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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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Suglia V, Brunetti A, Pasquini G, Caputo M, Marvulli TM, Sibilano E, Della Bella S, Carrozza P, Beni C, Naso D, Monaco V, Cristella G, Bevilacqua V, Buongiorno D. A Serious Game for the Assessment of Visuomotor Adaptation Capabilities during Locomotion Tasks Employing an Embodied Avatar in Virtual Reality. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115017. [PMID: 37299744 DOI: 10.3390/s23115017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 05/17/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023]
Abstract
The study of visuomotor adaptation (VMA) capabilities has been encompassed in various experimental protocols aimed at investigating human motor control strategies and/or cognitive functions. VMA-oriented frameworks can have clinical applications, primarily in the investigation and assessment of neuromotor impairments caused by conditions such as Parkinson's disease or post-stroke, which affect the lives of tens of thousands of people worldwide. Therefore, they can enhance the understanding of the specific mechanisms of such neuromotor disorders, thus being a potential biomarker for recovery, with the aim of being integrated with conventional rehabilitative programs. Virtual Reality (VR) can be entailed in a framework targeting VMA since it allows the development of visual perturbations in a more customizable and realistic way. Moreover, as has been demonstrated in previous works, a serious game (SG) can further increase engagement thanks to the use of full-body embodied avatars. Most studies implementing VMA frameworks have focused on upper limb tasks and have utilized a cursor as visual feedback for the user. Hence, there is a paucity in the literature about VMA-oriented frameworks targeting locomotion tasks. In this article, the authors present the design, development, and testing of an SG-based framework that addresses VMA in a locomotion activity by controlling a full-body moving avatar in a custom VR environment. This workflow includes a set of metrics to quantitatively assess the participants' performance. Thirteen healthy children were recruited to evaluate the framework. Several quantitative comparisons and analyses were run to validate the different types of introduced visuomotor perturbations and to evaluate the ability of the proposed metrics to describe the difficulty caused by such perturbations. During the experimental sessions, it emerged that the system is safe, easy to use, and practical in a clinical setting. Despite the limited sample size, which represents the main limitation of the study and can be compensated for with future recruitment, the authors claim the potential of this framework as a useful instrument for quantitatively assessing either motor or cognitive impairments. The proposed feature-based approach gives several objective parameters as additional biomarkers that can integrate the conventional clinical scores. Future studies might investigate the relation between the proposed biomarkers and the clinical scores for specific disorders such as Parkinson's disease and cerebral palsy.
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Affiliation(s)
- Vladimiro Suglia
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, 70126 Bari, Italy
| | - Antonio Brunetti
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, 70126 Bari, Italy
- Apulian Bioengineering s.r.l., 70026 Modugno, Italy
| | - Guido Pasquini
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, 50143 Florence, Italy
| | - Mariapia Caputo
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, 70126 Bari, Italy
| | - Tommaso Maria Marvulli
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, 70126 Bari, Italy
| | - Elena Sibilano
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, 70126 Bari, Italy
| | | | - Paola Carrozza
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, 50143 Florence, Italy
| | - Chiara Beni
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, 50143 Florence, Italy
| | - David Naso
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, 70126 Bari, Italy
| | - Vito Monaco
- The Biorobotics Institute, Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
| | | | - Vitoantonio Bevilacqua
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, 70126 Bari, Italy
- Apulian Bioengineering s.r.l., 70026 Modugno, Italy
| | - Domenico Buongiorno
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, 70126 Bari, Italy
- Apulian Bioengineering s.r.l., 70026 Modugno, Italy
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