce experimental hydration free energies in the FreeSolv database in combination with the GAFF force field. Atomic charges were calculated by two atoms-in-molecules approaches, Hirshfeld-I and Minimal Basis Iterative Stockholder (MBIS). To account for polarization effects, the charges were derived from the solute's electron density computed with an implicit solvent model and the energy required to polarize the solute was added to the free energy cycle. The calculated hydration free energies were analyzed with an error model, revealing systematic errors associated with specific functional groups or chemical elements. The best agreement with the experimental data is observed for the MBIS atomic charge method, including the solvent polarization, with a root mean square error of 2.0 kcal mol<sup>-1</sup> for the 613 organic molecules studied. The largest deviation was observed for phosphor-containing molecules and the molecules with amide, ester and amine functional groups.</div>

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QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

10.26434/chemrxiv.7247045.v2 ◽

2019 ◽

Cited By ~ 1

Author(s):

Joshua Horton ◽

Alice Allen ◽

Leela Dodda ◽

Daniel Cole

Keyword(s):

Quantum Mechanics ◽

Force Field ◽

Organic Molecules ◽

Force Fields ◽

Liquid Density ◽

Small Organic Molecules ◽

Automated Generation ◽

Energy Of Hydration ◽

Novel Method ◽

Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

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