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Falandays JB, Yoshimi J, Warren WH, Spivey MJ. A potential mechanism for Gibsonian resonance: behavioral entrainment emerges from local homeostasis in an unsupervised reservoir network. Cogn Neurodyn 2024; 18:1811-1834. [PMID: 39104666 PMCID: PMC11297877 DOI: 10.1007/s11571-023-09988-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/15/2023] [Accepted: 06/25/2023] [Indexed: 08/07/2024] Open
Abstract
While the cognitivist school of thought holds that the mind is analogous to a computer, performing logical operations over internal representations, the tradition of ecological psychology contends that organisms can directly "resonate" to information for action and perception without the need for a representational intermediary. The concept of resonance has played an important role in ecological psychology, but it remains a metaphor. Supplying a mechanistic account of resonance requires a non-representational account of central nervous system (CNS) dynamics. Towards this, we present a series of simple models in which a reservoir network with homeostatic nodes is used to control a simple agent embedded in an environment. This network spontaneously produces behaviors that are adaptive in each context, including (1) visually tracking a moving object, (2) substantially above-chance performance in the arcade game Pong, (2) and avoiding walls while controlling a mobile agent. Upon analyzing the dynamics of the networks, we find that behavioral stability can be maintained without the formation of stable or recurring patterns of network activity that could be identified as neural representations. These results may represent a useful step towards a mechanistic grounding of resonance and a view of the CNS that is compatible with ecological psychology.
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Affiliation(s)
| | - Jeffrey Yoshimi
- Department of Cognitive and Information Sciences, University of California, Merced, Merced, USA
| | - William H. Warren
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, USA
| | - Michael J. Spivey
- Department of Cognitive and Information Sciences, University of California, Merced, Merced, USA
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Giuriato M, Filipas L, Crociani M, Carnevale Pellino V, Vandoni M, Gallo G, La Torre A, Rossi C, Lovecchio N, Codella R. Inter-Trial Rest Interval Affects Learning Throwing Skills among Adolescents. J Mot Behav 2023; 56:132-138. [PMID: 37828754 DOI: 10.1080/00222895.2023.2265869] [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: 04/04/2023] [Accepted: 09/06/2023] [Indexed: 10/14/2023]
Abstract
Newly acquired motor skills can be critically driven by different rest periods during practice. Specifically, in the initial stages of motor skill acquisition, the interval between individual trials plays a pivotal role in facilitating effective motor performance, such as in the case of throwing. The objective of this research was to determine the optimal inter-trial rest period promoting efficient motor performance, focusing on two specific motor task actions. In a randomized counterbalanced cross-over research design 169 high-school students aged 14 were studied (M = 150; F = 19). In one block, participants performed 10 basketball free throws with a short rest interval (< 5 s) and 10 other throws with a long rest interval (∼50-60 s). In a second block, they threw a regular size tennis ball into a 1-m diameter circle on the floor at 6.75 m, again throwing 10 times with a short inter-trial rest interval and 10 times with a long inter-trial rest interval. The order of the rest intervals within each block was randomized and counterbalanced. With a repeated measures two-way analysis of variance, greater accuracy seemed to be associated with short intra-set rest intervals as there were significant main effects of both conditions (F1,167 = 368.0, p < 0.001, η2p = 0.271) and resting time (F1,167 = 18.6, p < 0.001, η2p = 0.192) and no significant interaction "condition by time". Fast practice was efficient independently of the complexity of the throwing task, suggesting robust support for schema theory.
