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Huang L, Du F, Huang W, Ren H, Qiu W, Zhang J, Wang Y. Three-stage Dynamic Brain-cognitive Model of Understanding Action Intention Displayed by Human Body Movements. Brain Topogr 2024:10.1007/s10548-024-01061-3. [PMID: 38874853 DOI: 10.1007/s10548-024-01061-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 06/04/2024] [Indexed: 06/15/2024]
Abstract
The ability to comprehend the intention conveyed through human body movements is crucial for effective interpersonal interactions. If people can't understand the intention behind other individuals' isolated or interactive actions, their actions will become meaningless. Psychologists have investigated the cognitive processes and neural representations involved in understanding action intention, yet a cohesive theoretical explanation remains elusive. Hence, we mainly review existing literature related to neural correlates of action intention, and primarily propose a putative Three-stage Dynamic Brain-cognitive Model of understanding action intention, which involves body perception, action identification and intention understanding. Specifically, at the first stage, body parts/shapes are processed by those brain regions such as extrastriate and fusiform body areas; During the second stage, differentiating observed actions relies on configuring relationships between body parts, facilitated by the activation of the Mirror Neuron System; The last stage involves identifying various intention categories, utilizing the Mentalizing System for recruitment, and different activation patterns concerning the nature of the intentions participants dealing with. Finally, we delves into the clinical practice, like intervention training based on a theoretical model for individuals with autism spectrum disorders who encounter difficulties in interpersonal communication.
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Affiliation(s)
- Liang Huang
- Fujian Key Laboratory of Applied Cognition and Personality, Minnan Normal University, Zhangzhou, China.
- Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy.
| | - Fangyuan Du
- Fuzhou University of International Studies and Trade, Fuzhou, China
| | - Wenxin Huang
- Fujian Key Laboratory of Applied Cognition and Personality, Minnan Normal University, Zhangzhou, China
- School of Management, Zhejiang University of Technology, Hangzhou, China
| | - Hanlin Ren
- Third People's Hospital of Zhongshan, Zhongshan, China
| | - Wenzhen Qiu
- Fujian Key Laboratory of Applied Cognition and Personality, Minnan Normal University, Zhangzhou, China
| | - Jiayi Zhang
- Fujian Key Laboratory of Applied Cognition and Personality, Minnan Normal University, Zhangzhou, China
| | - Yiwen Wang
- The School of Economics and Management, Fuzhou University, Fuzhou, China.
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Wang L, Jiang Y. Action observation network: domain-specific or domain-general? Trends Cogn Sci 2023; 27:981-982. [PMID: 37666724 DOI: 10.1016/j.tics.2023.08.012] [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: 08/01/2023] [Accepted: 08/14/2023] [Indexed: 09/06/2023]
Abstract
The action observation network (AON) has traditionally been thought to be dedicated to recognizing animate actions. A recent study by Karakose-Akbiyik et al. invites rethinking this assumption by demonstrating that the AON contains a shared neural code for general events, regardless of whether those events involve animate or inanimate entities.
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Affiliation(s)
- Li Wang
- State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, China.
| | - Yi Jiang
- State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, China.
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Monroy C, Wagner L. Finding Structure in Modern Dance. Cogn Sci 2023; 47:e13375. [PMID: 37950547 DOI: 10.1111/cogs.13375] [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: 11/04/2022] [Revised: 08/24/2023] [Accepted: 10/25/2023] [Indexed: 11/12/2023]
Abstract
Research has shown that both adults and children organize familiar activity into discrete units with consistent boundaries, despite the dynamic, continuous nature of everyday experiences. However, less is known about how observers segment unfamiliar event sequences. In the current study, we took advantage of the novelty that is inherent in modern dance. Modern dance features natural human motion but does not contain canonical goals-therefore, observers cannot recruit prior goal-related knowledge to segment it. Our main aims were to identify whether observers segment modern dance into the steps intended by the dancers, and what types of cues contribute to segmentation under these circumstances. Experiment 1 used a classic event segmentation task and found that adults were able to consistently identify only a few of the dancers' intended steps. Experiment 2 tested adults in an offline labeling task. Results showed that steps which could more easily be labeled offline in Experiment 2 were more likely to be segmented online in Experiment 1.
