Neural modeling and imaging of the cortical interactions underlying syllable production

This paper describes a neural model of speech acquisition and production that accounts for a wide range of acoustic, kinematic, and neuroimaging data concerning the control of speech movements. The model is a neural network whose components correspond to regions of the cerebral cortex and cerebellum, including premotor, motor, auditory, and somatosensory cortical areas. Computer simulations of the model verify its ability to account for compensation to lip and jaw perturbations during speech. Specific anatomical locations of the model's components are estimated, and these estimates are used to simulate fMRI experiments of simple syllable production.     

« Conciliation vie privée vie professionnelle » : plusieurs termes pour une même réalité ? Proposition de modélisation à travers l’usage français

The aim of this paper is to define the vocabulary used about interactions between people's work and their private lives. Several terms exist and are used as synonyms despite of their slightly different meanings are different: balance, conflict, interaction, enrichment; personal life, family life… Based on an analysis of 45 French articles in psychology, management and human resources, we notice that the terms used are rarely defined. In a thematic analysis of these articles, we suggest a model which defines all words usually used.     

Assessing the distinguishability of models and the informativeness of data

A difficulty in the development and testing of psychological models is that they are typically evaluated solely on their ability to fit experimental data, with little consideration given to their ability to fit other possible data patterns. By examining how well model A fits data generated by model B, and vice versa (a technique that we call landscaping), much safer inferences can be made about the meaning of a model's fit to data. We demonstrate the landscaping technique using four models of retention and 77 historical data sets, and show how the method can be used to: (1) evaluate the distinguishability of models, (2) evaluate the informativeness of data in distinguishing between models, and (3) suggest new ways to distinguish between models. The generality of the method is demonstrated in two other research areas (information integration and categorization), and its relationship to the important notion of model complexity is discussed.