Prior expectation mediates neural adaptation to repeated sounds in the auditory cortex: An MEG study

Contains fulltext : 99626.pdf (publisher's version ) (Open Access)Repetition suppression, the phenomenon that the second presentation of a stimulus attenuates neural activity, is typically viewed as an automatic consequence of repeated stimulus presentation. However, a recent neuroimaging study has suggested that repetition suppression may be driven by top-down expectations. Here we examined whether and when repetition suppression can be modulated by top-down expectation. Participants listened to auditory stimuli in blocks where tone repetitions were either expected or unexpected, while we recorded ongoing neural activity using magnetoencephalography. We found robust repetition suppression in the auditory cortex for repeated tones. Interestingly, this reduction was significantly larger for expected than unexpected repetitions, both in terms of evoked activity and gamma-band synchrony. These findings indicate a role of top-down expectation in generating repetition suppression and are in line with predictive coding models of perception, in which the difference between expected and actual input is propagated from lower to higher cortical areas.6 p

A bicycle can be balanced by stochastic optimal feedback control but only with accurate speed estimates

Balancing a bicycle is typical for the balance control humans perform as a part of a whole range of behaviors (walking, running, skating, skiing, etc.). This paper presents a general model of balance control and applies it to the balancing of a bicycle. Balance control has both a physics (mechanics) and a neurobiological component. The physics component pertains to the laws that govern the movements of the rider and his bicycle, and the neurobiological component pertains to the mechanisms via which the central nervous system (CNS) uses these laws for balance control. This paper presents a computational model of this neurobiological component, based on the theory of stochastic optimal feedback control (OFC). The central concept in this model is a computational system, implemented in the CNS, that controls a mechanical system outside the CNS. This computational system uses an internal model to calculate optimal control actions as specified by the theory of stochastic OFC. For the computational model to be plausible, it must be robust to at least two inevitable inaccuracies: (1) model parameters that the CNS learns slowly from interactions with the CNS-attached body and bicycle (i.e., the internal noise covariance matrices), and (2) model parameters that depend on unreliable sensory input (i.e., movement speed). By means of simulations, I demonstrate that this model can balance a bicycle under realistic conditions and is robust to inaccuracies in the learned sensorimotor noise characteristics. However, the model is not robust to inaccuracies in the movement speed estimates. This has important implications for the plausibility of stochastic OFC as a model for motor control

The correction of a formula in the speed-accuracy decomposition technique of Meyer, Irwin, Osman, and Kounios (1988)

Item does not contain fulltextThe speed-accuracy decomposition (SAD) technique was developed by Meyer et al. (Psychol. Rev. 95 (1988) 183) for studying the course of information accumulation during stimulus processing. This technique aims at calculating the so-called sophisticated-guessing probability. In this note, it is shown that Meyer et al. (Psychol. Rev. 95 (1988) 183) used an incorrect formula for calculating the sophisticated-guessing probability. The correct formula is derived and the implications for the SAD technique are discussed. (C) 2003 Elsevier Inc. All rights reserved

Somatosensory demands modulate muscular beta oscillations, independent of motor demands

Contains fulltext : 119976.pdf (publisher's version ) (Open Access)9 p

A resampling method for estimating the signal subspace of spatio-temporal EEG/MEG data

