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PURPOSE: There is an increasing focus on using motion in augmentative and alternative communication (AAC) systems. In considering brain-computer interface access to AAC (BCI-AAC), motion may provide a simpler or more intuitive avenue for BCI-AAC control. Different motion techniques may be utilized in supporting competency with AAC devices including simple (e.g., zoom) and complex (behaviorally relevant animation) methods. However, how different pictorial symbol animation techniques impact BCI-AAC is unclear. METHOD: Sixteen healthy children completed two experimental conditions. These conditions included highlighting of pictorial symbols via both functional (complex) and zoom (simple) animation to evaluate the effects of motion techniques on P300-based BCI-AAC signals and offline (predicted) BCI-AAC performance. RESULTS: Functional (complex) animation significantly increased attentional-related P200/P300 event-related potential (ERP) amplitudes in the parieto-occipital area. Zoom (simple) animation significantly decreased N400 latency. N400 ERP amplitude was significantly greater, and occurred significantly earlier, on the right versus left side for the functional animation condition within the parieto-occipital bin. N200 ERP latency was significantly reduced over the left hemisphere for the zoom condition in the central bin. As hypothesized, elicitation of all targeted ERP components supported offline (predicted) BCI-AAC performance being similar between conditions. CONCLUSION: Study findings provide continued support for the use of animation in BCI-AAC systems for children and highlight differences in neural and attentional processing between complex and simple animation techniques. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.24085623.

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Previous attempts to classify task from eye movement data have relied on model architectures designed to emulate theoretically defined cognitive processes and/or data that have been processed into aggregate (e.g., fixations, saccades) or statistical (e.g., fixation density) features. Black box convolutional neural networks (CNNs) are capable of identifying relevant features in raw and minimally processed data and images, but difficulty interpreting these model architectures has contributed to challenges in generalizing lab-trained CNNs to applied contexts. In the current study, a CNN classifier was used to classify task from two eye movement datasets (Exploratory and Confirmatory) in which participants searched, memorized, or rated indoor and outdoor scene images. The Exploratory dataset was used to tune the hyperparameters of the model, and the resulting model architecture was retrained, validated, and tested on the Confirmatory dataset. The data were formatted into timelines (i.e., x-coordinate, y-coordinate, pupil size) and minimally processed images. To further understand the informational value of each component of the eye movement data, the timeline and image datasets were broken down into subsets with one or more components systematically removed. Classification of the timeline data consistently outperformed the image data. The Memorize condition was most often confused with Search and Rate. Pupil size was the least uniquely informative component when compared with the x- and y-coordinates. The general pattern of results for the Exploratory dataset was replicated in the Confirmatory dataset. Overall, the present study provides a practical and reliable black box solution to classifying task from eye movement data.

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Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

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Music is a common accompaniment to exercise, but some running environments do not allow for personalized control over the music stimulus. The purpose of this study was to explore the effects of listening to preferred versus non-preferred music on sex differences in running performance. The sample consisted of 20 women and 15 men (M = 20.7 yr., SD = 2.3) who reported running at least once per week over the previous year. The participants completed three 12-min. Cooper Tests (i.e., aerobic fitness test) accompanied by preferred, non-preferred, or no music in randomized order. A 2 × 2 repeated-measures analysis of covariance (ANCOVA) was used to test the effect of music preference and sex on endurance running performance while controlling for the distance run with no music. Women ran further in the preferred music condition. However, music selection did not significantly change running performance for men. Listening to preferred instead of non-preferred music had a larger effect on the endurance running performance of women than men.

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The interactive capacity of the Internet offers benefits that are intimately linked with contemporary research innovation in the natural resource and environmental studies domains. However, e-research methodologies, such as the e-Delphi technique, have yet to undergo critical review. This study advances methodological discourse on the e-Delphi technique by critically assessing an e-Delphi case study. The analysis suggests that the benefits of using e-Delphi are noteworthy but the authors acknowledge that researchers are likely to face challenges that could potentially compromise research validity and reliability. To ensure that these issues are sufficiently considered when planning and designing an e-Delphi, important facets of the technique are discussed and recommendations are offered to help the environmental researcher avoid potential pitfalls associated with coordinating e-Delphi research.

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Analog optical signal processing of complex radio-frequency signals for range-Doppler radar information is theoretically described and experimentally demonstrated using crystalline optical memory materials and off-the-shelf photonic components. A model of the range-Doppler processing capability of the memory material for the case of single-target detection is presented. Radarlike signals were emulated and processed by the memory material; they consisted of broadband (> 1 GHz), spread-spectrum, pseudorandom noise sequences of 512 bits in length, which were binary phase-shift keyed on a 1.9 GHz carrier and repeated at 100 kHz over 7.5 ms. Delay (range) resolution of 8 ns and Doppler resolution of 130 Hz over 100 kHz were demonstrated.