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Avraham G, Ivry RB. Interference underlies attenuation upon relearning in sensorimotor adaptation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.27.596118. [PMID: 38853972 PMCID: PMC11160603 DOI: 10.1101/2024.05.27.596118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Savings refers to the gain in performance upon relearning a task. In sensorimotor adaptation, savings is tested by having participants adapt to perturbed feedback and, following a washout block during which the system resets to baseline, presenting the same perturbation again. While savings has been observed with these tasks, we have shown that the contribution from implicit sensorimotor adaptation, a process that uses sensory prediction errors to recalibrate the sensorimotor map, is actually attenuated upon relearning (Avraham et al., 2021). In the present study, we test the hypothesis that this attenuation is due to interference arising from the washout block, and more generally, from experience with a different relationship between the movement and the feedback. In standard adaptation studies, removing the perturbation at the start of the washout block results in a salient error signal in the opposite direction to that observed during learning. As a starting point, we replicated the finding that implicit adaptation is attenuated following a washout period in which the feedback now signals a salient opposite error. When we eliminated visual feedback during washout, implicit adaptation was no longer attenuated upon relearning, consistent with the interference hypothesis. Next, we eliminated the salient error during washout by gradually decreasing the perturbation, creating a scenario in which the perceived errors fell within the range associated with motor noise. Nonetheless, attenuation was still prominent. Inspired by this observation, we tested participants with an extended experience with veridical feedback during an initial baseline phase and found that this was sufficient to cause robust attenuation of implicit adaptation during the first exposure to the perturbation. This effect was context-specific: It did not generalize to movements that were not associated with the interfering feedback. Taken together, these results show that the implicit sensorimotor adaptation system is highly sensitive to memory interference from a recent experience with a discrepant action-outcome contingency.
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Affiliation(s)
- Guy Avraham
- Department of Psychology and Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Richard B Ivry
- Department of Psychology and Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA
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2
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Tsay JS, Asmerian H, Germine LT, Wilmer J, Ivry RB, Nakayama K. Large-scale citizen science reveals predictors of sensorimotor adaptation. Nat Hum Behav 2024; 8:510-525. [PMID: 38291127 DOI: 10.1038/s41562-023-01798-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 12/04/2023] [Indexed: 02/01/2024]
Abstract
Sensorimotor adaptation is essential for keeping our movements well calibrated in response to changes in the body and environment. For over a century, researchers have studied sensorimotor adaptation in laboratory settings that typically involve small sample sizes. While this approach has proved useful for characterizing different learning processes, laboratory studies are not well suited for exploring the myriad of factors that may modulate human performance. Here, using a citizen science website, we collected over 2,000 sessions of data on a visuomotor rotation task. This unique dataset has allowed us to replicate, reconcile and challenge classic findings in the learning and memory literature, as well as discover unappreciated demographic constraints associated with implicit and explicit processes that support sensorimotor adaptation. More generally, this study exemplifies how a large-scale exploratory approach can complement traditional hypothesis-driven laboratory research in advancing sensorimotor neuroscience.
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Affiliation(s)
- Jonathan S Tsay
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Hrach Asmerian
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA.
| | - Laura T Germine
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA
| | - Jeremy Wilmer
- Department of Psychology, Wellesley College, Wellesley, MA, USA
| | - Richard B Ivry
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Ken Nakayama
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
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3
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Barradas VR, Koike Y, Schweighofer N. Theoretical limits on the speed of learning inverse models explain the rate of adaptation in arm reaching tasks. Neural Netw 2024; 170:376-389. [PMID: 38029719 DOI: 10.1016/j.neunet.2023.10.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 09/08/2023] [Accepted: 10/30/2023] [Indexed: 12/01/2023]
Abstract
An essential aspect of human motor learning is the formation of inverse models, which map desired actions to motor commands. Inverse models can be learned by adjusting parameters in neural circuits to minimize errors in the performance of motor tasks through gradient descent. However, the theory of gradient descent establishes limits on the learning speed. Specifically, the eigenvalues of the Hessian of the error surface around a minimum determine the maximum speed of learning in a task. Here, we use this theoretical framework to analyze the speed of learning in different inverse model learning architectures in a set of isometric arm-reaching tasks. We show theoretically that, in these tasks, the error surface and, thus the speed of learning, are determined by the shapes of the force manipulability ellipsoid of the arm and the distribution of targets in the task. In particular, rounder manipulability ellipsoids generate a rounder error surface, allowing for faster learning of the inverse model. Rounder target distributions have a similar effect. We tested these predictions experimentally in a quasi-isometric reaching task with a visuomotor transformation. The experimental results were consistent with our theoretical predictions. Furthermore, our analysis accounts for the speed of learning in previous experiments with incompatible and compatible virtual surgery tasks, and with visuomotor rotation tasks with different numbers of targets. By identifying aspects of a task that influence the speed of learning, our results provide theoretical principles for the design of motor tasks that allow for faster learning.
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Affiliation(s)
- Victor R Barradas
- Institute of Innovative Research, Tokyo Institute of Technology, 4259 R2-16 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8503, Japan.
| | - Yasuharu Koike
- Institute of Innovative Research, Tokyo Institute of Technology, 4259 R2-16 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
| | - Nicolas Schweighofer
- Biokinesiology and Physical Therapy, University of Southern California, 1540 Alcazar Street, CHP 155, Los Angeles, CA 90089-9006, USA
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4
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Jang J, Shadmehr R, Albert ST. A software tool for at-home measurement of sensorimotor adaptation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.12.571359. [PMID: 38168264 PMCID: PMC10760058 DOI: 10.1101/2023.12.12.571359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Sensorimotor adaptation is traditionally studied in well-controlled laboratory settings with specialized equipment. However, recent public health concerns such as the COVID-19 pandemic, as well as a desire to recruit a more diverse study population, have led the motor control community to consider at-home study designs. At-home motor control experiments are still rare because of the requirement to write software that can be easily used by anyone on any platform. To this end, we developed software that runs locally on a personal computer. The software provides audiovisual instructions and measures the ability of the subject to control the cursor in the context of visuomotor perturbations. We tested the software on a group of at-home participants and asked whether the adaptation principles inferred from in-lab measurements were reproducible in the at-home setting. For example, we manipulated the perturbations to test whether there were changes in adaptation rates (savings and interference), whether adaptation was associated with multiple timescales of memory (spontaneous recovery), and whether we could selectively suppress subconscious learning (delayed feedback, perturbation variability) or explicit strategies (limited reaction time). We found remarkable similarity between in-lab and at-home behaviors across these experimental conditions. Thus, we developed a software tool that can be used by research teams with little or no programming experience to study mechanisms of adaptation in an at-home setting.
