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Linking perceptual learning with identical stimuli to imagery perceptual learning

Publications associées (42)

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Modeling exploration strategies to predict student performance within a learning environment and beyond

Modeling and predicting student learning is an important task in computer-based education. A large body of work has focused on representing and predicting student knowledge accurately. Existing techniques are mostly based on students' performance and on ti ...

ACM2017

Our brain continuously self-organizes to construct and maintain an internal representation of the world based on the information arriving through sensory stimuli. Remarkably, cortical areas related to different sensory modalities appear to share the same f ...

EPFL2016

Hyper-specificity of perceptual learning

Lukasz Grzeczkowski

Perceptual learning is the ability to improve perception through practice. Perceptual learning is usually specific for the task and the stimulus features trained with. For example, training orientation discrimination with vertically oriented stimuli does n ...

EPFL2016

Does computational neuroscience need new synaptic learning paradigms?

Wulfram Gerstner, Johanni Michael Brea

Computational neuroscience is dominated by a few paradigmatic models, but it remains an open question whether the existing modelling frameworks are sufficient to explain observed behavioural phenomena in terms of neural implementation. We take learning and ...

Elsevier Science Bv2016

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Perceptual learning is learning to perceive. For example, in a bisection task three parallel lines are presented. The central line is slightly offset towards the right or the left outer line. Observers indicate the offset direction. Training greatly improv ...

2014

Perceptual Learning With Only One Stimulus

Michael Herzog, Lukasz Grzeczkowski, Fred Mast, Elisa Tartaglia

Perceptual learning is learning to see. For example in a bisection task, three parallel lines are presented with the central line slightly offset towards the right or the left outer line. Participants indicate the offset direction. Training gradually impro ...

2014

Diffusion adaptation over networks

Ali H. Sayed

Adaptive networks are well-suited to perform decentralized information processing and optimization tasks and to model various types of self-organized and complex behavior encountered in nature. Adaptive networks consist of a collection of agents with proce ...

Elsevier2014

Deleterious effects of roving on learned tasks

Michael Herzog, Lukasz Grzeczkowski, Aaron Michael Clarke, Fred Mast

In typical perceptual learning experiments, one stimulus type (e.g., a bisection stimulus offset either to the left or right) is presented per trial. In roving, two different stimulus types (e.g., a 30' and a 20' wide bisection stimulus) are randomly inter ...

Pergamon-Elsevier Science Ltd2014

Learning Categories from Few Examples with Multi Model Knowledge Transfer

Barbara Caputo, Tatiana Tommasi

Learning a visual object category from few samples is a compelling and challenging problem. In several real-world applications collecting many annotated data is costly and not always possible. However a small training set does not allow to cover the high i ...

Idiap2013

No Stress With Perceptual Learning.

Michael Herzog, Maria del Carmen Sandi Perez, Aaron Michael Clarke, Carl Kristoffer Aberg

While stress has been shown to affect higher-level cognitive learning (such as learning to remember pictures of different emotional salience), it does not affect perceptual learning. ...

2013