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: Distinguishability of battery equivalent-circuit models containing CPEs: Updating the work of Berthier, Diard, & Michel

authors: Poihipi, Eden Kathleen

subjects: Constant Phase Elements

abstract: This manuscript revisits the assertion of Berthier et al. that competing fractional-element equivalent-circuit models of battery cells are indistinguishable in the presence of noise. Starting with Westerhoff’s general equivalent-circuit model (ECM) of 2016 and an idealized impedance spectrum of a lithium battery, a contemporary set of possible ECMs are chosen. Their distinguishability is investigated. Using the extended frequency range recommended by Mauracher & Karden in 1997 or Hasan & Scott in 2019, an approach is presented that permits selection of the appropriate model, even in the presence of noise. For the given frequency range, models with up to five elements or eight real parameters are studied. Fitting to measured data with straightforward numerical methods and choosing the most primitive appropriate sub-circuit is shown to reproduce the data. Typically a three-element, five-parameter ECM is shown to model real, measured data with precision comparable to individual sample error.

text: Distinguishability of battery equivalent-circuit models containing CPEs: Updating the work of Berthier, Diard, & Michel This manuscript revisits the assertion of Berthier et al. that competing fractional-element equivalent-circuit models of battery cells are indistinguishable in the presence of noise. Starting with Westerhoff’s general equivalent-circuit model (ECM) of 2016 and an idealized impedance spectrum of a lithium battery, a contemporary set of possible ECMs are chosen. Their distinguishability is investigated. Using the extended frequency range recommended by Mauracher & Karden in 1997 or Hasan & Scott in 2019, an approach is presented that permits selection of the appropriate model, even in the presence of noise. For the given frequency range, models with up to five elements or eight real parameters are studied. Fitting to measured data with straightforward numerical methods and choosing the most primitive appropriate sub-circuit is shown to reproduce the data. Typically a three-element, five-parameter ECM is shown to model real, measured data with precision comparable to individual sample error.

year: 2022

title: Ko ngōku maumaharatanga i ahau i Te Whare Wānanga o Waikato

authors: Papesch, Te Rita

subjects: Tohu maumahara

abstract: Kua huri te rima ngahuru o ngā tau kua tū Te Whare Wānanga hei whakaruruhau mō rātou ka kuhu ki ngōna kuaha. Ko taku tāne tuatahi me taku tungāne pakeke ake i ahau ngētehi o ngā akonga tuatahi, ā, e maumahara ana ahau i tētehi wā i tae atu ahau ki reira ki te tiki i a rāua i waho tonu i te whare pukapuka. I taua wā ko A Block tērā, te wāhi e tū nei Te Puawānanga-ki-te Ao ināianei. Ko te Cowshed tonu te wāhi whakahaere i Te Whare Wānanga. Ka mīharo i tōna rerekētanga i ngēnei wā!

text: Ko ngōku maumaharatanga i ahau i Te Whare Wānanga o Waikato Kua huri te rima ngahuru o ngā tau kua tū Te Whare Wānanga hei whakaruruhau mō rātou ka kuhu ki ngōna kuaha. Ko taku tāne tuatahi me taku tungāne pakeke ake i ahau ngētehi o ngā akonga tuatahi, ā, e maumahara ana ahau i tētehi wā i tae atu ahau ki reira ki te tiki i a rāua i waho tonu i te whare pukapuka. I taua wā ko A Block tērā, te wāhi e tū nei Te Puawānanga-ki-te Ao ināianei. Ko te Cowshed tonu te wāhi whakahaere i Te Whare Wānanga. Ka mīharo i tōna rerekētanga i ngēnei wā!

year: 2016