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Affiliation(s)
- Jihoon Jang
- Laboratory for Computational Motor Control, Department of Biomedical Engineering Johns Hopkins School of Medicine, Baltimore MD
| | - Reza Shadmehr
- Laboratory for Computational Motor Control, Department of Biomedical Engineering Johns Hopkins School of Medicine, Baltimore MD
| | - Scott T Albert
- Laboratory for Computational Motor Control, Department of Biomedical Engineering Johns Hopkins School of Medicine, Baltimore MD
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5
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Tays GD, Hupfeld KE, McGregor HR, Beltran NE, Kofman IS, De Dios YE, Mulder ER, Bloomberg JJ, Mulavara AP, Wood SJ, Seidler RD. Daily artificial gravity is associated with greater neural efficiency during sensorimotor adaptation. Cereb Cortex 2023; 33:8011-8023. [PMID: 36958815 PMCID: PMC10267627 DOI: 10.1093/cercor/bhad094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 02/27/2023] [Accepted: 02/28/2023] [Indexed: 03/25/2023] Open
Abstract
Altered vestibular signaling and body unloading in microgravity results in sensory reweighting and adaptation. Microgravity effects are well-replicated in head-down tilt bed rest (HDBR). Artificial gravity (AG) is a potential countermeasure to mitigate the effects of microgravity on human physiology and performance. We examined the effectiveness of daily AG for mitigating brain and/or behavioral changes in 60 days of HDBR. One group received AG for 30 minutes daily (AG; n = 16) and a control group spent the same time in HDBR but received no AG (CTRL; n = 8). All participants performed a sensorimotor adaptation task five times during fMRI scanning: twice prior to HDBR, twice during HDBR, and once following HDBR. The AG group showed similar behavioral adaptation effects compared with the CTRLs. We identified decreased brain activation in the AG group from pre to late HDBR in the cerebellum for the task baseline portion and in the thalamus, calcarine, cuneus, premotor cortices, and superior frontal gyrus in the AG group during the early adaptation phase. The two groups also exhibited differential brain-behavior correlations. Together, these results suggest that AG may result in a reduced recruitment of brain activity for basic motor processes and sensorimotor adaptation. These effects may stem from the somatosensory and vestibular stimulation that occur with AG.
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Affiliation(s)
- Grant D Tays
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL 32603, USA
| | - Kathleen E Hupfeld
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL 32603, USA
| | - Heather R McGregor
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL 32603, USA
| | | | | | | | | | | | | | - Scott J Wood
- NASA Johnson Space Center, Houston, TX 77058, USA
| | - Rachael D Seidler
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL 32603, USA
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL 32603, USA
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6
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Bergoin R, Torcini A, Deco G, Quoy M, Zamora-López G. Inhibitory neurons control the consolidation of neural assemblies via adaptation to selective stimuli. Sci Rep 2023; 13:6949. [PMID: 37117236 PMCID: PMC10147639 DOI: 10.1038/s41598-023-34165-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 04/25/2023] [Indexed: 04/30/2023] Open
Abstract
Brain circuits display modular architecture at different scales of organization. Such neural assemblies are typically associated to functional specialization but the mechanisms leading to their emergence and consolidation still remain elusive. In this paper we investigate the role of inhibition in structuring new neural assemblies driven by the entrainment to various inputs. In particular, we focus on the role of partially synchronized dynamics for the creation and maintenance of structural modules in neural circuits by considering a network of excitatory and inhibitory [Formula: see text]-neurons with plastic Hebbian synapses. The learning process consists of an entrainment to temporally alternating stimuli that are applied to separate regions of the network. This entrainment leads to the emergence of modular structures. Contrary to common practice in artificial neural networks-where the acquired weights are typically frozen after the learning session-we allow for synaptic adaptation even after the learning phase. We find that the presence of inhibitory neurons in the network is crucial for the emergence and the post-learning consolidation of the modular structures. Indeed networks made of purely excitatory neurons or of neurons not respecting Dale's principle are unable to form or to maintain the modular architecture induced by the stimuli. We also demonstrate that the number of inhibitory neurons in the network is directly related to the maximal number of neural assemblies that can be consolidated, supporting the idea that inhibition has a direct impact on the memory capacity of the neural network.
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Affiliation(s)
- Raphaël Bergoin
- ETIS, UMR 8051, ENSEA, CY Cergy Paris Université, CNRS, 6 Av. du Ponceau, 95000, Cergy-Pontoise, France.
- Center for Brain and Cognition, Department of Information and Communications Technologies, Pompeu Fabra University, Carrer Ramón Trias i Fargas 25-27, 08005, Barcelona, Spain.
| | - Alessandro Torcini
- Laboratoire de Physique Théorique et Modélisation, UMR 8089, CY Cergy Paris Université, CNRS, 2 Av. Adolphe Chauvin, 95032, Cergy-Pontoise, France
| | - Gustavo Deco
- Center for Brain and Cognition, Department of Information and Communications Technologies, Pompeu Fabra University, Carrer Ramón Trias i Fargas 25-27, 08005, Barcelona, Spain
- Instituciò Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010, Barcelona, Spain
| | - Mathias Quoy
- ETIS, UMR 8051, ENSEA, CY Cergy Paris Université, CNRS, 6 Av. du Ponceau, 95000, Cergy-Pontoise, France
- IPAL, CNRS, 1 Fusionopolis Way #21-01 Connexis (South Tower), Singapore, 138632, Singapore
| | - Gorka Zamora-López
- Center for Brain and Cognition, Department of Information and Communications Technologies, Pompeu Fabra University, Carrer Ramón Trias i Fargas 25-27, 08005, Barcelona, Spain
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7
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Standage DI, Areshenkoff CN, Gale DJ, Nashed JY, Flanagan JR, Gallivan JP. Whole-brain dynamics of human sensorimotor adaptation. Cereb Cortex 2023; 33:4761-4778. [PMID: 36245212 PMCID: PMC10110437 DOI: 10.1093/cercor/bhac378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 08/26/2022] [Accepted: 08/27/2022] [Indexed: 11/13/2022] Open
Abstract
Humans vary greatly in their motor learning abilities, yet little is known about the neural processes that underlie this variability. We identified distinct profiles of human sensorimotor adaptation that emerged across 2 days of learning, linking these profiles to the dynamics of whole-brain functional networks early on the first day when cognitive strategies toward sensorimotor adaptation are believed to be most prominent. During early learning, greater recruitment of a network of higher-order brain regions, involving prefrontal and anterior temporal cortex, was associated with faster learning. At the same time, greater integration of this "cognitive network" with a sensorimotor network was associated with slower learning, consistent with the notion that cognitive strategies toward adaptation operate in parallel with implicit learning processes of the sensorimotor system. On the second day, greater recruitment of a network that included the hippocampus was associated with faster learning, consistent with the notion that declarative memory systems are involved with fast relearning of sensorimotor mappings. Together, these findings provide novel evidence for the role of higher-order brain systems in driving variability in adaptation.
