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Modeling perceptual learning: why mice do not play backgammon

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On the Learning Behavior of Adaptive Networks - Part I: Transient Analysis

Ali H. Sayed

This paper carries out a detailed transient analysis of the learning behavior of multiagent networks, and reveals interesting results about the learning abilities of distributed strategies. Among other results, the analysis reveals how combination policies ...

2015

Contour erasure and filling-in: Old simulations account for most new observations

Gregory Francis

Three recent studies used similar stimulus sequences to investigate mechanisms for brightness perception. Anstis and Greenlee (2014) demonstrated that adaptation to a flickering black and white outline erased the visibility of a subsequent target shape def ...

Pion Ltd2015

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

On the Influence of Informed Agents on Learning and Adaptation Over Networks

Ali H. Sayed

Adaptive networks consist of a collection of agents with adaptation and learning abilities. The agents interact with each other on a local level and diffuse information across the network through their collaboration. In this work, we consider two types of ...

Institute of Electrical and Electronics Engineers, Inc., 345 E. 47 th St. NY NY 10017-2394 United States2013

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 the Unsupervised Bias

Michael Herzog, Wulfram Gerstner, Aaron Michael Clarke

Perceptual learning is reward-based. A recent mathematical analysis showed that any reward-based learning system can learn two tasks only when the mean reward is identical for both tasks [Frémaux, Sprekeler and Gerstner, 2010, The Journal of Neuroscience, ...

2011

Perceptual learning in a nonretinotopic frame of reference

Michael Herzog, Thomas Otto

Perceptual learning is the ability to improve perception through practice. Perceptual learning is usually specific for the task and features learned. For example, improvements in performance for a certain stimulus do not transfer if the stimulus is rotated ...

Wiley-Blackwell2010

Does perceptual learning suffer from retrograde interference?

Michael Herzog, Carl Kristoffer Aberg

In motor learning, training a task B can disrupt improvements of performance of a previously learned task A, indicating that learning needs consolidation. An influential study suggested that this is the case also for visual perceptual learning [1]. Using t ...

Public Library of Science2010

Perceptual learning: why mice do not play backgammon

Elisa Tartaglia

Perceptual learning is the ability to modify perception through practice. As a form of brain plasticity, perceptual learning has been studied for more than thirty years in different fields including psychology, neurophysiology and computational neuroscienc ...

EPFL2009

Stimulus sampling as an exploration mechanism for fast reinforcement learning

Eleni Vasilaki, Walter Senn

Reinforcement learning in neural networks requires a mechanism for exploring new network states in response to a single, nonspecific reward signal. Existing models have introduced synaptic or neuronal noise to drive this exploration. However, those types o ...

Springer Verlag2009