Results 41 to 50 of about 2,790,656 (286)
NeuroImage, 2020 Attention is a critical cognitive function, allowing humans to select, enhance, and sustain focus on information of behavioral relevance. Attention contains dissociable neural and psychological components.
Esther X.W. Wu +8 more
doaj +1 more source
Imaging attention networks [PDF]
NeuroImage, 2012 The study of attention has largely been about how to select among the various sensory events but also involves the selection among conflicting actions. Prior to the late 1980s, locating bottlenecks between sensory input and response dominated these studies, a different view was that attentional limits involved the importance of maintaining behavioral ...
openaire +3 more sources
Convolutional Self-Attention Networks [PDF]
Proceedings of the 2019 Conference of the North, 2019 Self-attention network (SAN) has recently attracted increasing interest due to its fully parallelized computation and flexibility in modeling dependencies. It can be further enhanced with multi-headed attention mechanism by allowing the model to jointly attend to information from different representation subspaces at different positions (Vaswani et al.,
Baosong Yang +4 more
openaire +6 more sources
CoRR, 2016 In this work, we propose Attentive Pooling (AP), a two-way attention mechanism for discriminative model training. In the context of pair-wise ranking or classification with neural networks, AP enables the pooling layer to be aware of the current input pair, in a way that information from the two input items can directly influence the computation of ...
Cícero Nogueira dos Santos +3 more
openaire +2 more sources
An attention-modulated associative network [PDF]
Learning & Behavior, 2010 We present an elemental model of associative learning that describes interactions between stimulus elements as a process of competitive normalization. Building on the assumptions laid out in Harris (2006), stimuli are represented as an array of elements that compete for attention according to the strength of their input.
Harris, Justin A, Livesey, Evan J
openaire +4 more sources
Hyperbolic Graph Attention Network [PDF]
IEEE Transactions on Big Data, 2021 Graph neural network (GNN) has shown superior performance in dealing with graphs, which has attracted considerable research attention recently. However, most of the existing GNN models are primarily designed for graphs in Euclidean spaces. Recent research has proven that the graph data exhibits non-Euclidean latent anatomy.
Yiding Zhang +4 more
openaire +3 more sources
Functional Specialization in the Attention Network [PDF]
Annual Review of Psychology, 2020 Spatial attention is comprised of neural mechanisms that boost sensory processing at a behaviorally relevant location while filtering out competing information. The present review examines functional specialization in the network of brain regions that directs such preferential processing.
Ian C, Fiebelkorn, Sabine, Kastner
openaire +2 more sources
Psychiatry Research Communications, 2023 Reduced attentional functioning has been identified as an important factor in depression etiology and maintenance. However, current research does not fully take into account the large heterogeneity of depression, for example identifying for whom and how ...
Brage Kraft +6 more
doaj +1 more source
Special attention network [PDF]
Physica A: Statistical Mechanics and its Applications, 2004 5 pages, no ...
openaire +2 more sources
Attention in Attention Network for Image Super-Resolution
CoRR, 2021 Convolutional neural networks have allowed remarkable advances in single image super-resolution (SISR) over the last decade. Among recent advances in SISR, attention mechanisms are crucial for high-performance SR models. However, the attention mechanism remains unclear on why and how it works in SISR.
Haoyu Chen 0003, Jinjin Gu, Zhi Zhang
openaire +2 more sources