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Affiliation(s)
- Dominic I Standage
- Centre for Neuroscience Studies, Queen’s University, Botterell Hall, 18 Stuart Street, Kingston, Ontario K7L 3N6, Canada
- Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, Ontario, Canada
| | - Corson N Areshenkoff
- Centre for Neuroscience Studies, Queen’s University, Botterell Hall, 18 Stuart Street, Kingston, Ontario K7L 3N6, Canada
| | - Daniel J Gale
- Centre for Neuroscience Studies, Queen’s University, Botterell Hall, 18 Stuart Street, Kingston, Ontario K7L 3N6, Canada
| | - Joseph Y Nashed
- Centre for Neuroscience Studies, Queen’s University, Botterell Hall, 18 Stuart Street, Kingston, Ontario K7L 3N6, Canada
- Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, Ontario, Canada
| | - J Randall Flanagan
- Centre for Neuroscience Studies, Queen’s University, Botterell Hall, 18 Stuart Street, Kingston, Ontario K7L 3N6, Canada
- Department of Psychology, Queen’s University, Humphrey Hall, 62 Arch Street, Kingston, Ontario K7L 3N6, Canada
| | - Jason P Gallivan
- Centre for Neuroscience Studies, Queen’s University, Botterell Hall, 18 Stuart Street, Kingston, Ontario K7L 3N6, Canada
- Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, Ontario, Canada
- Department of Psychology, Queen’s University, Humphrey Hall, 62 Arch Street, Kingston, Ontario K7L 3N6, Canada
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8
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Hadjiosif AM, Morehead JR, Smith MA. A double dissociation between savings and long-term memory in motor learning. PLoS Biol 2023; 21:e3001799. [PMID: 37104303 PMCID: PMC10138789 DOI: 10.1371/journal.pbio.3001799] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 03/06/2023] [Indexed: 04/28/2023] Open
Abstract
Memories are easier to relearn than learn from scratch. This advantage, known as savings, has been widely assumed to result from the reemergence of stable long-term memories. In fact, the presence of savings has often been used as a marker for whether a memory has been consolidated. However, recent findings have demonstrated that motor learning rates can be systematically controlled, providing a mechanistic alternative to the reemergence of a stable long-term memory. Moreover, recent work has reported conflicting results about whether implicit contributions to savings in motor learning are present, absent, or inverted, suggesting a limited understanding of the underlying mechanisms. To elucidate these mechanisms, we investigate the relationship between savings and long-term memory by experimentally dissecting the underlying memories based on short-term (60-s) temporal persistence. Components of motor memory that are temporally-persistent at 60 s might go on to contribute to stable, consolidated long-term memory, whereas temporally-volatile components that have already decayed away by 60 s cannot. Surprisingly, we find that temporally-volatile implicit learning leads to savings, whereas temporally-persistent learning does not, but that temporally-persistent learning leads to long-term memory at 24 h, whereas temporally-volatile learning does not. This double dissociation between the mechanisms for savings and long-term memory formation challenges widespread assumptions about the connection between savings and memory consolidation. Moreover, we find that temporally-persistent implicit learning not only fails to contribute to savings, but also that it produces an opposite, anti-savings effect, and that the interplay between this temporally-persistent anti-savings and temporally-volatile savings provides an explanation for several seemingly conflicting recent reports about whether implicit contributions to savings are present, absent, or inverted. Finally, the learning curves we observed for the acquisition of temporally-volatile and temporally-persistent implicit memories demonstrate the coexistence of implicit memories with distinct time courses, challenging the assertion that models of context-based learning and estimation should supplant models of adaptive processes with different learning rates. Together, these findings provide new insight into the mechanisms for savings and long-term memory formation.
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Affiliation(s)
- Alkis M. Hadjiosif
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, United States of America
| | - J. Ryan Morehead
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, United States of America
- School of Psychology, University of Leeds, Leeds, United Kingdom
| | - Maurice A. Smith
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, United States of America
- Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States of America
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9
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Heald JB, Lengyel M, Wolpert DM. Contextual inference in learning and memory. Trends Cogn Sci 2023; 27:43-64. [PMID: 36435674 PMCID: PMC9789331 DOI: 10.1016/j.tics.2022.10.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 10/11/2022] [Accepted: 10/12/2022] [Indexed: 11/25/2022]
Abstract
Context is widely regarded as a major determinant of learning and memory across numerous domains, including classical and instrumental conditioning, episodic memory, economic decision-making, and motor learning. However, studies across these domains remain disconnected due to the lack of a unifying framework formalizing the concept of context and its role in learning. Here, we develop a unified vernacular allowing direct comparisons between different domains of contextual learning. This leads to a Bayesian model positing that context is unobserved and needs to be inferred. Contextual inference then controls the creation, expression, and updating of memories. This theoretical approach reveals two distinct components that underlie adaptation, proper and apparent learning, respectively referring to the creation and updating of memories versus time-varying adjustments in their expression. We review a number of extensions of the basic Bayesian model that allow it to account for increasingly complex forms of contextual learning.
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Affiliation(s)
- James B Heald
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA.
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK; Center for Cognitive Computation, Department of Cognitive Science, Central European University, Budapest, Hungary.
| | - Daniel M Wolpert
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK.
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10
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Zhou W, Kruse EA, Brower R, North R, Joiner WM. Motion state-dependent motor learning based on explicit visual feedback is quickly recalled, but is less stable than adaptation to physical perturbations. J Neurophysiol 2022; 128:854-871. [PMID: 36043804 PMCID: PMC9529258 DOI: 10.1152/jn.00520.2021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Recent studies have shown that adaptation to visual feedback perturbations during arm reaching movements involves implicit and explicit learning components. Evidence also suggests that explicit, intentional learning mechanisms are largely responsible for savings—a faster recalibration compared with initial training. However, the extent explicit learning mechanisms facilitate learning and early savings (i.e., the rapid recall of previous performance) for motion state-dependent learning is generally unknown. To address this question, we compared the early savings/recall achieved by two groups of human subjects. One experienced physical perturbations (a velocity-dependent force-field, vFF) to promote adaptation that is thought to be a largely implicit process. The second was only given visual feedback of the required force-velocity relationship; subjects moved in force channels and we provided visual feedback of the lateral force exerted during the movement, as well as the required force pattern based on the movement velocity. Thus, subjects were shown explicit information on the extent the applied temporal pattern of force matched the required velocity-dependent force profile if the force-field perturbation had been applied. After training, both groups experienced a decay and washout period, which was followed by a reexposure block to assess early savings/recall. Although decay was faster for the explicit visual feedback group, the single-trial recall was similar to the physical perturbation group. Thus, compared with visual feedback perturbations, conscious modification of motor output based on motion state-dependent feedback demonstrates rapid recall, but this adjustment is less stable than adaptation based on experiencing the multisensory errors that accompany physical perturbations. NEW & NOTEWORTHY The extent explicit feedback facilitates motion state-dependent changes to motor output is largely unknown. Here, we examined motor adaptation for subjects that experienced physical perturbations and another that made adjustments based on explicit visual feedback information of the required force-velocity relationship. Our results suggest that adjustment of motor output can be based on explicit motion state-dependent information and demonstrates rapid recall, but this learning is less stable than adaptation based on physical perturbations to movement.
