AIMC Topic: Attention

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The effect of stress on prospective memory in robotic command and control.

Cognitive research: principles and implications
Remembering to carry out an intention at the appropriate time (prospective memory-PM) requires attentional resources that may be limited in stressful circumstances. PM failures in high-risk/high stress environments, such as military operations, can h...

Exploring preparatory reading in bidirectional sight and written translation through clustering analysis of eye-tracking data.

PloS one
Preparatory reading-the phase between a translator's initial reading of the source text and the production of the first word of the target text-remains underexplored despite its crucial role in both sight (SiT) and written translation (WT). This stud...

Use of computer vision analysis for labeling inattention periods in EEG recordings with visual stimuli.

Scientific reports
Electroencephalography (EEG) recordings with visual stimuli require detailed coding to determine the periods of participant's attention. Here we propose to use a supervised machine learning model and off-the-shelf video cameras only. We extract compu...

Decoding target discriminability and time pressure using eye and head movement features in a foraging search task.

Cognitive research: principles and implications
In military operations, rapid and accurate decision-making is crucial, especially in visually complex and high-pressure environments. This study investigates how eye and head movement metrics can infer changes in search behavior during a naturalistic...

Automatic detection of cognitive events using machine learning and understanding models' interpretations of human cognition.

Scientific reports
The pupillary response is a valuable indicator of cognitive workload, capturing fluctuations in attention and arousal governed by the autonomic nervous system. Cognitive events, defined as the initiation of mental processes, are closely linked to cog...

An interactive information based DCNN-BiLSTM model with dual attention mechanism for facial expression recognition.

Scientific reports
Human's facial expressions and emotions have direct impact on their action and decision-making abilities. Basic CNN models are complexity of speeding up the operation to minimize the complexity. In this paper, we have proposed a Deep Convolutional Ne...

Attention-based multimodal deep learning for interpretable and generalizable prediction of pathological complete response in breast cancer.

Journal of translational medicine
BACKGROUND: Accurate prediction of pathological complete response (pCR) to neoadjuvant chemotherapy has significant clinical utility in the management of breast cancer treatment. Although multimodal deep learning models have shown promise for predict...

The application of improved AFCNN model for children's psychological emotion recognition.

Scientific reports
Children's mental health has become an increasingly prominent concern in modern education. However, insufficient attention from schools and families to children's psychological and emotional issues has exacerbated the problem. This study proposes a p...

A human activity recognition model based on deep neural network integrating attention mechanism.

Scientific reports
Human Activity Recognition (HAR) is crucial in multiple fields. Existing HAR techniques include manual feature extraction, codebook-based methods, and deep learning, each with limitations. This paper presents DCAM-Net (DeepConvAttentionMLPNet), a nov...

Attentional responses in toddlers: A protocol for assessing the impact of a robotic animated animal and a real dog.

PloS one
BACKGROUND: Attentional processes in toddlers are characterized by a state of alertness in which they focus their waking state for short periods. It is essential to develop assessment and attention stimulation protocols from an early age to improve t...