This article describes some aspects of the underlying logic of the attention schema theory (AST) of subjective consciousness. It is a theory that distinguishes between what the brain actually, physically has, what is represented by information models constructed in the brain, what higher cognition thinks based on access to those models and what speech machinery claims based on the information within higher cognition. It is a theory of how we claim to have an essentially magical, subjective mind, based on the impoverishment and reduction of information along that pathway. While the article can stand on its own as a brief account of some critical aspects of AST, it specifically addresses questions and concerns raised by a set of commentaries on a target article.
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http://dx.doi.org/10.1080/02643294.2020.1761782 | DOI Listing |
Neural Netw
December 2024
College of Computer Science, Zhejiang University, Hangzhou, 310027, China; Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University, Hangzhou, 310027, China. Electronic address:
Graph Neural Networks (GNNs) have achieved remarkable success in various graph mining tasks by aggregating information from neighborhoods for representation learning. The success relies on the homophily assumption that nearby nodes exhibit similar behaviors, while it may be violated in many real-world graphs. Recently, heterophilous graph neural networks (HeterGNNs) have attracted increasing attention by modifying the neural message passing schema for heterophilous neighborhoods.
View Article and Find Full Text PDFClin Psychol Psychother
December 2024
School of Behavioural and Health Sciences, Australian Catholic University, Fitzroy, Victoria, Australia.
Background: Dissociation is an underresearched and important clinical construct associated with impaired functioning and poor quality of life. Improved understanding of the modifiable correlates of dissociation can inform early detection and effective treatments. The aim of this systematic review and meta-analysis was to synthesise the evidence on the associations between dissociative symptoms and early maladaptive schemas (EMSs).
View Article and Find Full Text PDFDatabase (Oxford)
December 2024
Intelligent Computing Department, Institute of Medical Information & Library, Chinese Academy of Medical Sciences/Peking Union Medical College, No. 3 Yabao Road, Beijing 100020, China.
Atherosclerotic cerebrovascular disease could result in a great number of deaths and disabilities. However, it did not acquire enough attention. Less information, statistics, or data on the disease has been revealed.
View Article and Find Full Text PDFNeural Netw
December 2024
College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China; Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350116, China. Electronic address:
Recently, heterogeneous graphs have attracted widespread attention as a powerful and practical superclass of traditional homogeneous graphs, which reflect the multi-type node entities and edge relations in the real world. Most existing methods adopt meta-path construction as the mainstream to learn long-range heterogeneous semantic messages between nodes. However, such schema constructs the node-wise correlation by connecting nodes via pre-computed fixed paths, which neglects the diversities of meta-paths on the path type and path range.
View Article and Find Full Text PDFbioRxiv
November 2024
Department of Psychology, Vanderbilt University, Nashville, TN 37240.
Intelligent behavior involves mentally arranging learned information in novel ways and is particularly well developed in humans. While nonhuman primates (NHP) will learn to arrange new items in complex serial order and re-arrange neighboring items within that order, it has remained contentious whether they are capable to re-assign items more flexibly to non-adjacent positions. Such mental re-indexing is facilitated by inferring the latent temporal structure of experiences as opposed to learning serial chains of item-item associations.
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