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Affiliation(s)
- Weiwei Zhou
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, CA, United States
| | - Elizabeth A Kruse
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, CA, United States
| | - Rylee Brower
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, CA, United States
| | - Ryan North
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, CA, United States
| | - Wilsaan M Joiner
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, CA, United States.,NDepartment of Neurology, University of California, Davis, Davis, CA, United States
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11
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Herzog M, Focke A, Maurus P, Thürer B, Stein T. Random Practice Enhances Retention and Spatial Transfer in Force Field Adaptation. Front Hum Neurosci 2022; 16:816197. [PMID: 35601906 PMCID: PMC9116228 DOI: 10.3389/fnhum.2022.816197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/03/2022] [Indexed: 11/17/2022] Open
Abstract
The contextual-interference effect is a frequently examined phenomenon in motor skill learning but has not been extensively investigated in motor adaptation. Here, we first tested experimentally if the contextual-interference effect is detectable in force field adaptation regarding retention and spatial transfer, and then fitted state-space models to the data to relate the findings to the “forgetting-and-reconstruction hypothesis”. Thirty-two participants were divided into two groups with either a random or a blocked practice schedule. They practiced reaching to four targets and were tested 10 min and 24 h afterward for motor retention and spatial transfer on an interpolation and an extrapolation target, and on targets which were shifted 10 cm away. The adaptation progress was participant-specifically fitted with 4-slow-1-fast state-space models accounting for generalization and set breaks. The blocked group adapted faster (p = 0.007) but did not reach a better adaptation at practice end. We found better retention (10 min), interpolation transfer (10 min), and transfer to shifted targets (10 min and 24 h) for the random group (each p < 0.05). However, no differences were found for retention or for the interpolation target after 24 h. Neither group showed transfer to the extrapolation target. The extended state-space model could replicate the behavioral results with some exceptions. The study shows that the contextual-interference effect is partially detectable in practice, short-term retention, and spatial transfer in force field adaptation; and that state-space models provide explanatory descriptions for the contextual-interference effect in force field adaptation.
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Affiliation(s)
- Michael Herzog
- BioMotion Center, Karlsruhe Institute of Technology, Karlsruhe, Germany
- *Correspondence: Michael Herzog,
| | - Anne Focke
- BioMotion Center, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Philipp Maurus
- Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada
| | - Benjamin Thürer
- BioMotion Center, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Thorsten Stein
- BioMotion Center, Karlsruhe Institute of Technology, Karlsruhe, Germany
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12
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Sadaphal DP, Kumar A, Mutha PK. Sensorimotor Learning in Response to Errors in Task Performance. eNeuro 2022; 9:ENEURO.0371-21.2022. [PMID: 35110383 PMCID: PMC8938978 DOI: 10.1523/eneuro.0371-21.2022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 01/18/2022] [Accepted: 01/23/2022] [Indexed: 01/09/2023] Open
Abstract
The human sensorimotor system is sensitive to both limb-related prediction errors and task-related performance errors. Prediction error signals are believed to drive implicit refinements to motor plans. However, an understanding of the mechanisms that performance errors stimulate has remained unclear largely because their effects have not been probed in isolation from prediction errors. Diverging from past work, we induced performance errors independent of prediction errors by shifting the location of a reach target but keeping the intended and actual kinematic consequences of the motion matched. Our first two experiments revealed that rather than implicit learning, motor adjustments in response to performance errors reflect the use of deliberative, volitional strategies. Our third experiment revealed a potential dissociation of performance-error-driven strategies based on error size. Specifically, behavioral changes following large errors were consistent with goal-directed or model-based control, known to be supported by connections between prefrontal cortex and associative striatum. In contrast, motor changes following smaller performance errors carried signatures of model-free stimulus-response learning, of the kind underpinned by pathways between motor cortical areas and sensorimotor striatum. Across all experiments, we also found remarkably faster re-learning, advocating that such "savings" is associated with retrieval of previously learned strategic error compensation and may not require a history of exposure to limb-related errors.
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Affiliation(s)
- Dhwani P Sadaphal
- Center for Cognitive and Brain Sciences, Indian Institute of Technology Gandhinagar, Palaj, Gandhinagar 382355, India
| | - Adarsh Kumar
- Center for Cognitive and Brain Sciences, Indian Institute of Technology Gandhinagar, Palaj, Gandhinagar 382355, India
- Department of Mechanical Engineering, Indian Institute of Technology Gandhinagar, India
| | - Pratik K Mutha
- Center for Cognitive and Brain Sciences, Indian Institute of Technology Gandhinagar, Palaj, Gandhinagar 382355, India
- Department of Biological Engineering, Indian Institute of Technology Gandhinagar, Palaj, Gandhinagar 382355, India
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13
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Albert ST, Jang J, Modchalingam S, 't Hart BM, Henriques D, Lerner G, Della-Maggiore V, Haith AM, Krakauer JW, Shadmehr R. Competition between parallel sensorimotor learning systems. eLife 2022; 11:e65361. [PMID: 35225229 PMCID: PMC9068222 DOI: 10.7554/elife.65361] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 02/11/2022] [Indexed: 11/13/2022] Open
Abstract
Sensorimotor learning is supported by at least two parallel systems: a strategic process that benefits from explicit knowledge and an implicit process that adapts subconsciously. How do these systems interact? Does one system's contributions suppress the other, or do they operate independently? Here, we illustrate that during reaching, implicit and explicit systems both learn from visual target errors. This shared error leads to competition such that an increase in the explicit system's response siphons away resources that are needed for implicit adaptation, thus reducing its learning. As a result, steady-state implicit learning can vary across experimental conditions, due to changes in strategy. Furthermore, strategies can mask changes in implicit learning properties, such as its error sensitivity. These ideas, however, become more complex in conditions where subjects adapt using multiple visual landmarks, a situation which introduces learning from sensory prediction errors in addition to target errors. These two types of implicit errors can oppose each other, leading to another type of competition. Thus, during sensorimotor adaptation, implicit and explicit learning systems compete for a common resource: error.
