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The literature on neuropsychological intervention (NI) uses a variety of terms to refer to equivalent constructs, making it difficult to compare intervention programmes and their outcomes. The purpose of this work is to propose a unified terminological framework for describing NI programmes. The terminological framework was developed based on a previous proposal for common terminology by Johnstone and Stonnington (Rehabilitation of neuropsychological disorders: A practical guide for rehabilitation professionals. Psychology Press, 2011) and driven by Cognitive Psychology concepts. The terminological framework was organized into two sections: (a) NI, which includes types of NI, methods and approaches, instructional methods, and strategies; and (b) neurocognitive functions, which include temporal and spatial orientation, sensation, perception, visuo-constructional abilities, attention, memory, language, reasoning of several sorts (e.g., abstract reasoning, and numerical reasoning), and executive functions. Most NI tasks target a main neurocognitive function, but there are underlying neurocognitive functions that may impair performance in the former. Since it is difficult to create a task that is solely focused on one neurocognitive function, the proposed terminology should not be viewed as a taxonomy, but rather as dimensional, with the same task allowing to work different functions, in varying grades. Adopting this terminological framework will allow to define the targeted neurocognitive functions more accurately and simplify the comparison between NI programmes and their outcomes. Future research should focus on describing the main techniques/strategies for each neurocognitive function and non-cognitive interventions.
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http://dx.doi.org/10.1111/jnp.12310 | DOI Listing |
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi
August 2025
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China.
Brain-computer interface (BCI) technology faces structural risks due to a misalignment between its technological maturity and industrialization expectations. This study used the Technology Readiness Level (TRL) framework to assess the status of major BCI paradigms-such as steady-state visual evoked potential (SSVEP), motor imagery, and P300-and found that they predominantly remained at TRL4 to TRL6, with few stable applications reaching TRL9. The analysis identified four interrelated sources of bubble risk: overly broad definitions of BCI, excessive focus on decoding performance, asynchronous translational progress, and imprecise terminology usage.
View Article and Find Full Text PDFActa Psychol (Amst)
September 2025
Department of Early Childhood Education, National Pingtung University, No. 4-18, Minsheng Rd., Pingtung City, Pingtung County 900391, Taiwan. Electronic address:
This meta-analysis investigates the impact of smartphone usage on university students' academic performance, with a focus on identifying moderating factors. A total of 45 studies were analyzed, revealing a small but statistically significant negative effect of smartphone usage frequency on academic performance (r = -0.12).
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View Article and Find Full Text PDFFront Sports Act Living
July 2025
Faculty of Education, Silpakorn University, Nakhon Pathom, Thailand.
Introduction: Street dance research has expanded rapidly since its inception, yet the absence of a unified terminology has produced inconsistent academic interpretations and a substantial knowledge gap. This study aimed to establish a clear definition of "street dance," classify its common styles, and identify the factors underlying terminological inconsistency.
Methods: Following PRISMA guidelines, we conducted a systematic review.
Comput Biol Med
September 2025
Institute of Data Science, Maastricht University, Maastricht, Netherlands. Electronic address:
The standardization of clinical data elements (CDEs) aims to ensure consistent and comprehensive patient information across various healthcare systems. Existing methods often falter when standardizing CDEs of varying representation and complex structure, impeding data integration and interoperability in clinical research. This paper presents CDE-Mapper, a framework that combines a retrieval-augmented generation strategy with large language models to automate the alignment of CDEs with controlled vocabularies.
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