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Kalidindi HT, Crevecoeur F. Task-dependent coarticulation of movement sequences. eLife 2024; 13:RP96854. [PMID: 39331027 PMCID: PMC11434614 DOI: 10.7554/elife.96854] [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] [Indexed: 09/28/2024] Open
Abstract
Combining individual actions into sequences is a hallmark of everyday activities. Classical theories propose that the motor system forms a single specification of the sequence as a whole, leading to the coarticulation of the different elements. In contrast, recent neural recordings challenge this idea and suggest independent execution of each element specified separately. Here, we show that separate or coarticulated sequences can result from the same task-dependent controller, without implying different representations in the brain. Simulations show that planning for multiple reaches simultaneously allows separate or coarticulated sequences depending on instructions about intermediate goals. Human experiments in a two-reach sequence task validated this model. Furthermore, in co-articulated sequences, the second goal influenced long-latency stretch responses to external loads applied during the first reach, demonstrating the involvement of the sensorimotor network supporting fast feedback control. Overall, our study establishes a computational framework for sequence production that highlights the importance of feedback control in this essential motor skill.
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Affiliation(s)
- Hari Teja Kalidindi
- Institute for Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Université Catholique de Louvain, Louvain-la-Neuve, Belgium
- Institute of Neuroscience (IoNS), Université Catholique de Louvain, Brussels, Belgium
| | - Frederic Crevecoeur
- Institute for Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Université Catholique de Louvain, Louvain-la-Neuve, Belgium
- Institute of Neuroscience (IoNS), Université Catholique de Louvain, Brussels, Belgium
- WEL Research Institute, Wavre, Belgium
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Katayama R, Shiraki R, Ishii S, Yoshida W. Belief inference for hierarchical hidden states in spatial navigation. Commun Biol 2024; 7:614. [PMID: 38773301 PMCID: PMC11109253 DOI: 10.1038/s42003-024-06316-0] [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: 10/22/2023] [Accepted: 05/10/2024] [Indexed: 05/23/2024] Open
Abstract
Uncertainty abounds in the real world, and in environments with multiple layers of unobservable hidden states, decision-making requires resolving uncertainties based on mutual inference. Focusing on a spatial navigation problem, we develop a Tiger maze task that involved simultaneously inferring the local hidden state and the global hidden state from probabilistically uncertain observation. We adopt a Bayesian computational approach by proposing a hierarchical inference model. Applying this to human task behaviour, alongside functional magnetic resonance brain imaging, allows us to separate the neural correlates associated with reinforcement and reassessment of belief in hidden states. The imaging results also suggest that different layers of uncertainty differentially involve the basal ganglia and dorsomedial prefrontal cortex, and that the regions responsible are organised along the rostral axis of these areas according to the type of inference and the level of abstraction of the hidden state, i.e. higher-order state inference involves more anterior parts.
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Affiliation(s)
- Risa Katayama
- Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan.
- Department of AI-Brain Integration, Advanced Telecommunications Research Institute International, Kyoto, 619-0288, Japan.
| | - Ryo Shiraki
- Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan
| | - Shin Ishii
- Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan
- Neural Information Analysis Laboratories, Advanced Telecommunications Research Institute International, Kyoto, 619-0288, Japan
- International Research Center for Neurointelligence, the University of Tokyo, Tokyo, 113-0033, Japan
| | - Wako Yoshida
- Department of Neural Computation for Decision-Making, Advanced Telecommunications Research Institute International, Kyoto, 619-0288, Japan
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
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Kalidindi HT, Crevecoeur F. Human reaching control in dynamic environments. Curr Opin Neurobiol 2023; 83:102810. [PMID: 37950956 DOI: 10.1016/j.conb.2023.102810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 10/09/2023] [Accepted: 10/19/2023] [Indexed: 11/13/2023]
Abstract
Closed-loop models of movement control have attracted growing interest in how the nervous system transforms sensory information into motor commands, and several brain structures have been identified as neural substrates for these computational operations. Recently, several studies have focused on how these models need to be updated when environmental parameters change. Current evidence suggests that when the task changes, rapid control updates enable flexible modifications of current actions and online decisions. At the same time, when movement dynamics change, humans use different strategies based on a combination of adaptation and modulation of controller sensitivity to exogenous perturbations (robust control). This review proposes a unified framework to capture these results based on online estimation of model parameters with dynamic updates in control. The reviewed studies also identify the time scales of associated behavioral mechanisms to guide future research on the neural basis of movement control.
