Publication

Interleaving bisection stimuli - randomly or in sequence - does not disrupt perceptual learning, it just makes it more difficult

Publications associées (32)

MATHICSE Technical Report : Multi-index stochastic collocation convergence rates for random PDEs with parametric regularity

We analyze the recent Multi-index Stochastic Collocation (MISC) method for computing statistics of the solution of a partial differential equation with random data, where the random coefficient is parametrized by means of a countable sequence of terms in a ...

MATHICSE2015

Convergence of quasi-optimal Stochastic Galerkin methods for a class of PDES with random coefficients

Fabio Nobile, Lorenzo Tamellini

In this work we consider quasi-optimal versions of the Stochastic Galerkin method for solving linear elliptic PDEs with stochastic coefficients. In particular, we consider the case of a finite number

N

of random inputs and an analytic dependence of the s ...

2014

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

MATHICSE Technical Report : Convergence of quasi-optimal stochastic Galerkin methods for a class of PDES with random coefficients

Fabio Nobile, Lorenzo Tamellini

In this work we consider quasi-optimal versions of the Stochastic Galerkin Method for solving linear elliptic PDEs with stochastic coeffcients. In particular, we consider the case of a finite number N of random inputs and an analytic dependence of the solu ...

MATHICSE2012

A Mobile World of Security

Christine Neuberg

Security in mobile communications is a topic of increasing relevance in everyday life. We all use mobile devices for everyday communications, maybe even for exchanging confidential information with the work place. This requires security systems that are re ...

EPFL2011

Applications of Derandomization Theory in Coding

Mahdi Cheraghchi Bashi Astaneh

Randomized techniques play a fundamental role in theoretical computer science and discrete mathematics, in particular for the design of efficient algorithms and construction of combinatorial objects. The basic goal in derandomization theory is to eliminate ...

EPFL2010

Learning under stress: the inverted-U-shape function revisited

Maria del Carmen Sandi Perez, Maria Isabel Cordero Campana, Basira Salehi

Although the relationship between stress intensity and memory function is generally believed to follow an inverted-U-shaped curve, strikingly this phenomenon has not been demonstrated under the same experimental conditions. We investigated this phenomenon ...

Cold Spring Harbor Laboratory Press2010

An anisotropic sparse grid stochastic collocation method for partial differential equations with random input data

Fabio Nobile

This work proposes and analyzes an anisotropic sparse grid stochastic collocation method for solving partial differential equations with random coefficients and forcing terms ( input data of the model). The method consists of a Galerkin approximation in th ...

Society for Industrial and Applied Mathematics2008

A sparse grid stochastic collocation method for partial differential equations with random input data

Fabio Nobile

This work proposes and analyzes a Smolyak-type sparse grid stochastic collocation method for the approximation of statistical quantities related to the solution of partial differential equations with random coeffcients and forcing terms ( input data of the ...

Society for Industrial and Applied Mathematics2008

When does roving disrupt perceptual learning?

Michael Herzog, Elisa Tartaglia, Carl Kristoffer Aberg

Training with one type of a visual stimulus usually improves performance. When observers train with two or more stimulus types presented in random order (so-called roving), performance improves for certain stimulus types but not for others. To understand w ...

2008

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Convergence of quasi-optimal Stochastic Galerkin methods for a class of PDES with random coefficients

Fabio Nobile, Lorenzo Tamellini

In this work we consider quasi-optimal versions of the Stochastic Galerkin method for solving linear elliptic PDEs with stochastic coefficients. In particular, we consider the case of a finite number

N

of random inputs and an analytic dependence of the s ...

2014