Results 101 to 110 of about 2,613,907 (255)

Spoken word recognition in context: Evidence from Chinese ERP analyses

open access: yes, 2006
Two event-related potential (ERP) experiments were conducted to investigate spoken word recognition in Chinese and the effect of contextual constraints on this process.
H. Shu, Shu, H, Liu, YN, Wei, JH
core   +1 more source

Descriptors to Dynamics: A Materials and Device Perspective on in‐Materio Physical Reservoir Computing for Neuromorphic Edge Intelligence

open access: yesAdvanced Materials, EarlyView.
Intrinsic material dynamics are harnessed as computational resources for neuromorphic in‐materio physical reservoir computing. Defects, ionic motion, interfaces, percolation, geometry, and biasing shape transient states that provide fading memory, nonlinearity, and high‐dimensional projection for simple readout. A descriptor‐to‐dynamics framework links
Kshitij RB Singh   +5 more
wiley   +1 more source

Rime Priming Effects in Spoken Word Recognition. [PDF]

open access: yesExp Psychol, 2023
Dufour S, Mirault J, Grainger J.
europepmc   +1 more source

Information Transmission Strategies for Self‐Organized Robotic Aggregation

open access: yesAdvanced Robotics Research, EarlyView.
In this review, we discuss how information transmission influences the neighbor‐based self‐organized aggregation of swarm robots. We focus specifically on local interactions regarding information transfer and categorize previous studies based on the functions of the information exchanged.
Shu Leng   +5 more
wiley   +1 more source

Multimodal Engagement Assessment in Children During Invented Story Paradigm With a Social Robot

open access: yesAdvanced Robotics Research, EarlyView.
A multimodal framework is proposed to assess children's engagement during storytelling interactions with a social robot. Gaze, physiological, and behavioral data are combined and validated against observer ratings. An automated gaze‐labeling strategy is introduced, and supervised classifiers achieve high accuracy. The study supports scalable engagement
Laura Fiorini   +7 more
wiley   +1 more source

Multimodal Human–Robot Interaction Using Human Pose Estimation and Local Large Language Models

open access: yesAdvanced Robotics Research, EarlyView.
A multimodal human–robot interaction framework integrates human pose estimation (HPE) and a large language model (LLM) for gesture‐ and voice‐based robot control. Speech‐to‐text (STT) enables voice command interpretation, while a safety‐aware arbitration mechanism prioritizes gesture input for rapid intervention.
Nasiru Aboki   +2 more
wiley   +1 more source

The slow developmental timecourse of real-time spoken word recognition

open access: yesDevelopmental Psychology, 2015
This study investigated the developmental timecourse of spoken word recognition in older children using eye-tracking to assess how the real-time processing dynamics of word recognition change over development.
Hannah Rigler   +5 more
semanticscholar   +1 more source

Task modulation of disyllabic spoken word recognition in Mandarin Chinese: a unimodal ERP study

open access: yes, 2016
Using unimodal auditory tasks of word-matching and meaning-matching, this study investigated how the phonological and semantic processes in Chinese disyllabic spoken word recognition are modulated by top-down mechanism induced by experimental tasks. Both
Chang, Ruohan   +3 more
core   +1 more source

LLM‐Integrated Human–Robot Interaction System for Microrobots

open access: yesAdvanced Robotics Research, EarlyView.
This paper proposes an LLM‐based control framework for guiding microrobots using human natural language. This framework can convert the natural human speech into safe and executable command sets for reliable navigation in complex environments. The experimental results show high accuracy and robustness in task performance, demonstrating the potential of
Bairong Zhu, Amar Salehi, Tingting Yu
wiley   +1 more source

Overcoming Artificial Structures in Resolution‐Enhanced Hi‐C Data by Signal Decomposition and Multi‐Scale Attention

open access: yesAdvanced Science, EarlyView.
Deep‐learning‐based signal enhancement is an effective way to recover high‐resolution details from a low‐resolution chromatin contact map. However, due to computational challenges, existing methods commonly divide up the contact map into small patches and create artificial discontinuities at patch boundaries.
Qinyao Li   +6 more
wiley   +1 more source

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