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Perceptual learning and roving: Stimulus types and overlapping neural populations

Publications associées (35)

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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

Dependent Object Types

Nada Amin

A scalable programming language is one in which the same concepts can describe small as well as large parts. Towards this goal, Scala unifies concepts from object and module systems. In particular, objects can contain type members, which can be selected as ...

EPFL2016

Implementing Higher-Kinded Types in Dotty

Martin Odersky, Guillaume André Fradji Martres, Dmytro Petrashko

dotty is a new, experimental Scala compiler based on DOT, the calculus of Dependent Object Types. Higher-kinded types are a natural extension of first-order lambda calculus, and have been a core construct of Haskell and Scala. As long as such types are jus ...

ACM2016

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

Improving the Performance of Scala Collections with Miniboxing

Martin Odersky, Vlad Ureche, Aymeric Genet

Using generics, Scala collections can be used to store different types of data in a type-safe manner. Unfortunately, due to the erasure transformation, the performance of generics is degraded when storing primitive types, such as integers and floating poin ...

2014

The second order pullback equation

Bernard Dacorogna, Olivier Kneuss, Gyula Csató

Let f, g be two closed k-forms over R-n. The pullback equation studies the existence of a diffeomorphism phi : R-n -> R-n such that phi*(g) = f. We prove two types of results. The first one sharpens some of the existing regularity results. The second one d ...

Springer2014

Different Neuronal Recording Schemes

Amir Hesam Salavati

Having access to neuronal recordings is essential for any network inference algorithm. However, the term ”recording” covers many different schemes which vary significantly in technology, type of output and abundance of data. This report aims to briefly ove ...

2014

Governance of competition and performance in European railways: An analysis of five cases

Matthias Finger

Based on extensive qualitative research, this article analyzes the governance of competition in European railways and relates this to their performance via five case studies covering the Netherlands, France, Germany, Sweden and the UK. Even though some tre ...

Elsevier Sci Ltd2014

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

Materials Taking a Lesson from Nature

Holger Frauenrath, Emmanuel Croisier, Liangfei Tian

Structural biomaterials with their often extraordinary properties and versatile functions are typically constructed from very limited sets of building blocks and types of supramolecular interactions. In this review we discuss how, inspired by nature's desi ...

Swiss Chemical Soc2013