Weak coherence,no theory of mind,or executive dysfunction? Solving the puzzle of pragmatic language disorders

Deficits in pragmatic language ability are common to a number of clinical populations, for example, right-hemisphere damage (RHD), Autism and traumatic brain injury (TBI). In these individuals the basic structural components of language may be intact, but the ability to use language to engage socially is impaired. Despite the nature of these difficulties being well documented, exactly what causes these difficulties is less clear. Furthermore, the current status of causal explanations for pragmatic difficulties across these populations is divergent and sometimes contradictory. This paper explores the empirical validity of three theories that attempt to explain pragmatic language impairment. It is recommended that a new, more convergent approach to investigating the causes of pragmatic language disability be adopted.     

Bayesian estimation and testing of structural equation models

The Gibbs sampler can be used to obtain samples of arbitrary size from the posterior distribution over the parameters of a structural equation model (SEM) given covariance data and a prior distribution over the parameters. Point estimates, standard deviations and interval estimates for the parameters can be computed from these samples. If the prior distribution over the parameters is uninformative, the posterior is proportional to the likelihood, and asymptotically the inferences based on the Gibbs sample are the same as those based on the maximum likelihood solution, for example, output from LISREL or EQS. In small samples, however, the likelihood surface is not Gaussian and in some cases contains local maxima. Nevertheless, the Gibbs sample comes from the correct posterior distribution over the parameters regardless of the sample size and the shape of the likelihood surface. With an informative prior distribution over the parameters, the posterior can be used to make inferences about the parameters underidentified models, as we illustrate on a simple errors-in-variables model.We thank David Spiegelhalter for suggesting applying the Gibbs sampler to structural equation models to the first author at a 1994 workshop in Wiesbaden. We thank Ulf Böckenholt, Chris Meek, Marijtje van Duijn, Clark Glymour, Ivo Molenaar, Steve Klepper, Thomas Richardson, Teddy Seidenfeld, and Tom Snijders for helpful discussions, mathematical advice, and critiques of earlier drafts of this paper.     

On matters of mind and body: regarding Descartes

In this paper the author considers Descartes’ place in current thinking about the mind‐body dilemma. The premise here is that in the history of ideas, the questions posed can be as significant as the answers acquired. Descartes’ paramount question was ‘How do we determine certainty?’ and his pursuit of an answer led to cogito ergo sum. His discovery simultaneously raised the question whether mind is separate from or unified with the body. Some who currently hold that brain and subjectivity are unified contend that the philosopher ‘split’ mind from body and refer to ‘Descartes’ error’. This paper puts forward that Descartes’ detractors fail to recognise Descartes’ contribution to Western thought, which was to introduce the Enlightenment and to give a place to human subjectivity. Added to this, evidence from Descartes’ correspondence with Princess Elisabeth of Bohemia supports the conclusion that Descartes did in fact believe in the unity of mind and body although he could not reconcile this rationally with the certainty from personal experience that they were separate substances. In this Descartes was engaged in just the same dilemma as that of current thinkers and researchers, a conflict which still is yet to be resolved.     

Finding MAPs using strongly equivalent high order recurrent symmetric connectionist networks

Belief revision is the problem of finding the most plausible explanation for an observed set of evidences. It has many applications in various scientific domains like natural language understanding, medical diagnosis and computational biology. Bayesian Networks (BN) is an important probabilistic graphical formalism widely used for belief revision tasks. In BN, belief revision can be achieved by finding the maximum a posteriori (MAP) assignment. Finding MAP is an NP-Hard problem. In previous work, we showed how to find the MAP assignment in BN using High Order Recurrent Neural Networks (HORN) through an intermediate representation of Cost-Based Abduction. This method eliminates the need to explicitly construct the energy function in two steps, objective and constraints. This paper builds on that previous work by providing the theoretical foundation and proving that the resultant HORN used to find MAP is strongly equivalent to the original BN it tries to solve.     