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Affiliation(s)
- Scott T Albert
- Department of Biomedical Engineering, Johns Hopkins School of MedicineBaltimoreUnited States
- Neuroscience Center, University of North CarolinaChapel HillUnited States
| | - Jihoon Jang
- Department of Biomedical Engineering, Johns Hopkins School of MedicineBaltimoreUnited States
- Vanderbilt University School of MedicineNashvilleUnited States
| | | | | | - Denise Henriques
- Department of Kinesiology and Health Science, York UniversityTorontoCanada
| | - Gonzalo Lerner
- IFIBIO Houssay, Deparamento de Fisiología y Biofísia, Facultad de Medicina, Universidad de Buenos AiresBuenos AiresArgentina
| | - Valeria Della-Maggiore
- IFIBIO Houssay, Deparamento de Fisiología y Biofísia, Facultad de Medicina, Universidad de Buenos AiresBuenos AiresArgentina
| | - Adrian M Haith
- Department of Neurology, Johns Hopkins School of MedicineBaltimoreUnited States
| | - John W Krakauer
- Department of Neurology, Johns Hopkins School of MedicineBaltimoreUnited States
- Department of Neuroscience, Johns Hopkins School of MedicineBaltimoreUnited States
- The Santa Fe InstituteSanta FeUnited States
| | - Reza Shadmehr
- Department of Biomedical Engineering, Johns Hopkins School of MedicineBaltimoreUnited States
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14
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Practice Schedules Affect How Learners Correct Their Errors: Secondary Analysis From a Contextual Interference Study. JOURNAL OF MOTOR LEARNING AND DEVELOPMENT 2022. [DOI: 10.1123/jmld.2022-0021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Contextual interference is an established phenomenon in learning research; random practice schedules are associated with poorer performance, but superior learning, compared with blocked practice schedules. We present a secondary analysis of N = 84 healthy young adults, replicating the contextual interference effect in a time estimation task. We used the determinant of a correlation matrix to measure the amount of order in participant responses. We calculated this determinant in different phase spaces: trial space, the determinant of the previous five trials (lagged constant error 0–4); and target space, the determinant of the previous five trials of the same target. In trial space, there was no significant difference between groups (p = .98) and no Group × Lag interaction (p = .54), although there was an effect of Lag (p < .01). In target space, there were effects of Group (p = .02), Lag (p < .01), and a Group × Lag interaction (p = .03). Ultimately, randomly scheduled practice was associated with adaptive corrections but positive correlations between errors from trial to trial (e.g., overshoots followed by smaller overshoots). Blocked practice was associated with more adaptive corrections but uncorrelated responses. Our findings suggest that random practice leads to the retrieval and updating of the target from memory, facilitating long-term retention and transfer.
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15
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Moore RT, Cluff T. Individual Differences in Sensorimotor Adaptation Are Conserved Over Time and Across Force-Field Tasks. Front Hum Neurosci 2021; 15:692181. [PMID: 34916916 PMCID: PMC8669441 DOI: 10.3389/fnhum.2021.692181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 11/02/2021] [Indexed: 11/23/2022] Open
Abstract
Sensorimotor adaptation enables the nervous system to modify actions for different conditions and environments. Many studies have investigated factors that influence adaptation at the group level. There is growing recognition that individuals vary in their ability to adapt motor skills and that a better understanding of individual differences in adaptation may inform how motor skills are taught and rehabilitated. Here we examined individual differences in the adaptation of upper-limb reaching movements. We quantified the extent to which participants adapted their movements to a velocity-dependent force field during an initial session, at 24 h, and again 1-week later. Participants (n = 28) displayed savings, which was expressed as greater initial adaptation when re-exposed to the force field. Individual differences in adaptation across various stages of the experiment displayed weak-strong reliability, such that individuals who adapted to a greater extent in the initial session tended to do so when re-exposed to the force field. Our second experiment investigated if individual differences in adaptation are also present when participants adapt to different force fields or a force field and visuomotor rotation. Separate groups of participants adapted to position- and velocity-dependent force fields (Experiment 2a; n = 20) or a velocity-dependent force field and visuomotor rotation in a single session (Experiment 2b; n = 20). Participants who adapted to a greater extent to velocity-dependent forces tended to show a greater extent of adaptation when exposed to position-dependent forces. In contrast, correlations were weak between various stages of adaptation to the force-field and visuomotor rotation. Collectively, our study reveals individual differences in adaptation that are reliable across repeated exposure to the same force field and present when adapting to different force fields.
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Affiliation(s)
- Robert T Moore
- Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Tyler Cluff
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada
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16
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Rossi C, Roemmich RT, Schweighofer N, Bastian AJ, Leech KA. Younger and Late Middle-Aged Adults Exhibit Different Patterns of Cognitive-Motor Interference During Locomotor Adaptation, With No Disruption of Savings. Front Aging Neurosci 2021; 13:729284. [PMID: 34899267 PMCID: PMC8664558 DOI: 10.3389/fnagi.2021.729284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 10/21/2021] [Indexed: 11/13/2022] Open
Abstract
It has been proposed that motor adaptation and subsequent savings (or faster relearning) of an adapted movement pattern are mediated by cognitive processes. Here, we evaluated the pattern of cognitive-motor interference that emerges when young and late middle-aged adults perform an executive working memory task during locomotor adaptation. We also asked if this interferes with savings of a newly learned walking pattern, as has been suggested by a study of reaching adaptation. We studied split-belt treadmill adaptation and savings in young (21 ± 2 y/o) and late middle-aged (56 ± 6 y/o) adults with or without a secondary 2-back task during adaptation. We found that young adults showed similar performance on the 2-back task during baseline and adaptation, suggesting no effect of the dual-task on cognitive performance; however, dual-tasking interfered with adaptation over the first few steps. Conversely, dual-tasking caused a decrement in cognitive performance in late middle-aged adults with no effect on adaptation. To determine if this effect was specific to adaptation, we also evaluated dual-task interference in late middle-aged adults that dual-tasked while walking in a complex environment that did not induce motor adaptation. This group exhibited less cognitive-motor interference than late middle-aged adults who dual-tasked during adaptation. Savings was unaffected by dual-tasking in both young and late middle-aged adults, which may indicate different underlying mechanisms for savings of reaching and walking. Collectively, our findings reveal an age-dependent effect of cognitive-motor interference during dual-task locomotor adaptation and no effect of dual-tasking on savings, regardless of age. Young adults maintain cognitive performance and show a mild decrement in locomotor adaptation, while late middle-aged adults adapt locomotion at the expense of cognitive performance.
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Affiliation(s)
- Cristina Rossi
- Center for Movement Studies, Kennedy Krieger Institute, Baltimore, MD, United States
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Ryan T. Roemmich
- Center for Movement Studies, Kennedy Krieger Institute, Baltimore, MD, United States
- Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Nicolas Schweighofer
- Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, United States
| | - Amy J. Bastian
- Center for Movement Studies, Kennedy Krieger Institute, Baltimore, MD, United States
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Kristan A. Leech
- Center for Movement Studies, Kennedy Krieger Institute, Baltimore, MD, United States
- Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, United States
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17
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Contextual inference underlies the learning of sensorimotor repertoires. Nature 2021; 600:489-493. [PMID: 34819674 DOI: 10.1038/s41586-021-04129-3] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 10/13/2021] [Indexed: 11/09/2022]
Abstract
ASBTRACT Humans spend a lifetime learning, storing and refining a repertoire of motor memories. For example, through experience, we become proficient at manipulating a large range of objects with distinct dynamical properties. However, it is unknown what principle underlies how our continuous stream of sensorimotor experience is segmented into separate memories and how we adapt and use this growing repertoire. Here we develop a theory of motor learning based on the key principle that memory creation, updating and expression are all controlled by a single computation-contextual inference. Our theory reveals that adaptation can arise both by creating and updating memories (proper learning) and by changing how existing memories are differentially expressed (apparent learning). This insight enables us to account for key features of motor learning that had no unified explanation: spontaneous recovery1, savings2, anterograde interference3, how environmental consistency affects learning rate4,5 and the distinction between explicit and implicit learning6. Critically, our theory also predicts new phenomena-evoked recovery and context-dependent single-trial learning-which we confirm experimentally. These results suggest that contextual inference, rather than classical single-context mechanisms1,4,7-9, is the key principle underlying how a diverse set of experiences is reflected in our motor behaviour.
