Self-concept, creativity and developmental dyslexia in university students: effects of age of assessment

Educational experiences often influence self-concept. Thus, readers with dyslexia can have low self-esteem and self-efficacy, and perceive themselves as less intelligent than their peers. They may develop creativity to succeed despite their difficulties but findings are inconsistent and rarely consider the effect of age of assessment on self-perception. This study included 145 university students (Mage = 24.43 years), 72 with dyslexia; of these, 53% had been assessed in childhood (Mage = 11.89 years), 47% in adulthood (Mage = 27.38 years). A survey assessed self-esteem, self-efficacy, creativity and estimated intelligence. Students with dyslexia reported lower levels of self-esteem, self-efficacy and estimated intelligence. When assessment age was considered, those assessed early displayed lower self-esteem and self-efficacy but no difference in estimated intelligence. Those assessed late displayed lower estimated intelligence and self-esteem but no difference in self-efficacy. Findings highlight the importance of providing psychological support to students with dyslexia to enhance their self-perceptions

A force profile analysis comparison between functional data analysis, statistical parametric mapping and statistical non-parametric mapping in on-water single sculling

Objectives: To examine whether the Functional Data Analysis (FDA), Statistical Parametric Mapping (SPM) and Statistical non-Parametric Mapping (SnPM) hypothesis testing techniques differ in their ability to draw inferences in the context of a single, simple experimental design. Design: The sample data used is cross-sectional (two-sample gender comparison) and evaluation of differences between statistical techniques used a combination of descriptive and qualitative assessments. Methods: FDA, SPM and SnPM t-tests were applied to sample data of twenty highly skilled male and female rowers, rowing at 32 strokes per minute in a single scull boat. Statistical differences for gender were assessed by applying two t-tests (one for each side of the boat). Results: The t-statistic values were identical for all three methods (with the FDA t-statistic presented as an absolute measure). The critical t-statistics (tcrit) were very similar between the techniques, with SPM tcrit providing a marginally higher tcrit than the FDA and SnPM tcrit values (which were identical). All techniques were successful in identifying consistent sections of the force waveform, where male and female rowers were shown to differ significantly (p < 0.05). Conclusions: This is the first study to show that FDA, SPM and SnPM t-tests provide consistent results when applied to sports biomechanics data. Though the results were similar, selection of one technique over another by applied researchers and practitioners should be based on the underlying parametric assumption of SPM, as well as contextual factors related to the type of waveform data to be analysed and the experimental research question of interest

PCA of waveforms and functional PCA: A primer for biomechanics.

Principal components analysis (PCA) of waveforms and functional PCA (fPCA) are statistical approaches used to explore patterns of variability in biomechanical curve data, with fPCA being an accepted statistical method grounded within the functional data analysis (FDA) statistical framework. This technical note demonstrates that PCA of waveforms is the most rudimentary form of FDA, and consequently can be rationalised within the FDA framework of statistical processes. Mathematical proofing applied demonstrations of both techniques, and an example of when fPCA may be of greater benefit to control over smoothing of functional principal components is provided using an open access motion sickness dataset. Finally, open access software is provided with this paper as means of priming the biomechanics community for using these methods as a part of future functional data explorations

Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye

We study involuntary micro-movements of the eye for biometric identification. While prior studies extract lower-frequency macro-movements from the output of video-based eye-tracking systems and engineer explicit features of these macro-movements, we develop a deep convolutional architecture that processes the raw eye-tracking signal. Compared to prior work, the network attains a lower error rate by one order of magnitude and is faster by two orders of magnitude: it identifies users accurately within seconds

Predicting Patterns of Customer Usage, with Niftecash

Report is the result of the working during 93rd European Study Group with Industry in Limerick

Predicting Patterns of Customer Usage, with Niftecash

Report is the result of the working during 93rd European Study Group with Industry in Limerick

The emergence of synaesthesia in a Neuronal Network Model via changes in perceptual sensitivity and plasticity

Synaesthesia is an unusual perceptual experience in which an inducer stimulus triggers a percept in a different domain in addition to its own. To explore the conditions under which synaesthesia evolves, we studied a neuronal network model that represents two recurrently connected neural systems. The interactions in the network evolve according to learning rules that optimize sensory sensitivity. We demonstrate several scenarios, such as sensory deprivation or heightened plasticity, under which synaesthesia can evolve even though the inputs to the two systems are statistically independent and the initial cross-talk interactions are zero. Sensory deprivation is the known causal mechanism for acquired synaesthesia and increased plasticity is implicated in developmental synaesthesia. The model unifies different causes of synaesthesia within a single theoretical framework and repositions synaesthesia not as some quirk of aberrant connectivity, but rather as a functional brain state that can emerge as a consequence of optimising sensory information processing

Call to increase statistical collaboration in sports science, sport and exercise medicine and sports physiotherapy

Statistical errors are common in many biomedical fields.1–5 We believe the nature and impact of these errors to be great enough in sports science and medicine to warrant special attention.6–14 Poor methodological and statistical practices have led to calls for change in other fields, such as psychology.15–18 We believe that a similar call to action is needed in sports science and medicine. Specifically, we see two pressing needs: (1) to increase collaboration between researchers and statisticians, and (2) to increase statistical training within the exercise science/medicine/physiotherapy (PT) discipline. Our call to action extends the work of those who have previously called for increased statistical collaboration in sports medicine and sports injury researc