Human Pose, Shape and Action
3D Pose from Images
2D Pose from Images
Beyond Motion Capture
Action and Behavior
Body Perception
Body Applications
Pose and Motion Priors
Clothing Models (2011-2015)
Reflectance Filtering
Learning on Manifolds
Markerless Animal Motion Capture
Multi-Camera Capture
2D Pose from Optical Flow
Body Perception
Neural Prosthetics and Decoding
Part-based Body Models
Intrinsic Depth
Lie Bodies
Layers, Time and Segmentation
Understanding Action Recognition (JHMDB)
Intrinsic Video
Intrinsic Images
Action Recognition with Tracking
Neural Control of Grasping
Flowing Puppets
Faces
Deformable Structures
Model-based Anthropometry
Modeling 3D Human Breathing
Optical flow in the LGN
FlowCap
Smooth Loops from Unconstrained Video
PCA Flow
Efficient and Scalable Inference
Motion Blur in Layers
Facade Segmentation
Smooth Metric Learning
Robust PCA
3D Recognition
Object Detection
Executive Functions Training
People's limited attentional resources are challenged by the high prevalence of potential distractors in daily life. How well people can cope with the arising demands is moderated by individual attention control abilities that have proven to mitigate potentially harmful effects (Wirzberger & Rey, 2018).
In this project, we develop a computer-based training application that can enhance individual attention control abilities when people get distracted. To promote metacognitive reinforcement learning, the training provides metacognitive feedback on the value of goal-directed attention when people distract themselves from a self-chosen task. Contrary to existing approaches that use artificial tasks in non-transferable situations, our approach re-designs real-world settings into optimal learning environments for stable and sustainable skill acquisition.
We evaluate the training in behavioral field experiments, where our participants use the application over a defined period of time in their daily study or work contexts. Based on the learned value of control (LVOC) model (Lieder, Shenhav, Musslick, & Griffiths, 2018), we model the development of attention control skills from the obtained human data.
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