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18
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The cost of correcting for error during sensorimotor adaptation. Proc Natl Acad Sci U S A 2021; 118:2101717118. [PMID: 34580215 DOI: 10.1073/pnas.2101717118] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/25/2021] [Indexed: 11/18/2022] Open
Abstract
Learning from error is often a slow process. In machine learning, the learning rate depends on a loss function that specifies a cost for error. Here, we hypothesized that during motor learning, error carries an implicit cost for the brain because the act of correcting for error consumes time and energy. Thus, if this implicit cost could be increased, it may robustly alter how the brain learns from error. To vary the implicit cost of error, we designed a task that combined saccade adaptation with motion discrimination: movement errors resulted in corrective saccades, but those corrections took time away from acquiring information in the discrimination task. We then modulated error cost using coherence of the discrimination task and found that when error cost was large, pupil diameter increased and the brain learned more from error. However, when error cost was small, the pupil constricted and the brain learned less from the same error. Thus, during sensorimotor adaptation, the act of correcting for error carries an implicit cost for the brain. Modulating this cost affects how much the brain learns from error.
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19
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Savings in Human Force Field Learning Supported by Feedback Adaptation. eNeuro 2021; 8:ENEURO.0088-21.2021. [PMID: 34465612 PMCID: PMC8457419 DOI: 10.1523/eneuro.0088-21.2021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 07/26/2021] [Accepted: 08/17/2021] [Indexed: 11/23/2022] Open
Abstract
Savings have been described as the ability of healthy humans to relearn a previously acquired motor skill faster than the first time, which in the context of motor adaptation suggests that the learning rate in the brain could be adjusted when a perturbation is recognized. Alternatively, it has been argued that apparent savings were the consequence of a distinct process that instead of reflecting a change in the learning rate, revealed an explicit re-aiming strategy. Based on recent evidence that feedback adaptation may be central to both planning and control, we hypothesized that this component could genuinely accelerate relearning in human adaptation to force fields (FFs) during reaching. Consistent with our hypothesis, we observed that on re-exposure to a previously learned FF, the very first movement performed by healthy volunteers in the relearning context was better adapted to the external disturbance, and this occurred without any anticipation or cognitive strategy because the relearning session was started unexpectedly. We conclude that feedback adaptation is a medium by which the nervous system can genuinely accelerate learning across movements.
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20
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Coltman SK, van Beers RJ, Medendorp WP, Gribble PL. Sensitivity to error during visuomotor adaptation is similarly modulated by abrupt, gradual and random perturbation schedules. J Neurophysiol 2021; 126:934-945. [PMID: 34379553 DOI: 10.1152/jn.00269.2021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
It has been suggested that sensorimotor adaptation involves at least two processes (i.e., fast and slow) that differ in retention and error sensitivity. Previous work has shown that repeated exposure to an abrupt force field perturbation results in greater error sensitivity for both the fast and slow processes. While this implies that the faster relearning is associated with increased error sensitivity, it remains unclear what aspects of prior experience modulate error sensitivity. In the present study, we manipulated the initial training using different perturbation schedules, thought to differentially affect fast and slow learning processes based on error magnitude, and then observed what effect prior learning had on subsequent adaptation. During initial training of a visuomotor rotation task, we exposed three groups of participants to either an abrupt, a gradual, or a random perturbation schedule. During a testing session, all three groups were subsequently exposed to an abrupt perturbation schedule. Comparing the two sessions of the control group who experienced repetition of the same perturbation, we found an increased error sensitivity for both processes. We found that the error sensitivity was increased for both the fast and slow processes, with no reliable changes in the retention, for both the gradual and structural learning groups when compared to the first session of the control group. We discuss the findings in the context of how fast and slow learning processes respond to a history of errors.
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Affiliation(s)
- Susan K Coltman
- Graduate Program in Neuroscience, Western University, London, ON, Canada.,Brain and Mind Institute, Western University, London, ON, Canada.,Department of Psychology, Western University, London, ON, Canada
| | - Robert J van Beers
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands.,Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - W Pieter Medendorp
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Paul L Gribble
- Brain and Mind Institute, Western University, London, ON, Canada.,Department of Psychology, Western University, London, ON, Canada.,Department of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.,Haskins Laboratories, New Haven CT, USA
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21
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Qiu S, Mei G. Spontaneous recovery of adaptation aftereffects of natural facial categories. Vision Res 2021; 188:202-210. [PMID: 34365177 DOI: 10.1016/j.visres.2021.07.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 07/07/2021] [Accepted: 07/23/2021] [Indexed: 10/20/2022]
Abstract
Adaptation to a natural face attribute such as a happy face can bias the perception of a subsequent face in this dimension such as a neutral face. Such face adaptation aftereffects have been widely found in many natural facial categories. However, how temporally tuned mechanisms could control the temporal dynamics of natural face adaptation aftereffects remains unknown. To address the question, we used a deadaptation paradigm to examine whether the spontaneous recovery of natural facial aftereffects would emerge in four natural facial categories including variable categories (emotional expressions in Experiment 1 and eye gaze in Experiment 2) and invariable categories (facial gender in Experiment 3 and facial identity in Experiment 4). In the deadaptation paradigm, participants adapted to a face with an extreme attribute (such as a 100% angry face in Experiment 1) for a relatively long duration, and then deadapted to a face with an opposite extreme attribute (such as a 100% happy face in Experiment 1) for a relatively short duration. The time courses of face adaptation aftereffects were measured using a top-up manner. Deadaptation only masked the effects of initial longer-lasting adaptation, and the spontaneous recovery of adaptation aftereffects was observed at the post-test stage for all four natural facial categories. These results likely indicate that the temporal dynamics of adaptation aftereffects of natural facial categories may be controlled by multiple temporally tuned mechanisms.
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Affiliation(s)
- Shiming Qiu
- School of Psychology, Guizhou Normal University, Guiyang, PR China
| | - Gaoxing Mei
- School of Psychology, Guizhou Normal University, Guiyang, PR China.
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22
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Albert ST, Jang J, Sheahan HR, Teunissen L, Vandevoorde K, Herzfeld DJ, Shadmehr R. An implicit memory of errors limits human sensorimotor adaptation. Nat Hum Behav 2021; 5:920-934. [PMID: 33542527 DOI: 10.1038/s41562-020-01036-x] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 11/24/2020] [Indexed: 01/30/2023]
Abstract
During extended motor adaptation, learning appears to saturate despite persistence of residual errors. This adaptation limit is not fixed but varies with perturbation variance; when variance is high, residual errors become larger. These changes in total adaptation could relate to either implicit or explicit learning systems. Here, we found that when adaptation relied solely on the explicit system, residual errors disappeared and learning was unaltered by perturbation variability. In contrast, when learning depended entirely, or in part, on implicit learning, residual errors reappeared. Total implicit adaptation decreased in the high-variance environment due to changes in error sensitivity, not in forgetting. These observations suggest a model in which the implicit system becomes more sensitive to errors when they occur in a consistent direction. Thus, residual errors in motor adaptation are at least in part caused by an implicit learning system that modulates its error sensitivity in response to the consistency of past errors.