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Affiliation(s)
- Hari T Kalidindi
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics, University of Louvain (UCLouvain), Belgium; Institute of Neuroscience, UCLouvain, Belgium
| | - Frédéric Crevecoeur
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics, University of Louvain (UCLouvain), Belgium; Institute of Neuroscience, UCLouvain, Belgium.
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Langsdorf L, Goehringer F, Schween R, Schenk T, Hegele M. Additional cognitive load decreases performance but not adaptation to a visuomotor transformation. Acta Psychol (Amst) 2022; 226:103586. [PMID: 35427929 DOI: 10.1016/j.actpsy.2022.103586] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 03/16/2022] [Accepted: 04/06/2022] [Indexed: 12/22/2022] Open
Abstract
Dual-task paradigms are procedures for investigating interference with two tasks performed simultaneously. Studies that previously addressed dual-task paradigms within a visuomotor reaching task yielded mixed results. While some of the studies found evidence of cognitive interference, called dual-task costs, other studies did not. We assume that dual-task costs only manifest themselves within the explicit component of adaptation, as it involves cognitive resources for processing. We suspect the divergent findings to be due to the lack of differentiation between the explicit and implicit component. In this study, we aimed to investigate how a cognitive secondary task affects visuomotor adaptation overall and its different components, both during and after adaptation. In a series of posttests, we examined the explicit and implicit components separately. Eighty participants performed a center-outward reaching movement with a 30° cursor perturbation. Participants were either assigned to a single task group (ST) or a dual-task group (DT) with an additional auditory 1-back task. To further enhance our predicted effect of dual-task interference on the explicit component, we added a visual feedback delay condition to both groups (ST/DTDEL). In the other condition, participants received visual feedback immediately after movement termination (ST/DTNoDEL). While there were clear dual-task costs during the practice phase, there were no dual-task effects on any of the posttest measures. On one hand, our findings suggest that dual-task costs in visuomotor adaptation tasks can occur with sufficient cognitive demand, and on the other hand, that cognitive constraints may affect motor performance but not necessarily motor adaptation.
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Mathew J, Crevecoeur F. Adaptive Feedback Control in Human Reaching Adaptation to Force Fields. Front Hum Neurosci 2022; 15:742608. [PMID: 35027886 PMCID: PMC8751623 DOI: 10.3389/fnhum.2021.742608] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 11/29/2021] [Indexed: 11/26/2022] Open
Abstract
Sensorimotor adaptation is a central function of the nervous system, as it allows humans and other animals to flexibly anticipate their interaction with the environment. In the context of human reaching adaptation to force fields, studies have traditionally separated feedforward (FF) and feedback (FB) processes involved in the improvement of behavior. Here, we review computational models of FF adaptation to force fields and discuss them in light of recent evidence highlighting a clear involvement of feedback control. Instead of a model in which FF and FB mechanisms adapt in parallel, we discuss how online adaptation in the feedback control system can explain both trial-by-trial adaptation and improvements in online motor corrections. Importantly, this computational model combines sensorimotor control and short-term adaptation in a single framework, offering novel perspectives for our understanding of human reaching adaptation and control.
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Affiliation(s)
- James Mathew
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Catholic University of Louvain, Louvain-la-Neuve, Belgium.,Institute of Neuroscience (IoNS), Catholic University of Louvain, Louvain-la-Neuve, Belgium
| | - Frédéric Crevecoeur
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Catholic University of Louvain, Louvain-la-Neuve, Belgium.,Institute of Neuroscience (IoNS), Catholic University of Louvain, Louvain-la-Neuve, Belgium
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Abstract
In several papers published in Biological Cybernetics in the 1980s and 1990s, Kawato and colleagues proposed computational models explaining how internal models are acquired in the cerebellum. These models were later supported by neurophysiological experiments using monkeys and neuroimaging experiments involving humans. These early studies influenced neuroscience from basic, sensory-motor control to higher cognitive functions. One of the most perplexing enigmas related to internal models is to understand the neural mechanisms that enable animals to learn large-dimensional problems with so few trials. Consciousness and metacognition-the ability to monitor one's own thoughts, may be part of the solution to this enigma. Based on literature reviews of the past 20 years, here we propose a computational neuroscience model of metacognition. The model comprises a modular hierarchical reinforcement-learning architecture of parallel and layered, generative-inverse model pairs. In the prefrontal cortex, a distributed executive network called the "cognitive reality monitoring network" (CRMN) orchestrates conscious involvement of generative-inverse model pairs in perception and action. Based on mismatches between computations by generative and inverse models, as well as reward prediction errors, CRMN computes a "responsibility signal" that gates selection and learning of pairs in perception, action, and reinforcement learning. A high responsibility signal is given to the pairs that best capture the external world, that are competent in movements (small mismatch), and that are capable of reinforcement learning (small reward-prediction error). CRMN selects pairs with higher responsibility signals as objects of metacognition, and consciousness is determined by the entropy of responsibility signals across all pairs. This model could lead to new-generation AI, which exhibits metacognition, consciousness, dimension reduction, selection of modules and corresponding representations, and learning from small samples. It may also lead to the development of a new scientific paradigm that enables the causal study of consciousness by combining CRMN and decoded neurofeedback.
