Dyslexia has been widely held to be associated with deficient temporal processing. It is, however, not established that the slower visual processing of dyslexic readers is not a secondary effect of task difficulty. To illustrate this we re-analyze data from Liddle et al. (2009) who studied temporal order judgment in dyslexia and plotted the results as d' as a function of Stimulus Onset Asynchrony (SOA). These data make it possible to compare the results of dyslexic readers and controls both in terms of d' which is related closely to task difficulty and in terms of time (i.e. SOA). It is found that the difference between the groups is about equally well accounted for in terms of d' as in terms of temporal factors. This suggests that the results of Liddle et al. (2009) may be equally well accounted for in terms of general task difficulty as temporal factors.
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http://dx.doi.org/10.1016/j.neuropsychologia.2010.04.013 | DOI Listing |
PLoS One
January 2025
Institute of Behavioural Neuroscience, Department of Experimental Psychology, Division of Psychology and Language Sciences, University College London, London, United Kingdom.
Virtual reality environments presented on tablets and smartphones offer a novel way of measuring navigation skill and predicting real-world navigation problems. The extent to which such virtual tests are effective at predicting navigation in older populations remains unclear. We compared the performance of 20 older participants (54-74 years old) in wayfinding tasks in a real-world environment in London, UK, and in similar tasks designed in a mobile app-based test of navigation (Sea Hero Quest).
View Article and Find Full Text PDFSci Rep
January 2025
Centre for Brain, Mind and Markets, Faculty of Business and Economics, The University of Melbourne, Melbourne, Australia.
Metacognition, the ability to monitor and reflect on our own mental states, enables us to assess our performance at different levels - from confidence in individual decisions to overall self-performance estimates (SPEs). It plays a particularly important part in computationally complex decisions that require a high level of cognitive resources, as the allocation of such limited resources presumably is based on metacognitive evaluations. However, little is known about metacognition in complex decisions, in particular, how people construct SPEs.
View Article and Find Full Text PDFJ Clin Med
January 2025
Department of Psychology, Università degli Studi della Campania "L. Vanvitelli", 81100 Caserta, Italy.
Mental representation of spatial information relies on egocentric (body-based) and allocentric (environment-based) frames of reference. Research showed that spatial memory deteriorates as Alzheimer's disease (AD) progresses and that allocentric spatial memory is among the earliest impaired areas. Most studies have been conducted in static situations despite the dynamic nature of real-world spatial processing.
View Article and Find Full Text PDFJ Clin Med
January 2025
Health, Physical Activity and Sports Technology (HEALTH-TECH), Department of General and Specific Didactics, Faculty of Education, University of Alicante, 03690 Alicante, Spain.
: Parkinson's disease (PD) is a neurodegenerative disorder that significantly impairs motor function, leading to mobility challenges and an increased risk of falls. Current assessment tools often inadequately measure the complexities of motor impairments associated with PD, highlighting the need for a reliable tool. This study introduces the Motor Assessment Timed Test (MATT), designed to assess functional mobility in PD patients.
View Article and Find Full Text PDFMolecules
January 2025
Computational Systems Biology Group, National Center for Biotechnology (CNB-CSIC), 28049 Madrid, Spain.
Knowing which residues of a protein are important for its function is of paramount importance for understanding the molecular basis of this function and devising ways of modifying it for medical or biotechnological applications. Due to the difficulty in detecting these residues experimentally, prediction methods are essential to cope with the sequence deluge that is filling databases with uncharacterized protein sequences. Deep learning approaches are especially well suited for this task due to the large amounts of protein sequences for training them, the trivial codification of this sequence data to feed into these systems, and the intrinsic sequential nature of the data that makes them suitable for language models.
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