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Affiliation(s)
- Scott T Albert
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA.
| | - Jihoon Jang
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Hannah R Sheahan
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Lonneke Teunissen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Koenraad Vandevoorde
- Leuven Brain Institute, KU Leuven, Leuven, Belgium
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - David J Herzfeld
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Reza Shadmehr
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
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23
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Kumar N, van Vugt FT, Ostry DJ. Recognition memory for human motor learning. Curr Biol 2021; 31:1678-1686.e3. [PMID: 33667372 DOI: 10.1016/j.cub.2021.01.097] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 12/26/2020] [Accepted: 01/27/2021] [Indexed: 11/19/2022]
Abstract
Motor skill retention is typically measured by asking participants to reproduce previously learned movements from memory. The analog of this retention test (recall memory) in human verbal memory is known to underestimate how much learning is actually retained. Here we asked whether information about previously learned movements, which can no longer be reproduced, is also retained. Following visuomotor adaptation, we used tests of recall that involved reproduction of previously learned movements and tests of recognition in which participants were asked whether a candidate limb displacement, produced by a robot arm held by the subject, corresponded to a movement direction that was experienced during active training. The main finding was that 24 h after training, estimates of recognition memory were about twice as accurate as those of recall memory. Thus, there is information about previously learned movements that is not retrieved using recall testing but can be accessed in tests of recognition. We conducted additional tests to assess whether, 24 h after learning, recall for previously learned movements could be improved by presenting passive movements as retrieval cues. These tests were conducted immediately prior to recall testing and involved the passive playback of a small number of movements, which were spread across the workspace and included both adapted and baseline movements, without being marked as such. This technique restored recall memory for movements to levels close to those of recognition memory performance. Thus, somatic information may enable retrieval of otherwise inaccessible motor memories.
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Affiliation(s)
- Neeraj Kumar
- Centre for Brain and Cognitive Sciences, Indian Institute of Technology Gandhinagar, Gujarat 382355, India; Department of Psychology, McGill University, Montreal, QC H3A1G1, Canada; Department of Liberal Arts, Indian Institute of Technology Hyderabad, Telangana 502285, India
| | - Floris T van Vugt
- Department of Psychology, McGill University, Montreal, QC H3A1G1, Canada; Haskins Laboratories, New Haven, CT 06511, USA; Department of Psychology, University of Montreal, Montreal, QC H3T 1J4, Canada
| | - David J Ostry
- Department of Psychology, McGill University, Montreal, QC H3A1G1, Canada; Haskins Laboratories, New Haven, CT 06511, USA.
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24
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Avraham G, Morehead JR, Kim HE, Ivry RB. Reexposure to a sensorimotor perturbation produces opposite effects on explicit and implicit learning processes. PLoS Biol 2021; 19:e3001147. [PMID: 33667219 PMCID: PMC7968744 DOI: 10.1371/journal.pbio.3001147] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 03/17/2021] [Accepted: 02/15/2021] [Indexed: 12/31/2022] Open
Abstract
The motor system demonstrates an exquisite ability to adapt to changes in the environment and to quickly reset when these changes prove transient. If similar environmental changes are encountered in the future, learning may be faster, a phenomenon known as savings. In studies of sensorimotor learning, a central component of savings is attributed to the explicit recall of the task structure and appropriate compensatory strategies. Whether implicit adaptation also contributes to savings remains subject to debate. We tackled this question by measuring, in parallel, explicit and implicit adaptive responses in a visuomotor rotation task, employing a protocol that typically elicits savings. While the initial rate of learning was faster in the second exposure to the perturbation, an analysis decomposing the 2 processes showed the benefit to be solely associated with explicit re-aiming. Surprisingly, we found a significant decrease after relearning in aftereffect magnitudes during no-feedback trials, a direct measure of implicit adaptation. In a second experiment, we isolated implicit adaptation using clamped visual feedback, a method known to eliminate the contribution of explicit learning processes. Consistent with the results of the first experiment, participants exhibited a marked reduction in the adaptation function, as well as an attenuated aftereffect when relearning from the clamped feedback. Motivated by these results, we reanalyzed data from prior studies and observed a consistent, yet unappreciated pattern of attenuation of implicit adaptation during relearning. These results indicate that explicit and implicit sensorimotor processes exhibit opposite effects upon relearning: Explicit learning shows savings, while implicit adaptation becomes attenuated.
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Affiliation(s)
- Guy Avraham
- Department of Psychology, University of California, Berkeley, Berkeley, California, United States of America
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
| | - J. Ryan Morehead
- School of Psychology, University of Leeds, Leeds, United Kingdom
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, United States of America
| | - Hyosub E. Kim
- Department of Physical Therapy, University of Delaware, Newark, Delaware, United States of America
- Department of Psychological and Brain Sciences, University of Delaware, Newark, Delaware, United States of America
| | - Richard B. Ivry
- Department of Psychology, University of California, Berkeley, Berkeley, California, United States of America
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
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25
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Forano M, Franklin DW. Timescales of motor memory formation in dual-adaptation. PLoS Comput Biol 2020; 16:e1008373. [PMID: 33075047 PMCID: PMC7595703 DOI: 10.1371/journal.pcbi.1008373] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 10/29/2020] [Accepted: 09/09/2020] [Indexed: 11/19/2022] Open
Abstract
The timescales of adaptation to novel dynamics are well explained by a dual-rate model with slow and fast states. This model can predict interference, savings and spontaneous recovery, but cannot account for adaptation to multiple tasks, as each new task drives unlearning of the previously learned task. Nevertheless, in the presence of appropriate contextual cues, humans are able to adapt simultaneously to opposing dynamics. Consequently this model was expanded, suggesting that dual-adaptation occurs through a single fast process and multiple slow processes. However, such a model does not predict spontaneous recovery within dual-adaptation. Here we assess the existence of multiple fast processes by examining the presence of spontaneous recovery in two experimental variations of an adaptation-de-adaptation-error-clamp paradigm within dual-task adaptation in humans. In both experiments, evidence for spontaneous recovery towards the initially learned dynamics (A) was found in the error-clamp phase, invalidating the one-fast-two-slow dual-rate model. However, as adaptation is not only constrained to two timescales, we fit twelve multi-rate models to the experimental data. BIC model comparison again supported the existence of two fast processes, but extended the timescales to include a third rate: the ultraslow process. Even within our single day experiment, we found little evidence for decay of the learned memory over several hundred error-clamp trials. Overall, we show that dual-adaptation can be best explained by a two-fast-triple-rate model over the timescales of adaptation studied here. Longer term learning may require even slower timescales, explaining why we never forget how to ride a bicycle. Retaining motor skills is crucial to perform basic daily life tasks. However we still have limited understanding of the computational structure of these motor memories, an understanding that is critical for designing rehabilitation. Here we demonstrate that learning any task involves adaptation of independent fast, slow and ultraslow processes to build a motor memory. The selection of the appropriate motor memory is gated through a contextual cue. Together this work extends our understanding of the architecture of motor memories, by merging disparate computational theories to propose a new model.
