Processing Argument Structure and Syntactic Complexity in People with Schizophrenia Spectrum Disorders

Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth of the results. For the analyses we use the R add-on package multcomp, which provides a convenient interface to the general approach adopted here. Copyright 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Show more

An information-processing model is outlined that predicts that performance on non-routine tasks can be impaired independently of performance on routine tasks. The model is related to views on frontal lobe functions, particularly those of Luria. Two methods of obtaining more rigorous tests of the model are discussed. One makes use of ideas from artificial intelligence to derive a task heavily loaded on planning abilities. A group of patients with left anterior lesions has a specific deficit on the task. Subsidiary investigations support the inference that this is a planning impairment. Show more

The advent of functional neuroimaging has allowed tremendous advances in our understanding of brain-language relationships, in addition to generating substantial empirical data on this subject in the form of thousands of activation peak coordinates reported in a decade of language studies. We performed a large-scale meta-analysis of this literature, aimed at defining the composition of the phonological, semantic, and sentence processing networks in the frontal, temporal, and inferior parietal regions of the left cerebral hemisphere. For each of these language components, activation peaks issued from relevant component-specific contrasts were submitted to a spatial clustering algorithm, which gathered activation peaks on the basis of their relative distance in the MNI space. From a sample of 730 activation peaks extracted from 129 scientific reports selected among 260, we isolated 30 activation clusters, defining the functional fields constituting three distributed networks of frontal and temporal areas and revealing the functional organization of the left hemisphere for language. The functional role of each activation cluster is discussed based on the nature of the tasks in which it was involved. This meta-analysis sheds light on several contemporary issues, notably on the fine-scale functional architecture of the inferior frontal gyrus for phonological and semantic processing, the evidence for an elementary audio-motor loop involved in both comprehension and production of syllables including the primary auditory areas and the motor mouth area, evidence of areas of overlap between phonological and semantic processing, in particular at the location of the selective human voice area that was the seat of partial overlap of the three language components, the evidence of a cortical area in the pars opercularis of the inferior frontal gyrus dedicated to syntactic processing and in the posterior part of the superior temporal gyrus a region selectively activated by sentence and text processing, and the hypothesis that different working memory perception-actions loops are identifiable for the different language components. These results argue for large-scale architecture networks rather than modular organization of language in the left hemisphere. Show more