Item does not contain fulltextSource localization using spatio-temporal electroencephalography (EEG) and magnetoencephalography (MEG) data is usually performed by means of signal subspace methods. The first step of these methods is the estimation of a set of vectors that spans a subspace containing as well as possible the signal of interest. This estimation is usually performed by means of a singular value decomposition (SVD) of the data matrix: The rank of the signal subspace (denoted by r) is estimated from a plot in which the singular values are plotted against their rank order, and the signal subspace itself is estimated by the first r singular vectors. The main problem with this method is that it is strongly affected by spatial covariance in the noise. Therefore, two methods are proposed that are much less affected by this spatial covariance, and old and a new method. The old method involves prewhitening of the data matrix, making use of an estimate of the spatial noise covariance matrix. The new method is based on the matrix product of two average data matrices, resulting from a random partition of a set of stochastically independent replications of the spatio-temporal data matrix. The estimated signal subspace is obtained by first filtering out the asymmetric and negative definite components of this matrix product and then retaining the eigenvectors that correspond to the r largest eigenvalues of this filtered data matrix. The main advantages of the partition-based eigen decomposition over prewhited SVD is that 1) it does not require an estimate of the spatial noise covariance matrix and 2b) that it allows one to make use of a resampling distribution (the so-called partitioning distribution) as a natural quantification of the uncertainty in the estimated rank. The performance of three methods (SVD with and without prewhitening, and the partition-based method) is compared in a simulation study. From this study, it could be concluded that prewhited SVD and the partition-based eigen decomposition perform equally well when the amplitude time series are constant, but that the partition-based method performs better when the amplitude time series are variable

Testing the race model inequality: A nonparametric approach

Item does not contain fulltextThis paper introduces a nonparametric procedure for testing the race model explanation of the redundant signals effect. The null hypothesis is the race model inequality derived from the race model by Miller (Cognitive Psychol. 14 (1982) 247). The construction of a nonparametric test is made possible by a small change in the usual experimental procedure. This change involves that whenever only a single stimulus is presented, its modality is determined independently from the previous trials. It is shown that the test procedure is consistent against every violation of the null hypothesis. The test procedure is developed for data from a single participant, but it can easily be extended to the testing of the null hypothesis across participants, and this is also shown in the paper. (C) 2003 Elsevier Inc. All rights reserved

Statistically comparing EEG/MEG waveforms through successive significant univariate tests: How bad can it be?

Contains fulltext : 138699pre.pdf (preprint version ) (Open Access)When making statistical comparisons, the temporal dimension of the EEG signal introduces problems. Guthrie and Buchwald (1991) proposed a formally correct statistical approach that deals with these problems: comparing waveforms by counting the number of successive significant univariate tests and then contrasting this number to a well-chosen critical value. However, in the literature, this method is often used inappropriately. Using real EEG data and Monte Carlo simulations, we examined the problems associated with the incorrect use of this approach under circumstances often encountered in the literature. Our results show inflated false-positive or false-negative rates depending on parameters of the data, including filtering. Our findings suggest that most applications of this method result in an inappropriate familywise error rate control. Solutions and alternative methods are discussed.4 p

Mogelijkheden van beperking van statistische uitzuivering binnen schooleffectiviteitsonderzoek

Item does not contain fulltextThe objective of this article is to give a didactic presentation of the contribution of statistics to school effectiviness research. Our advice for the methodology of school effectiviness is not new. The contribution of this article is twofold: (a) precise formulation of the research question and (b) an evaluation of the existing methods as an answer to this question. We start from a precise definition of what we want to compute (estimate): the school effect. To make an unbiased estimate of the school effect, one has to take into account differences between the schools incoming students and differences between the schools in drop-outs. The starting-point for a possible solution for both of these problems in prediction on the basis of covariates. It is not guaranteed that this solution produces an unbiased estimate, but the solution will be better as more covariates are used as a basis for the prediction

Phase-amplitude coupling in human electrocorticography is spatially distributed and phase diverse

Contains fulltext : 99195.pdf (publisher's version ) (Open Access)Spatially distributed phase-amplitude coupling (PAC) is a possible mechanism for selectively routing information through neuronal networks. If so, two key properties determine its selectivity and flexibility, phase diversity over space, and frequency diversity. To investigate these issues, we analyzed 42 human electrocorticographic recordings from 27 patients performing a working memory task. We demonstrate that (1) spatially distributed PAC occurred at distances > 10 cm, (2) involved diverse preferred coupling phases, and (3) involved diverse frequencies. Using a novel technique [N-way decomposition based on the PARAFAC (for Parallel Factor analysis) model], we demonstrate that (4) these diverse phases originated mainly from the phase-providing oscillations. With these properties, PAC can be the backbone of a mechanism that is able to separate spatially distributed networks operating in parallel.13 p