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Affiliation(s)
- Matteo Giuriato
- Laboratory of Adapted Motor Activity (LAMA), Department of Public Health, Experimental Medicine and Forensic Science, University of Pavia, Pavia, Italy
| | - Luca Filipas
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
- Department of Endocrinology, Nutrition and Metabolic Diseases, IRCCS MultiMedica, Milan, Italy
| | - Mariele Crociani
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
| | - Vittoria Carnevale Pellino
- Laboratory of Adapted Motor Activity (LAMA), Department of Public Health, Experimental Medicine and Forensic Science, University of Pavia, Pavia, Italy
| | - Matteo Vandoni
- Laboratory of Adapted Motor Activity (LAMA), Department of Public Health, Experimental Medicine and Forensic Science, University of Pavia, Pavia, Italy
| | - Gabriele Gallo
- Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Genoa, Italy
- Centro Polifunzionale di Scienze Motorie, University of Genoa, Genoa, Italy
| | - Antonio La Torre
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
| | - Carlo Rossi
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
| | - Nicola Lovecchio
- Department of Human and Social Sciences, Università di Bergamo, Bergamo, Italy
| | - Roberto Codella
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
- Department of Endocrinology, Nutrition and Metabolic Diseases, IRCCS MultiMedica, Milan, Italy
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Kohsaka H. Linking neural circuits to the mechanics of animal behavior in Drosophila larval locomotion. Front Neural Circuits 2023; 17:1175899. [PMID: 37711343 PMCID: PMC10499525 DOI: 10.3389/fncir.2023.1175899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 06/13/2023] [Indexed: 09/16/2023] Open
Abstract
The motions that make up animal behavior arise from the interplay between neural circuits and the mechanical parts of the body. Therefore, in order to comprehend the operational mechanisms governing behavior, it is essential to examine not only the underlying neural network but also the mechanical characteristics of the animal's body. The locomotor system of fly larvae serves as an ideal model for pursuing this integrative approach. By virtue of diverse investigation methods encompassing connectomics analysis and quantification of locomotion kinematics, research on larval locomotion has shed light on the underlying mechanisms of animal behavior. These studies have elucidated the roles of interneurons in coordinating muscle activities within and between segments, as well as the neural circuits responsible for exploration. This review aims to provide an overview of recent research on the neuromechanics of animal locomotion in fly larvae. We also briefly review interspecific diversity in fly larval locomotion and explore the latest advancements in soft robots inspired by larval locomotion. The integrative analysis of animal behavior using fly larvae could establish a practical framework for scrutinizing the behavior of other animal species.
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Affiliation(s)
- Hiroshi Kohsaka
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Chofu, Tokyo, Japan
- Department of Complexity Science and Engineering, Graduate School of Frontier Science, The University of Tokyo, Chiba, Japan
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Vasung L, Xu J, Abaci-Turk E, Zhou C, Holland E, Barth WH, Barnewolt C, Connolly S, Estroff J, Golland P, Feldman HA, Adalsteinsson E, Grant PE. Cross-Sectional Observational Study of Typical in utero Fetal Movements Using Machine Learning. Dev Neurosci 2022; 45:105-114. [PMID: 36538911 PMCID: PMC10233700 DOI: 10.1159/000528757] [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: 08/09/2022] [Accepted: 11/14/2022] [Indexed: 12/24/2022] Open
Abstract
Early variations of fetal movements are the hallmark of a healthy developing central nervous system. However, there are no automatic methods to quantify the complex 3D motion of the developing fetus in utero. The aim of this prospective study was to use machine learning (ML) on in utero MRI to perform quantitative kinematic analysis of fetal limb movement, assessing the impact of maternal, placental, and fetal factors. In this cross-sectional, observational study, we used 76 sets of fetal (24-40 gestational weeks [GW]) blood oxygenation level-dependent (BOLD) MRI scans of 52 women (18-45 years old) during typical pregnancies. Pregnant women were scanned for 5-10 min while breathing room air (21% O2) and for 5-10 min while breathing 100% FiO2 in supine and/or lateral position. BOLD acquisition time was 20 min in total with effective temporal resolution approximately 3 s. To quantify upper and lower limb kinematics, we used a 3D convolutional neural network previously trained to track fetal key points (wrists, elbows, shoulders, ankles, knees, hips) on similar BOLD time series. Tracking was visually assessed, errors were manually corrected, and the absolute movement time (AMT) for each joint was calculated. To identify variables that had a significant association with AMT, we constructed a mixed-model ANOVA with interaction terms. Fetuses showed significantly longer duration of limb movements during maternal hyperoxia. We also found a significant centrifugal increase of AMT across limbs and significantly longer AMT of upper extremities <31 GW and longer AMT of lower extremities >35 GW. In conclusion, using ML we successfully quantified complex 3D fetal limb motion in utero and across gestation, showing maternal factors (hyperoxia) and fetal factors (gestational age, joint) that impact movement. Quantification of fetal motion on MRI is a potential new biomarker of fetal health and neuromuscular development.
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Affiliation(s)
- Lana Vasung
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Junshen Xu
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts, USA
| | - Esra Abaci-Turk
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Cindy Zhou
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Elizabeth Holland
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - William H Barth
- Department of Obstetrics and Gynecology, Massachusetts General Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Carol Barnewolt
- Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Susan Connolly
- Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Judy Estroff
- Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Polina Golland
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts, USA
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts, USA
- Institute for Medical Engineering and Science, MIT, Cambridge, Massachusetts, USA
| | - Henry A Feldman
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
- Institutional Centers for Clinical and Translational Research, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Elfar Adalsteinsson
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts, USA
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts, USA
- Institute for Medical Engineering and Science, MIT, Cambridge, Massachusetts, USA
| | - P Ellen Grant
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
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