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Perceptual Learning, Roving and the Unsupervised Bias

Publications associées (33)

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Reinforcement learning: the effect of environment

Michael Herzog, He Xu

Reinforcement learning is a type of supervised learning, where reward is sparse and delayed. For example in chess, a series of moves is made until a sparse reward (win, loss) is issued, which makes it impossible to evaluate the value of a single move. Stil ...

2016

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

Linking perceptual learning with identical stimuli to imagery perceptual learning

Michael Herzog, Lukasz Grzeczkowski, Fred Mast, Elisa Tartaglia

Perceptual learning is usually thought to be exclusively driven by the stimuli presented during training (and the underlying synaptic learning rules). In some way, we are slaves of our visual experiences. However, learning can occur even when no stimuli ar ...

Association for Research in Vision and Ophthalmology2015

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

Perceptual Learning With Indiscriminable Stimuli

Lukasz Grzeczkowski, Fred Mast, Elisa Tartaglia

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

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

Models of Reward-Modulated Spike-Timing-Dependent Plasticity

Nicolas Frémaux

How do animals learn to repeat behaviors that lead to the obtention of food or other “rewarding” objects? As a biologically plausible paradigm for learning in spiking neural networks, spike-timing dependent plasticity (STDP) has been shown to perform well ...

EPFL2013

Perceptual learning, roving and the unsupervised bias

Michael Herzog, Wulfram Gerstner, Carl Kristoffer Aberg, Nicolas Frémaux

Perceptual learning improves perception through training. Perceptual learning improves with most stimulus types but fails when certain stimulus types are mixed during training (roving). This result is surprising because classical supervised and unsupervise ...

Elsevier2012

Perceptual Learning, Roving, and Synaptic Drift

Michael Herzog, Aaron Michael Clarke

Perceptual learning improves with most basic stimuli. Interestingly, performance does not improve when stimuli of two types are randomly presented during training (roving). For example, there is no perceptual learning when left or right bisection stimuli w ...

2012

Multiclass Transfer Learning from Unconstrained Priors

Barbara Caputo, Jie Luo, Tatiana Tommasi

The vast majority of transfer learning methods proposed in the visual recognition domain over the last years ad- dresses the problem of object category detection, assuming a strong control over the priors from which transfer is done. This is a strict condi ...

2011