Results 11 to 20 of about 564,783 (303)

Measuring cognitive load [PDF]

Perspectives on Medical Education, 2018
None
John Sweller
doaj   +3 more sources

Performance of a cognitive load inventory during simulated handoffs: Evidence for validity [PDF]

SAGE Open Medicine, 2016
Background: Advancing patient safety during handoffs remains a public health priority. The application of cognitive load theory offers promise, but is currently limited by the inability to measure cognitive load types.
John Q Young, Christy K Boscardin, Savannah M van Dijk, Ruqayyah Abdullah, David M Irby, Justin L Sewell, Olle Ten Cate, Patricia S O’Sullivan   +7 more
doaj   +3 more sources

Cognitive load theory, educational research, and instructional design: some food for thought [PDF]

Instructional Science, 2009
Cognitive load is a theoretical notion with an increasingly central role in the educational research literature. The basic idea of cognitive load theory is that cognitive capacity in working memory is limited, so that if a learning task requires too much
Jong, T. de
core   +4 more sources

Cognitive Load in Pediatric Critical Care Medicine: Tsunamis and a Thousand Cuts [PDF]

Critical Care Explorations
IMPORTANCE:. Excessive cognitive load impairs task performance and contributes to burnout, but studies of cognitive load in pediatric critical care medicine (PCCM) settings are limited. OBJECTIVES:. To better understand cognitive load in an academic PCCM
Daniel E. Ehrmann, MD, MS, FABP, Sara N. Gallant, PhD, Sunkyung Yu, MS, Danny Eytan, MD, PhD, Elaine Gilfoyle, MD, FRCPC, Azadeh Assadi, PhD, Seth Gray, MD, FABP, Oshri Zaulan, MD, FRCPC, Mjaye Mazwi, MBChB, FABP   +8 more
doaj   +2 more sources

Asymmetric Spatial Processing Under Cognitive Load [PDF]

Frontiers in Psychology, 2018
Spatial attention allows us to selectively process information within a certain location in space. Despite the vast literature on spatial attention, the effect of cognitive load on spatial processing is still not fully understood. In this study we added cognitive load to a spatial processing task, so as to see whether it would differentially impact ...
Lien Naert, Mario Bonato, Mario Bonato, Wim Fias   +3 more
openaire   +10 more sources

Examining the Size of the Latent Space of Convolutional Variational Autoencoders Trained With Spectral Topographic Maps of EEG Frequency Bands

IEEE Access, 2022
Dimensionality reduction and the automatic learning of key features from electroencephalographic (EEG) signals have always been challenging tasks. Variational autoencoders (VAEs) have been used for EEG data generation and augmentation, denoising, and ...
Taufique Ahmed, Luca Longo
doaj   +1 more source

Explaining Deep Q-Learning Experience Replay with SHapley Additive exPlanations

Machine Learning and Knowledge Extraction, 2023
Reinforcement Learning (RL) has shown promise in optimizing complex control and decision-making processes but Deep Reinforcement Learning (DRL) lacks interpretability, limiting its adoption in regulated sectors like manufacturing, finance, and healthcare.
Robert S. Sullivan, Luca Longo
doaj   +1 more source

Interpreting Disentangled Representations of Person-Specific Convolutional Variational Autoencoders of Spatially Preserving EEG Topographic Maps via Clustering and Visual Plausibility

Information, 2023
Dimensionality reduction and producing simple representations of electroencephalography (EEG) signals are challenging problems. Variational autoencoders (VAEs) have been employed for EEG data creation, augmentation, and automatic feature extraction.
Taufique Ahmed, Luca Longo
doaj   +1 more source

On the Dimensionality and Utility of Convolutional Autoencoder’s Latent Space Trained with Topology-Preserving Spectral EEG Head-Maps

Machine Learning and Knowledge Extraction, 2022
Electroencephalography (EEG) signals can be analyzed in the temporal, spatial, or frequency domains. Noise and artifacts during the data acquisition phase contaminate these signals adding difficulties in their analysis.
Arjun Vinayak Chikkankod, Luca Longo
doaj   +1 more source

Modeling Cognitive Load as a Self-Supervised Brain Rate with Electroencephalography and Deep Learning

Brain Sciences, 2022
The principal reason for measuring mental workload is to quantify the cognitive cost of performing tasks to predict human performance. Unfortunately, a method for assessing mental workload that has general applicability does not exist yet. This is due to
Luca Longo
doaj   +1 more source