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Affiliation(s)
- Marion Forano
- Neuromuscular Diagnostics, Department of Sport and Health Sciences, Technical University of Munich, Germany
| | - David W. Franklin
- Neuromuscular Diagnostics, Department of Sport and Health Sciences, Technical University of Munich, Germany
- * E-mail:
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26
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Coltman SK, Gribble PL. Time course of changes in the long-latency feedback response parallels the fast process of short-term motor adaptation. J Neurophysiol 2020; 124:388-399. [PMID: 32639925 DOI: 10.1152/jn.00286.2020] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Adapting to novel dynamics involves modifying both feedforward and feedback control. We investigated whether the motor system alters feedback responses during adaptation to a novel force field in a manner similar to adjustments in feedforward control. We simultaneously tracked the time course of both feedforward and feedback systems via independent probes during a force field adaptation task. Participants (n = 35) grasped the handle of a robotic manipulandum and performed reaches to a visual target while the hand and arm were occluded. We introduced an abrupt counterclockwise velocity-dependent force field during a block of reaching trials. We measured movement kinematics and shoulder and elbow muscle activity with surface EMG electrodes. We tracked the feedback stretch response throughout the task. Using force channel trials, we measured overall learning, which was later decomposed into a fast and slow process. We found that the long-latency feedback response (LLFR) was upregulated in the early stages of learning and was correlated with the fast component of feedforward adaptation. The change in feedback response was specific to the long-latency epoch (50-100 ms after muscle stretch) and was observed only in the triceps muscle, which was the muscle required to counter the force field during adaptation. The similarity in time course for the LLFR and the estimated time course of the fast process suggests both are supported by common neural circuits. While some propose that the fast process reflects an explicit strategy, we argue instead that it may be a proxy for the feedback controller.NEW & NOTEWORTHY We investigated whether changes in the feedback stretch response were related to the proposed fast and slow processes of motor adaptation. We found that the long-latency component of the feedback stretch response was upregulated in the early stages of learning and the time course was correlated with the fast process. While some propose that the fast process reflects an explicit strategy, we argue instead that it may be a proxy for the feedback controller.
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Affiliation(s)
- Susan K Coltman
- Graduate Program in Neuroscience, Western University, London, Ontario, Canada.,Brain and Mind Institute, Western University, London, Ontario, Canada.,Department of Psychology, Western University, London, Ontario, Canada
| | - Paul L Gribble
- Brain and Mind Institute, Western University, London, Ontario, Canada.,Department of Psychology, Western University, London, Ontario, Canada.,Department of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Haskins Laboratories, New Haven, Connecticut
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A Very Fast Time Scale of Human Motor Adaptation: Within Movement Adjustments of Internal Representations during Reaching. eNeuro 2020; 7:ENEURO.0149-19.2019. [PMID: 31949026 PMCID: PMC7004489 DOI: 10.1523/eneuro.0149-19.2019] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 12/19/2019] [Accepted: 12/23/2019] [Indexed: 12/20/2022] Open
Abstract
Humans and other animals adapt motor commands to predictable disturbances within tens of trials in laboratory conditions. A central question is how does the nervous system adapt to disturbances in natural conditions when exactly the same movements cannot be practiced several times. Because motor commands and sensory feedback together carry continuous information about limb dynamics, we hypothesized that the nervous system could adapt to unexpected disturbances online. Humans and other animals adapt motor commands to predictable disturbances within tens of trials in laboratory conditions. A central question is how does the nervous system adapt to disturbances in natural conditions when exactly the same movements cannot be practiced several times. Because motor commands and sensory feedback together carry continuous information about limb dynamics, we hypothesized that the nervous system could adapt to unexpected disturbances online. We tested this hypothesis in two reaching experiments during which velocity-dependent force fields (FFs) were randomly applied. We found that within-movement feedback corrections gradually improved, despite the fact that the perturbations were unexpected. Moreover, when participants were instructed to stop at a via-point, the application of a FF prior to the via-point induced mirror-image after-effects after the via-point, consistent with within-trial adaptation to the unexpected dynamics. These findings suggest a fast time-scale of motor learning, which complements feedback control and supports adaptation of an ongoing movement.
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28
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Nguyen KP, Zhou W, McKenna E, Colucci-Chang K, Bray LCJ, Hosseini EA, Alhussein L, Rezazad M, Joiner WM. The 24-h savings of adaptation to novel movement dynamics initially reflects the recall of previous performance. J Neurophysiol 2019; 122:933-946. [PMID: 31291156 DOI: 10.1152/jn.00569.2018] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Humans rapidly adapt reaching movements in response to perturbations (e.g., manipulations of movement dynamics or visual feedback). Following a break, when reexposed to the same perturbation, subjects demonstrate savings, a faster learning rate compared with the time course of initial training. Although this has been well studied, there are open questions on the extent early savings reflects the rapid recall of previous performance. To address this question, we examined how the properties of initial training (duration and final adaptive state) influence initial single-trial adaptation to force-field perturbations when training sessions were separated by 24 h. There were two main groups that were distinct based on the presence or absence of a washout period at the end of day 1 (with washout vs. without washout). We also varied the training duration on day 1 (15, 30, 90, or 160 training trials), resulting in 8 subgroups of subjects. We show that single-trial adaptation on day 2 scaled with training duration, even for similar asymptotic levels of learning on day 1 of training. Interestingly, the temporal force profile following the first perturbation on day 2 matched that at the end of day 1 for the longest training duration group that did not complete the washout. This correspondence persisted but was significantly lower for shorter training durations and the washout subject groups. Collectively, the results suggest that the adaptation observed very early in reexposure results from the rapid recall of the previously learned motor recalibration but is highly dependent on the initial training duration and final adaptive state.NEW & NOTEWORTHY The extent initial readaptation reflects the recall of previous motor performance is largely unknown. We examined early single-trial force-field adaptation on the second day of training and distinguished initial retention from recall. We found that the single-trial adaptation following the 24-h break matched that at the end of the first day, but this recall was modified by the training duration and final level of learning on the first day of training.
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Affiliation(s)
- Katrina P Nguyen
- Department of Bioengineering, George Mason University, Fairfax, Virginia
| | - Weiwei Zhou
- Department of Bioengineering, George Mason University, Fairfax, Virginia
| | - Erin McKenna
- Department of Neuroscience, George Mason University, Fairfax, Virginia
| | | | | | - Eghbal A Hosseini
- Department of Bioengineering, George Mason University, Fairfax, Virginia
| | - Laith Alhussein
- Department of Bioengineering, George Mason University, Fairfax, Virginia
| | - Meena Rezazad
- Department of Bioengineering, George Mason University, Fairfax, Virginia
| | - Wilsaan M Joiner
- Department of Bioengineering, George Mason University, Fairfax, Virginia.,Department of Neuroscience, George Mason University, Fairfax, Virginia.,Department of Neurobiology, Physiology and Behavior, University of California, Davis, California
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