Mobile Interface Design Patterns and Attentional Maps

International Journal of Human-Computer Interaction, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 10

Published: Feb. 12, 2025

Language: Английский

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Statistical learning re-shapes the center-surround inhibition of the visuo-spatial attentional focus

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 5, 2025

To effectively navigate a crowded and dynamic visual world, our neurocognitive system possesses the remarkable ability to extract learn its statistical regularities implicitly guide allocation of spatial attention resources in immediate future. The way through which we deploy space has been consistently outlined by "center-surround inhibition" pattern, wherein ring sustained inhibition is projected around center attentional focus optimize signal-noise ratio between goal-relevant targets interfering distractors. While it observed that experience-dependent mechanisms could disrupt inhibitory ring, whether learning contingencies an effect on such surround - if any exact unravels are hitherto unexplored questions. Therefore, search psychophysical experiment, aimed fill this gap entirely mapping visuo-spatial profile, asking subjects (N = 26) detect report orientation 'C' letter appearing either as color singleton (Baseline Condition) or non-salient probe (Probe among other irrelevant objects at progressively increasing probe-to-singleton distances. Critically, manipulated contingency so make appear more frequently adjacent probe, specifically distance where attending generates surround-inhibition hindering performance. Results showed markedly reshaped focus, transforming center-surround profile into non-linear gradient one performance gain over high probability distance. Noteworthy, reshaping was uneven time asymmetric, varied across blocks appeared only within quadrants, leaving unaltered unmanipulated ones. Our findings offer insights theoretical interest understanding how environmental orchestrate allocate plastic re-weighting priority maps. Additionally, going beyond physical dimension, data provide interesting implications about information coded working memory representations, especially under scenarios heightened uncertainty.

Language: Английский

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Analysis and Prediction of Schizophrenia Patients Based on High-Order Graph Attention Generative Adversarial Networks

Published: April 10, 2025

Generative Adversarial Networks, a popular deep learning method, have achieved excellent performance in both classification and prediction tasks. However, there been relatively few applications of generative adversarial networks to EEG data. To study the effect high-order brain functional on schizophrenia patients, graph attention network model is proposed, generator utilizes long short-term memory capture topological features persistence images for early diagnosis patients. The research results five frequency bands show that proposed performs best Theta band, with AUC MAP values reaching 93.5% 93.0%, respectively, an average accuracy 91.5%, which are superior selected comparison methods. Moreover, image quality coefficient used quantify realism clarity generated by model. coefficients patients were significantly correlated PANSS total scores Gamma bands, provided new idea features.

Language: Английский

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Complex trade-offs in a dual-target visual search task are indexed by lateralised ERP components

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 1, 2024

Language: Английский

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