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Affiliation(s)
| | - Laura Wagner
- Department of Psychology, The Ohio State University
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O’Shea H. Mapping relational links between motor imagery, action observation, action-related language, and action execution. Front Hum Neurosci 2022; 16:984053. [DOI: 10.3389/fnhum.2022.984053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 10/07/2022] [Indexed: 11/06/2022] Open
Abstract
Actions can be physically executed, observed, imagined, or simply thought about. Unifying mental processes, such as simulation, emulation, or predictive processing, are thought to underlie different action types, whether they are mental states, as in the case of motor imagery and action observation, or involve physical execution. While overlapping brain activity is typically observed across different actions which indicates commonalities, research interest is also concerned with investigating the distinct functional components of these action types. Unfortunately, untangling subtleties associated with the neurocognitive bases of different action types is a complex endeavour due to the high dimensional nature of their neural substrate (e.g., any action process is likely to activate multiple brain regions thereby having multiple dimensions to consider when comparing across them). This has impeded progress in action-related theorising and application. The present study addresses this challenge by using the novel approach of multidimensional modeling to reduce the high-dimensional neural substrate of four action-related behaviours (motor imagery, action observation, action-related language, and action execution), find the least number of dimensions that distinguish or relate these action types, and characterise their neurocognitive relational links. Data for the model comprised brain activations for action types from whole-brain analyses reported in 53 published articles. Eighty-two dimensions (i.e., 82 brain regions) for the action types were reduced to a three-dimensional model, that mapped action types in ordination space where the greater the distance between the action types, the more dissimilar they are. A series of one-way ANOVAs and post-hoc comparisons performed on the mean coordinates for each action type in the model showed that across all action types, action execution and concurrent action observation (AO)-motor imagery (MI) were most neurocognitively similar, while action execution and AO were most dissimilar. Most action types were similar on at least one neurocognitive dimension, the exception to this being action-related language. The import of the findings are discussed in terms of future research and implications for application.
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Syrov N, Bredikhin D, Yakovlev L, Miroshnikov A, Kaplan A. Mu-desynchronization, N400 and corticospinal excitability during observation of natural and anatomically unnatural finger movements. Front Hum Neurosci 2022; 16:973229. [PMID: 36118966 PMCID: PMC9480608 DOI: 10.3389/fnhum.2022.973229] [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: 06/19/2022] [Accepted: 08/15/2022] [Indexed: 11/25/2022] Open
Abstract
The action observation networks (AON) (or the mirror neuron system) are the neural underpinnings of visuomotor integration and play an important role in motor control. Besides, one of the main functions of the human mirror neuron system is recognition of observed actions and the prediction of its outcome through the comparison with the internal mental motor representation. Previous studies focused on the human mirror neurons (MNs) activation during object-oriented movements observation, therefore intransitive movements observation effects on MNs activity remains relatively little-studied. Moreover, the dependence of MNs activation on the biomechanical characteristics of observed movement and their biological plausibility remained highly underexplored. In this study we proposed that naturalness of observed intransitive movement can modulate the MNs activity. Event-related desynchronization (ERD) of sensorimotor electroencephalography (EEG) rhythms, N400 event-related potentials (ERPs) component and corticospinal excitability were investigated in twenty healthy volunteers during observation of simple non-transitive finger flexion that might be either biomechanically natural or unnatural when finger wriggled out toward the dorsal side of palm. We showed that both natural and unnatural movements caused mu/beta-desynchronization, which gradually increased during the flexion phase and returned to baseline while observation of extension. Desynchronization of the mu-rhythm was significantly higher during observation of the natural movements. At the same time, beta-rhythm was not found to be sensitive to the action naturalness. Also, observation of unnatural movements caused an increased amplitude of the N400 component registered in the centro-parietal regions. We suggest that the sensitivity of N400 to intransitive action observation with no explicit semantic context might imply the broader role of N400 sources within AON. Surprisingly, no changes in corticospinal excitability were found. This lack of excitability modulation by action observation could be related with dependence of the M1 activity on the observed movement phase.
