Attention Dynamics
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
Attention Dynamics and Adaptive Decision Support in C5ISR: A Recurrence Quantification Analysis of Visual and Multimodal Attention Guidance Effects on Mission Performance
arXiv:2606.02382v1 Announce Type: new Abstract: Modern command, control, communications, computers, cyber, intelligence, surveillance, and reconnaissance (C5ISR) environments place substantial attentional demands on mission commanders. Failures in attention allocation in these high-risk settings can have severe operational consequences. This study investigates the efficacy of gaze-driven, attention-guided adaptive decision support tools, including visual-only and multimodal designs, in a...
MVCL-DAF++: Enhancing Multimodal Intent Recognition via Prototype-Aware Contrastive Alignment and Coarse-to-Fine Dynamic Attention Fusion
arXiv:2509.17446v3 Announce Type: replace Abstract: Multimodal intent recognition (MMIR) suffers from weak semantic grounding and poor robustness under noisy or rare-class conditions. We propose MVCL-DAF++, which extends MVCL-DAF with two key modules: (1) Prototype-aware contrastive alignment, aligning instances to class-level prototypes to enhance semantic consistency; and (2) Coarse-to-fine attention fusion, integrating global modality summaries with token-level features for hierarchical...
MVCL-DAF++: Enhancing Multimodal Intent Recognition via Prototype-Aware Contrastive Alignment and Coarse-to-Fine Dynamic Attention Fusion
arXiv:2509.17446v4 Announce Type: replace Abstract: Multimodal intent recognition (MMIR) suffers from weak semantic grounding and poor robustness under noisy or rare-class conditions. We propose MVCL-DAF++, which extends MVCL-DAF with two key modules: (1) Prototype-aware contrastive alignment, aligning instances to class-level prototypes to enhance semantic consistency; and (2) Coarse-to-fine attention fusion, integrating global modality summaries with token-level features for hierarchical...
Positional versus Symbolic Attention Heads: Learning Dynamics, RoPE Geometry, and Length Generalization
new Abstract: Transformer-based language models are widespread in today's society. As such, understanding the mechanisms by which they solve structured tasks and predicting how they may behave in novel scenarios is of great importance for safe deployment. We study the learning dynamics of attention heads in a controlled setting by training a decoder-only Transformer (GPT-J) on two structurally equivalent multi-hop reasoning tasks: a number task requiring positional reasoning and a letter...
Asymmetric neural dynamics of visuospatial attention in autism spectrum disorder
Background: Selective attention enables the prioritization of behaviorally relevant information in complex sensory environments. Despite substantial evidence for altered attention in autism spectrum disorder (ASD), the neurophysiological mechanisms underlying these differences remain poorly understood. Here, we integrate high-density electroencephalography (EEG), pupillometry, and behavioral measures collected during a cued covert visuospatial selective attention task to characterize...
Surprised by Attention: Predictable Query Dynamics for Time Series Anomaly Detection
arXiv:2603.12916v3 Announce Type: replace Abstract: Multivariate time series anomalies often manifest as shifts in cross-channel dependencies rather than simple amplitude excursions. In autonomous driving, for instance, a steering command might be internally consistent but decouple from the resulting lateral acceleration. Residual-based detectors can miss such anomalies when flexible sequence models still reconstruct signals plausibly despite altered coordination.
LoCAtion: Long-time Collaborative Attention Framework for High Dynamic Range Video Reconstruction
arXiv:2603.14377v2 Announce Type: replace Abstract: Prevailing High Dynamic Range (HDR) video reconstruction methods are fundamentally trapped in a fragile alignment-and-fusion paradigm. While explicit spatial alignment can successfully recover fine details in controlled environments, it becomes a severe bottleneck in unconstrained dynamic scenes. By forcing rigid alignment across unpredictable motions and varying exposures, these methods inevitably translate registration errors into severe...
Intrinsic Population Dynamics are a Neuronal Substrate for Visual Attention
Perception results from a dynamic interplay between the feedforward processing of sensory stimuli and intrinsic neural activity, which is often dismissed as noise. To tailor perceptual processes to the organism's current needs on a continuous, moment-to-moment basis, intrinsic dynamics - rather than just being noise - have been suggested to reflect prior expectations, task demands, and attentional focus. Here, we identify a novel signature of attentive state in which intrinsic, collective...
Dynamic Spectral Denoising with Global-Context Attention for Multi-Behavior Recommendation
arXiv:2606.02417v1 Announce Type: new Abstract: Multi-behavior recommendation improves target-behavior prediction by exploiting heterogeneous auxiliary feedback (e.g., view, collect, and cart), yet its robustness is undermined by behavior-dependent noise and inconsistency. We argue that the key bottleneck is a representation-level failure caused by two coupled heterogeneities. First, intra-behavior representation entanglement arises when multi-hop propagation blends incidental signals with...
Hamiltonian-Inspired Attention Mechanism for Scalable RF Transmitter Fingerprinting
Announce Type: cross Abstract: Radio-frequency (RF) fingerprinting identifies wire-less transmitters using hardware-induced imperfections present in baseband I/Q signals. However, deep learning models often degrade under receiver and channel distribution shifts, particularly as transmitter populations grow. This work proposes the Hamiltonian Transformer, a physics-informed attention architecture that enforces norm preserving value dynamics within each attention head using a learned...