Manual actions are a hallmark of humanness. Their underlying neural circuitry gives rise to species-specific skills and interacts with language processes. In particular, multiple studies show that hand-related expressions - verbal units evoking manual activity - variously affect concurrent manual actions, yielding apparently controversial results (interference, facilitation, or null effects) in varied time windows. Through a systematic review of 108 experiments, we show that such effects are driven by several factors, such as the level of verbal processing, action complexity, and the time-lag between linguistic and motor processes. We reconcile key empirical patterns by introducing the Hand-Action-Network Dynamic Language Embodiment (HANDLE) model, an integrative framework based on neural coupling dynamics and predictive-coding principles. To conclude, we assess HANDLE against the backdrop of other action-cognition theories, illustrate its potential applications to understand high-level deficits in motor disorders, and discuss key challenges for further development. In sum, our work aligns with the 'pragmatic turn', moving away from passive and static representationalist perspectives to a more dynamic, enactive, and embodied conceptualization of cognitive processes.
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http://dx.doi.org/10.1016/j.neubiorev.2016.04.022 | DOI Listing |
Waste Manag
January 2025
Chair of Waste Processing Technology and Waste Management, Montanuniversitaet Leoben, Leoben, Austria. Electronic address:
Global waste generation is projected to reach 3.40 billion tons by 2050, necessitating improved waste sorting for effective recycling and progress toward a circular economy. Achieving this transformation requires higher sorting intensity through intensified processes, increased efficiency, and enhanced yield.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
Joint Surgery Department, Tianjin Hospital, No. 406, Jiefangnan Road, Tianjin, 300211, People's Republic of China.
Objective: This study aimed to assess the feasibility of computer model-based evaluation of knee joint functional capacity in comparison with manual assessment.
Methods: This study consisted of two phases: (1) developing an automatic knee joint action recognition and classification system on the basis of improved YOLOX and (2) analyzing the feasibility of assessment by the software system and doctors, identifying the knee joint function of patients, and determining the accuracy of the software system. We collected 40-50 samples for use in clinical experiments.
Med Phys
January 2025
Department of Radiation Oncology, Duke University, North Carolina, USA.
Background: The electronic compensation (ECOMP) technique for breast radiation therapy provides excellent dose conformity and homogeneity. However, the manual fluence painting process presents a challenge for efficient clinical operation.
Purpose: To facilitate the clinical treatment planning automation of breast radiation therapy, we utilized reinforcement learning (RL) to develop an auto-planning tool that iteratively edits the fluence maps under the guidance of clinically relevant objectives.
Infancy
January 2025
Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands.
The ability to recognize and act on others' emotions is crucial for navigating social interactions successfully and learning about the world. One way in which others' emotions are observable is through their movement kinematics. Movement information is available even at a distance or when an individual's face is not visible.
View Article and Find Full Text PDFJ Gen Physiol
March 2025
Institute for Neurophysiology, Uniklinik RWTH Aachen University, Aachen, Germany.
Voltage-gated sodium channels (VGSCs) in the peripheral nervous system shape action potentials (APs) and thereby support the detection of sensory stimuli. Most of the nine mammalian VGSC subtypes are expressed in nociceptors, but predominantly, three are linked to several human pain syndromes: while Nav1.7 is suggested to be a (sub-)threshold channel, Nav1.
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