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A Neural Active Inference Model of Perceptual-Motor Learning
The active inference framework (AIF) is a promising new computational framework grounded in contemporary neuroscience that can produce human-like behavior through reward-based learning.
RIT-Eyes: Rendering of near-eye images for eye-tracking applications
Deep neural networks for video-based eye tracking have demonstrated resilience to noisy environments, stray reflections, and low resolution.
Gaze-in-wild: A dataset for studying eye and head coordination in everyday activities
Our approach was to collect a novel, naturalistic, and multimodal dataset of eye+head movements when subjects performed everyday tasks while wearing a mobile eye tracker equipped with an inertial measurement unit and a 3D stereo camera.
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