Search

Background: The purpose of this study was to examine the relationship between selective visual attention (SVA), reading decoding, listening comprehension and reading comprehension in children with and without a reading disorder. Methods: We used longitudinal data from the Avon Longitudinal Study of Parents and Children. We split children into four groups: Typical Readers, Dyslexics, Poor Comprehenders and Comorbid Reading Disorder. We included measures of single word reading, nonword reading, spelling, phonological processing, vocabulary, receptive language, nonverbal intelligence, selective attention and reading comprehension. We used analysis of variance, correlations and structural equation modelling to examine the relationship between SVA and reading. We fit two possible models: SVA Indirect and SVA Direct. The difference between these models was the inclusion of a direct path from SVA to reading comprehension. Results: We examined an indirect model, where SVA predicted reading comprehension through word decoding and listening comprehension, and a direct model, which included a pathway from SVA to reading comprehension. Based on our analysis of variance and correlation results, we collapsed the Dyslexic, Poor Comprehenders and Comorbid Reading Disorder groups for the structural equation modelling. We found evidence that for Typical Readers, an indirect model was the best fit, whereas the direct model was the best model for children with a reading disorder. Conclusions: Selective visual attention is related to reading comprehension. This relationship differs for children with and without a reading disorder.

Background: The purpose of this study was to examine the relationship between selective visual attention (SVA), reading decoding, listening comprehension and reading comprehension in children with and without a reading disorder. Methods: We used longitudinal data from the Avon Longitudinal Study of Parents and Children. We split children into four groups: Typical Readers, Dyslexics, Poor Comprehenders and Comorbid Reading Disorder. We included measures of single word reading, nonword reading, spelling, phonological processing, vocabulary, receptive language, nonverbal intelligence, selective attention and reading comprehension. We used analysis of variance, correlations and structural equation modelling to examine the relationship between SVA and reading. We fit two possible models: SVA Indirect and SVA Direct. The difference between these models was the inclusion of a direct path from SVA to reading comprehension. Results: We examined an indirect model, where SVA predicted reading comprehension through word decoding and listening comprehension, and a direct model, which included a pathway from SVA to reading comprehension. Based on our analysis of variance and correlation results, we collapsed the Dyslexic, Poor Comprehenders and Comorbid Reading Disorder groups for the structural equation modelling. We found evidence that for Typical Readers, an indirect model was the best fit, whereas the direct model was the best model for children with a reading disorder. Conclusions: Selective visual attention is related to reading comprehension. This relationship differs for children with and without a reading disorder. [ABSTRACT FROM AUTHOR]

With the development and popularization of communication technology, massive amounts of information have brought great challenges to how people receive and process information. In this environment, people have begun to exhibit typical negative behaviors, such as forgetting to process the information, delaying replies, and reading without replying. Negative use behavior refers to the non-active and non-interactive behavior taken by users in an attempt to resist using information systems. To explore users' negative use behavior of instant messaging software, this study extracts characteristics of the negative use behavior through semi-structured interviews, constructs a conceptual model based on stimulus-organism-response (SOR) theory, and uses a structural equation model (SEM) to verify the proposed hypotheses. The results reveal that information overload, task load, and time pressure have a positive effect on individual information anxiety and psychological resistance. Information anxiety and psychological resistance have a positive effect on individual passive attention and delayed reply. We also found that perceived utility negatively mediates the relationship between individual perceptions and delayed reply. This study collects data from China with a cross-sectional design. Future research should include other countries, different online platforms and longitudinal methods to examine negative use behavior. The research conclusions present suggestions for operators to optimize software functions and users to improve multiple information literacy. • Information overload of instant messaging software affects users' information anxiety. • Task load and time pressure make users resistant to instant messaging software. • Users delay replying to messages when they feel information anxiety. • Users are less likely to delay replying to perceived useful information. [ABSTRACT FROM AUTHOR]

Modeling users' short-term dynamic and long-term static interests to enhance Point-of-Interests (POI) recommendation performance has shown lots of advantages. Since users' check-in records can be viewed as a graph network, methods based on Graph Neural Networks (GNNs) have recently shown promising applicability for POI recommendation. However, existing GNN-based works have the following shortcomings: (1) ignoring the impact of complex higher-order relationships between user-POI dynamics over time; and (2) ignoring the difference in POI importance that cannot effectively capture the imbalances of geographical influence among POIs. To address these challenges, we propose a novel Self-supervised Long-and Short-term model (SLS-REC) for POI recommendation. Specifically, we first design a spatio-temporal Hawkes attention hypergraph neural network to capture the spatial dependence and temporal evolution in users' short-term dynamic interests. Then we introduce a dynamic propagation mechanism of GNNs to learn the geographic influences underlying geographic imbalances among POIs. In addition, the contrastive learning framework over a fine-grained node dropout strategy is applied to maximize the mutual information of long and short-term interest representations. Finally, we adaptively unify the recommendation and self-supervised task with an attention-based mechanism to optimize the proposed SLS-REC model for POI recommendation. Experiments on real-world datasets show that the proposed model significantly outperforms state-of-the-art methods. • Complex interactions between POIs are overlooked. • Hypergraph with Hawkes attention mines the dynamics of users' interests. • Novel Geographic graph model capture asymmetric geographic influence. • Fine grained contrastive learning framework fosters performance improvements. [ABSTRACT FROM AUTHOR]