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Affiliation(s)
- Nikolay Syrov
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
- V. Zelman Center for Neurobiology and Brain Restoration, Skolkovo Institute of Science and Technology, Moscow, Russia
- *Correspondence: Nikolay Syrov,
| | - Dimitri Bredikhin
- Department of Human and Animal Physiology, Faculty of Biology, M. V. Lomonosov Moscow State University, Moscow, Russia
- Department of Psychology, Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia
| | - Lev Yakovlev
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
- V. Zelman Center for Neurobiology and Brain Restoration, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Andrei Miroshnikov
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Alexander Kaplan
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
- Department of Human and Animal Physiology, Faculty of Biology, M. V. Lomonosov Moscow State University, Moscow, Russia
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Schultz J, Frith CD. Animacy and the prediction of behaviour. Neurosci Biobehav Rev 2022; 140:104766. [DOI: 10.1016/j.neubiorev.2022.104766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 06/24/2022] [Accepted: 07/01/2022] [Indexed: 10/17/2022]
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Kosteletou E, Simos PG, Kavroulakis E, Antypa D, Maris TG, Liavas AP, Karakasis PA, Papadaki E. Improving the Sensitivity of Task-Related Functional Magnetic Resonance Imaging Data Using Generalized Canonical Correlation Analysis. Front Hum Neurosci 2022; 15:771668. [PMID: 34970129 PMCID: PMC8712565 DOI: 10.3389/fnhum.2021.771668] [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: 09/06/2021] [Accepted: 11/26/2021] [Indexed: 11/29/2022] Open
Abstract
General Linear Modeling (GLM) is the most commonly used method for signal detection in Functional Magnetic Resonance Imaging (fMRI) experiments, despite its main limitation of not taking into consideration common spatial dependencies between voxels. Multivariate analysis methods, such as Generalized Canonical Correlation Analysis (gCCA), have been increasingly employed in fMRI data analysis, due to their ability to overcome this limitation. This study, evaluates the improvement of sensitivity of the GLM, by applying gCCA to fMRI data after standard preprocessing steps. Data from a block-design fMRI experiment was used, where 25 healthy volunteers completed two action observation tasks at 1.5T. Whole brain analysis results indicated that the application of gCCA resulted in significantly higher intensity of activation in several regions in both tasks and helped reveal activation in the primary somatosensory and ventral premotor area, theoretically known to become engaged during action observation. In subject-level ROI analyses, gCCA improved the signal to noise ratio in the averaged timeseries in each preselected ROI, and resulted in increased extent of activation, although peak intensity was considerably higher in just two of them. In conclusion, gCCA is a promising method for improving the sensitivity of conventional statistical modeling in task related fMRI experiments.
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Affiliation(s)
- Emmanouela Kosteletou
- Institute of Applied and Computational Mathematics, Foundation for Research and Technology - Hellas (FORTH), Heraklion, Greece
| | - Panagiotis G Simos
- Computational Biomedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology - Hellas (FORTH), Heraklion, Greece.,Department of Psychiatry, School of Medicine, University of Crete, Heraklion, Greece
| | | | - Despina Antypa
- Department of Psychiatry, School of Medicine, University of Crete, Heraklion, Greece
| | - Thomas G Maris
- Department of Medical Physics, School of Medicine, University of Crete, Heraklion, Greece
| | - Athanasios P Liavas
- School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece
| | - Paris A Karakasis
- Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, United States
| | - Efrosini Papadaki
- Computational Biomedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology - Hellas (FORTH), Heraklion, Greece.,Department of Radiology, School of Medicine, University of Crete, Heraklion, Greece
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