Persons with Autism spectrum disorders (ASD) display atypical perceptual processing in visual and auditory tasks. In vision, Bertone, Mottron, Jelenic, and Faubert (2005) found that enhanced and diminished visual processing is linked to the level of neural complexity required to process stimuli, as proposed in the neural complexity hypothesis. Based on these findings, Samson, Mottron, Jemel, Belin, and Ciocca (2006) proposed to extend the neural complexity hypothesis to the auditory modality. They hypothesized that persons with ASD should display enhanced performance for simple tones that are processed in primary auditory cortical regions, but diminished performance for complex tones that require additional processing in associative auditory regions, in comparison to typically developing individuals. To assess this hypothesis, we designed four auditory discrimination experiments targeting pitch, non-vocal and vocal timbre, and loudness. Stimuli consisted of spectro-temporally simple and complex tones. The participants were adolescents and young adults with autism, Asperger syndrome, and typical developmental histories, all with IQs in the normal range. Consistent with the neural complexity hypothesis and enhanced perceptual functioning model of ASD (Mottron, Dawson, Soulières, Hubert, & Burack, 2006), the participants with autism, but not with Asperger syndrome, displayed enhanced pitch discrimination for simple tones. However, no discrimination-thresholds differences were found between the participants with ASD and the typically developing persons across spectrally and temporally complex conditions. These findings indicate that enhanced pure-tone pitch discrimination may be a cognitive correlate of speech-delay among persons with ASD. However, auditory discrimination among this group does not appear to be directly contingent on the spectro-temporal complexity of the stimuli.
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http://dx.doi.org/10.1016/j.neuropsychologia.2010.04.020 | DOI Listing |
J Chem Theory Comput
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Faculty of Science, School of Earth and Atmospheric Sciences, Queensland University of Technology, Brisbane, QLD, Australia.
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Institute for Global Ecology, Florida Institute of Technology, Melbourne, Florida, United States of America.
Marine heatwaves are increasing in intensity and frequency however, responses and survival of reef corals vary geographically. Geographical differences in thermal tolerance may be in part a consequence of intraspecific diversity, where high-diversity localities are more likely to support heat-tolerant alleles that promote survival through thermal stress. Here, we assessed geographical patterns of intraspecific genetic diversity in the ubiquitous coral Pocillopora damicornis species complex using 428 sequences of the Internal Transcribed Spacer 2 (ITS2) region across 44 sites in the Pacific and Indian Oceans.
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Department of Information Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China.
As education increasingly relies on data-driven methodologies, accurately predicting student performance is essential for implementing timely and effective interventions. The California Student Performance Dataset offers a distinctive basis for analyzing complex elements that affect educational results, such as student demographics, academic behaviours, and emotional health. This study presents the GNN-Transformer-InceptionNet (GNN-TINet) model to overcome the constraints of prior models that fail to effectively capture intricate interactions in multi-label contexts, where students may display numerous performance categories concurrently.
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Department of Information Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Ramapuram, Chennai, 600089, India.
Brain tumours are one of the most deadly and noticeable types of cancer, affecting both children and adults. One of the major drawbacks in brain tumour identification is the late diagnosis and high cost of brain tumour-detecting devices. Most existing approaches use ML algorithms to address problems, but they have drawbacks such as low accuracy, high loss, and high computing cost.
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