title: Examining neuroplasticity as a function of errors during motor learning

authors: Nirol

abstract: Proficient motor learning is important for both athletes looking to improve their performance and patients learning daily living skills. Research has identified different methods for learning motor skills, focusing on two types: implicit and explicit learning. Implicit learning happens with little awareness, while explicit learning requires more conscious thought. Studies show that implicit learning is often more effective because it uses less mental effort and is more reliable under stress. This might be due to stronger neural connections in the brain formed during implicit learning. However, it is unclear whether implicit learning leads to more brain changes, known as neuroplasticity, than explicit learning. Neuroplasticity is the brain's ability to adapt and form new memory networks in response to experiences. This study aims to investigate differences in neuroplastic changes resulting from implicit versus explicit learning of a traditional Dutch shuffleboard game (Sjoelbak). We used baseline electroencephalography (EEG) to measure changes in brain activity before and after practice to assess neuroplasticity. We examined the effects of errorless and errorful learning protocols on task performance and neural connectivity. Both strategies improved accuracy from pretest to posttest, indicating effective skill acquisition. However, participants struggled to transfer skills to new contexts, as shown by decreased performance in the transfer test. Despite expectations of greater neuroplastic changes as a function of errorless learning, resting-state beta connectivity analyses showed no significant interactions. However, no significant differences were evident in the overall error rates between the groups during the learning phase, suggesting that implicit motor learning may not have occurred for the errorless learning condition. This suggests that neural adaptations are more complex than initially hypothesized.

text: Examining neuroplasticity as a function of errors during motor learning Proficient motor learning is important for both athletes looking to improve their performance and patients learning daily living skills. Research has identified different methods for learning motor skills, focusing on two types: implicit and explicit learning. Implicit learning happens with little awareness, while explicit learning requires more conscious thought. Studies show that implicit learning is often more effective because it uses less mental effort and is more reliable under stress. This might be due to stronger neural connections in the brain formed during implicit learning. However, it is unclear whether implicit learning leads to more brain changes, known as neuroplasticity, than explicit learning. Neuroplasticity is the brain's ability to adapt and form new memory networks in response to experiences. This study aims to investigate differences in neuroplastic changes resulting from implicit versus explicit learning of a traditional Dutch shuffleboard game (Sjoelbak). We used baseline electroencephalography (EEG) to measure changes in brain activity before and after practice to assess neuroplasticity. We examined the effects of errorless and errorful learning protocols on task performance and neural connectivity. Both strategies improved accuracy from pretest to posttest, indicating effective skill acquisition. However, participants struggled to transfer skills to new contexts, as shown by decreased performance in the transfer test. Despite expectations of greater neuroplastic changes as a function of errorless learning, resting-state beta connectivity analyses showed no significant interactions. However, no significant differences were evident in the overall error rates between the groups during the learning phase, suggesting that implicit motor learning may not have occurred for the errorless learning condition. This suggests that neural adaptations are more complex than initially hypothesized.

year: 2025

title: Creative research in the arts: introduction to the special section.

authors: Barbour, Karen

subjects: study and teaching

abstract: Then article discusses various reports published within the issue, including one on the role of teachers and artists in research in arts and education and on the potential of the artist to contribute to the generation of new knowledge in art education.

text: Creative research in the arts: introduction to the special section. Then article discusses various reports published within the issue, including one on the role of teachers and artists in research in arts and education and on the potential of the artist to contribute to the generation of new knowledge in art education.

year: 2007

title: A hybrid architecture for labelling bilingual Māori-English tweets

authors: Trye, David

subjects: borrowing

abstract: Most large-scale language detection tools perform poorly at identifying Māori text. Moreover, rule-based and machine learning-based techniques devised specifically for the Māori-English language pair struggle with interlingual homographs. We develop a hybrid architecture that couples Māori-language orthography with machine learning models in order to annotate mixed Māori-English text. This architecture is used to label a new bilingual Twitter corpus at both the token (word) and tweet (sentence) levels. We use the collected tweets to show that the hybrid approach outperforms existing systems with respect to language detection of interlingual homographs and overall accuracy. We also evaluate its performance on out-of-domain data. Two interactive visualisations are provided for exploring the Twitter corpus and comparing errors across the new and existing techniques. The architecture code and visualisations are available online, and the corpus is available on request.

text: A hybrid architecture for labelling bilingual Māori-English tweets Most large-scale language detection tools perform poorly at identifying Māori text. Moreover, rule-based and machine learning-based techniques devised specifically for the Māori-English language pair struggle with interlingual homographs. We develop a hybrid architecture that couples Māori-language orthography with machine learning models in order to annotate mixed Māori-English text. This architecture is used to label a new bilingual Twitter corpus at both the token (word) and tweet (sentence) levels. We use the collected tweets to show that the hybrid approach outperforms existing systems with respect to language detection of interlingual homographs and overall accuracy. We also evaluate its performance on out-of-domain data. Two interactive visualisations are provided for exploring the Twitter corpus and comparing errors across the new and existing techniques. The architecture code and visualisations are available online, and the corpus is available on request.

year: 2022