Imitation and language processing are closely connected. According to the Ease of Language Understanding (ELU) model (Rönnberg et al., 2013) pre-existing mental representation of lexical items facilitates language understanding. Thus, imitation of manual gestures is likely to be enhanced by experience of sign language. We tested this by eliciting imitation of manual gestures from deaf and hard-of-hearing (DHH) signing and hearing non-signing children at a similar level of language and cognitive development. We predicted that the DHH signing children would be better at imitating gestures lexicalized in their own sign language (Swedish Sign Language, SSL) than unfamiliar British Sign Language (BSL) signs, and that both groups would be better at imitating lexical signs (SSL and BSL) than non-signs. We also predicted that the hearing non-signing children would perform worse than DHH signing children with all types of gestures the first time (T1) we elicited imitation, but that the performance gap between groups would be reduced when imitation was elicited a second time (T2). Finally, we predicted that imitation performance on both occasions would be associated with linguistic skills, especially in the manual modality. A split-plot repeated measures ANOVA demonstrated that DHH signers imitated manual gestures with greater precision than non-signing children when imitation was elicited the second but not the first time. Manual gestures were easier to imitate for both groups when they were lexicalized than when they were not; but there was no difference in performance between familiar and unfamiliar gestures. For both groups, language skills at T1 predicted imitation at T2. Specifically, for DHH children, word reading skills, comprehension and phonological awareness of sign language predicted imitation at T2. For the hearing participants, language comprehension predicted imitation at T2, even after the effects of working memory capacity and motor skills were taken into account. These results demonstrate that experience of sign language enhances the ability to imitate manual gestures once representations have been established, and suggest that the inherent motor patterns of lexical manual gestures are better suited for representation than those of non-signs. This set of findings prompts a developmental version of the ELU model, D-ELU.
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http://dx.doi.org/10.3389/fpsyg.2016.00107 | DOI Listing |
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January 2025
College of Engineering and Technology, American University of the Middle East, Egaila, Kuwait.
Adv Sci (Weinh)
January 2025
Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China.
Human-machine interfaces and wearable electronics, as fundamentals to achieve human-machine interactions, are becoming increasingly essential in the era of the Internet of Things. However, contemporary wearable sensors based on resistive and capacitive mechanisms demand an external power, impeding them from extensive and diverse deployment. Herein, a smart wearable system is developed encompassing five arch-structured self-powered triboelectric sensors, a five-channel data acquisition unit to collect finger bending signals, and an artificial intelligence (AI) methodology, specifically a long short-term memory (LSTM) network, to recognize signal patterns.
View Article and Find Full Text PDFChildren (Basel)
November 2024
Department of Psychology, University of A Coruña, 15071 A Coruña, Spain.
Background/objectives: Specific tests for the assessment of language development and language skills in deaf children are scarce. For this reason, parent inventories and/or standardized tests that are reliable and valid in the hearing population are used. The main aim of this study was to assess the usefulness of the Clinical Evaluation of Language Fundamentals 5 (CELF5) in determining the language skills of hearing-impaired children in a comprehensive way in comparison to their hearing peers.
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January 2025
Centre for Robotics and Automation, Department of Biomedical Engineering, City University of Hong Kong, Hong Kong 999077, China.
Liquid metals are highly conductive like metallic materials and have excellent deformability due to their liquid state, making them rather promising for flexible and stretchable wearable sensors. However, patterning liquid metals on soft substrates has been a challenge due to high surface tension. In this paper, a new method is proposed to overcome the difficulties in fabricating liquid-state strain sensors.
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