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Affiliation(s)
- Mitsuo Kawato
- ATR Brain Information Communication Research Group, Computational Neuroscience Laboratory, Hikaridai, Kyoto, 619-0288 Japan
| | - Aurelio Cortese
- ATR Brain Information Communication Research Group, Computational Neuroscience Laboratory, Hikaridai, Kyoto, 619-0288 Japan
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Ikegami T, Ganesh G, Gibo TL, Yoshioka T, Osu R, Kawato M. Hierarchical motor adaptations negotiate failures during force field learning. PLoS Comput Biol 2021; 17:e1008481. [PMID: 33872304 PMCID: PMC8084335 DOI: 10.1371/journal.pcbi.1008481] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 04/29/2021] [Accepted: 03/24/2021] [Indexed: 11/19/2022] Open
Abstract
Humans have the amazing ability to learn the dynamics of the body and environment to develop motor skills. Traditional motor studies using arm reaching paradigms have viewed this ability as the process of ‘internal model adaptation’. However, the behaviors have not been fully explored in the case when reaches fail to attain the intended target. Here we examined human reaching under two force fields types; one that induces failures (i.e., target errors), and the other that does not. Our results show the presence of a distinct failure-driven adaptation process that enables quick task success after failures, and before completion of internal model adaptation, but that can result in persistent changes to the undisturbed trajectory. These behaviors can be explained by considering a hierarchical interaction between internal model adaptation and the failure-driven adaptation of reach direction. Our findings suggest that movement failure is negotiated using hierarchical motor adaptations by humans. How do we improve actions after a movement failure? Although negotiating movement failures is obviously crucial, previous motor-control studies have predominantly examined human movement adaptations in the absence of failures, and it remains unclear how failures affect subsequent movement adaptations. Here we examined this issue by developing a novel force field adaptation task where the hand movement during an arm reaching is perturbed by novel forces that induce a large target error, that is a failure. Our experimental observation and computational modeling show that, in addition to the popular ‘internal model learning’ process of motor adaptations, humans also utilize a ‘failure-negotiating’ process, that enables them to quickly improve movements in the presence of failure, even at the expense of increased arm trajectory deflections, which are subsequently reduced gradually with training after the achievement of the task success. Our results suggest that a hierarchical interaction between these two processes is a key for humans to negotiate movement failures.
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Affiliation(s)
- Tsuyoshi Ikegami
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Osaka, Japan
- Brain Information Communication Research Laboratory Group, ATR, Kyoto, Japan
- Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan
- * E-mail:
| | - Gowrishankar Ganesh
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Osaka, Japan
- Brain Information Communication Research Laboratory Group, ATR, Kyoto, Japan
- Centre National de la Recherche Scientifique (CNRS), Universite Montpellier (UM) Laboratoire d’Informatique, de Robotique et de Microelectronique de, Montpellier (LIRMM), Montpellier, France
| | - Tricia L. Gibo
- Brain Information Communication Research Laboratory Group, ATR, Kyoto, Japan
- Emergo by UL, Utrecht, The Netherlands
| | - Toshinori Yoshioka
- Brain Information Communication Research Laboratory Group, ATR, Kyoto, Japan
| | - Rieko Osu
- Brain Information Communication Research Laboratory Group, ATR, Kyoto, Japan
- Faculty of Human Sciences, Waseda University, Saitama, Japan
| | - Mitsuo Kawato
- Brain Information Communication Research Laboratory Group, ATR, Kyoto, Japan
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