Attention and load
Variations of attention and analysis of cognitive load.
SPARK · Cognitive AI · Cognitive Foundation Models
Pre-trained on many hours of EEG, physiological and behavioural data with documented experimental contexts, a foundation model learns general representations of how the brain works under load, then adapts to each use.
Variations of attention and analysis of cognitive load.
Fatigue and individual follow-up over time.
The study of learning, plasticity and cognitive ageing.
A model trained across many people, tasks and disciplines learns what they share; fine-tuning then makes it specific to a discipline or a person.
A general model is not automatically competent everywhere: each application requires its own scientific validation.
Models that learn how a brain evolves, adapts and learns.
Foundation models are trained on the documented recordings of the platform, raw signal kept, then adapted to a domain or a person with their own validation.
A model is pre-trained on many hours of documented cognitive data.
It is then adapted to a specific question, such as attention or fatigue, with less data.
Each adapted use is validated on its own before anyone relies on it.
A model pre-trained on a large set of data, then adapted to specific uses.
No. A general model is not automatically competent in every use: each application needs its own scientific validation.
Documented recordings of brain activity, physiology and behaviour, with their experimental context, under consent and pseudonymisation.
Cognitive AI models, Cognitive Digital Twin and Cognitive Foundation Models: modelling how human cognition works.
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