No abstract

Models of attention demonstrated the existence of top-down, bottom-up, and history-driven attentional mechanisms, controlled by partially segregated networks of brain areas. However, few studies have examined the specific deficits in those attentional mechanisms in intellectual disability within the same experimental setting. The aim of the current study was to specify the attentional deficits in intellectual disability in top-down, bottom-up, and history-driven processing of multisensory stimuli, and gain insight into effective attentional cues that could be utilized in cognitive training programs for intellectual disability. The performance of adults with mild to moderate intellectual disability (n = 20) was compared with that of typically developing controls (n = 20) in a virtual reality visual search task. The type of a spatial cue that could aid search performance was manipulated to be either endogenous or exogenous in different sensory modalities (visual, auditory, tactile). The results identified that attentional deficits in intellectual disability are overall more pronounced in top-down rather than in bottom-up processing, but with different magnitudes across cue types: The auditory or tactile endogenous cues were much less effective than the visual endogenous cue in the intellectual disability group. Moreover, the history-driven processing in intellectual disability was altered, such that a reversed priming effect was observed for immediate repetitions of the same cue type. These results suggest that the impact of intellectual disability on attentional processing is specific to attentional mechanisms and cue types, which has theoretical as well as practical implications for developing effective cognitive training programs for the target population.

The electrocardiogram (ECG) has important clinical value for the early diagnosis of cardiovascular diseases. Recently, the performance of existing diagnosis models based on ECG data has improved with the introduction of deep learning (DL). However, the impact of various combinations of multiple DL components and/or the role of augmentation techniques on the diagnosis have not been sufficiently investigated in this field. In this sense, this study aims to design an integrated model consisting of diverse DL-based modules. Here, an ensemble-based multi-view learning approach with an ECG augmentation technique is proposed to achieve higher performance than traditional automatic 12-lead ECG diagnosis methods. Accordingly, several experiments have been conducted with CPSC2018 dataset for evaluation. The proposed model reports F1 score of 0.840, which outperforms existing state-of-the-art methods in the literature. Thus, this study provides quantitative evidence demonstrating that the multi-view learning approach can be used as a unified algorithmic method in the field of bioinformatics.

Fixation Cross (1000 ms)... Video (30 s) & 10 cue displayFigure 1: Attention guidance technique using visual subliminal cues in Experiment 2. The participants watched 30 videos without any special instructions, while cues were displayed 10 times in each video. The location of the cues was biased towards the left or the right hemifeld of the video, with 80% to 20% cue frequency distribution. The subliminal cues were altered in opacity and size for visibility purposes.