Multilevel Reliability Measures of Latent Scores Within an Item Response Theory Framework

Abstract

This paper evaluated multilevel reliability measures in two-level nested designs (e.g., students nested within teachers) within an item response theory framework. A simulation study was implemented to investigate the behavior of the multilevel reliability measures and the uncertainty associated with the measures in various multilevel designs regarding the number of clusters, cluster sizes, and intraclass correlations (ICCs), and in different test lengths, for two parameterizations of multilevel item response models with separate item discriminations or the same item discrimination over levels. Marginal maximum likelihood estimation (MMLE)-multiple imputation and Bayesian analysis were employed to evaluate the accuracy of the multilevel reliability measures and the empirical coverage rates of Monte Carlo (MC) confidence or credible intervals. Considering the accuracy of the multilevel reliability measures and the empirical coverage rate of the intervals, the results lead us to generally recommend MMLE-multiple imputation. In the model with separate item discriminations over levels, marginally acceptable accuracy of the multilevel reliability measures and empirical coverage rate of the MC confidence intervals were found in a limited condition, 200 clusters, 30 cluster size, .2 ICC, and 40 items, in MMLE-multiple imputation. In the model with the same item discrimination over levels, the accuracy of the multilevel reliability measures and the empirical coverage rate of the MC confidence intervals were acceptable in all multilevel designs we considered with 40 items under MMLE-multiple imputation. We discuss these findings and provide guidelines for reporting multilevel reliability measures.     

A rational analysis of the acquisition of multisensory representations

How do people learn multisensory, or amodal, representations, and what consequences do these representations have for perceptual performance? We address this question by performing a rational analysis of the problem of learning multisensory representations. This analysis makes use of a Bayesian nonparametric model that acquires latent multisensory features that optimally explain the unisensory features arising in individual sensory modalities. The model qualitatively accounts for several important aspects of multisensory perception: (a) it integrates information from multiple sensory sources in such a way that it leads to superior performances in, for example, categorization tasks; (b) its performances suggest that multisensory training leads to better learning than unisensory training, even when testing is conducted in unisensory conditions; (c) its multisensory representations are modality invariant; and (d) it predicts ‘‘missing” sensory representations in modalities when the input to those modalities is absent. Our rational analysis indicates that all of these aspects emerge as part of the optimal solution to the problem of learning to represent complex multisensory environments.     

Hierarchical models of simple mechanisms underlying confidence in decision making

Choice confidence is a central measure in psychological decision research, often being reported on a probabilistic scale. Simple mechanisms that describe the psychological processes underlying choice confidence, including those based on error and confirmation biases, have typically received support via fits to data averaged over subjects. While averaged data ease model development, they can also destroy important aspects of the confidence data distribution. In this paper, we develop a hierarchical model of raw confidence judgments using the beta distribution, and we implement two simple confidence mechanisms within it. We use Bayesian methods to fit the hierarchical model to data from a two-alternative confidence experiment, and we use a variety of Bayesian tools to diagnose shortcomings of the simple mechanisms that are overlooked when applied to averaged data. Bugs code for estimating the models is also supplied.     

The psychological neuroscience of depression: implications for understanding effects of deep brain stimulation

In this article it is suggested that current psychological theories of depression presuppose that this condition will develop as a result of a vicious circle involving negatively biased communication between systems of emotional stress-/alarm-signaling, executive functions and mood regulation. These systems may from a neuroanatomical point of view be located in the limbic system, the orbitofrontal and lateral prefrontal cortex and the hypothalamus-pituitary-adrenal (HPA-) axis respectively. The theoretical and practical implications of this model for the understanding of pharmacological treatments of depression are briefly discussed and this theory is related to the catecholamine hypothesis of depression. The model is furthermore discussed in relation to deep brain stimulation (DBS) of treatment resistant major depression. Similarities and differences between this perspective and the one advocated by the "homeostatic theory" of depression are discussed. It is concluded that a topographical psychological theory may offer a useful heuristic in thinking about depression and that it offers several testable predictions about treatments of the disorder.     

Modulation of N400 in chronic non-fluent aphasia using low frequency Repetitive Transcranial Magnetic Stimulation (rTMS)

Low frequency Repetitive Transcranial Magnetic Stimulation (rTMS) has previously been applied to language homologues in non-fluent populations of persons with aphasia yielding significant improvements in behavioral language function up to 43 months post stimulation. The present study aimed to investigate the electrophysiological correlates associated with the application of rTMS through measurement of the semantic based N400 Event-related brain potentials (ERP) component. Low frequency (1 Hz) rTMS was applied to the anterior portion of the homologue to Broca’s area (pars triangularis), for 20 min per day for 10 days, using a stereotactic neuronavigational system. Twelve non-fluent persons with aphasia, 2-6 years post stroke were stimulated. Six participants were randomly assigned to receive real stimulation and six participants were randomly assigned to receive a blind sham control condition. ERP measures were recorded at baseline, 1 week and 2 months subsequent to stimulation. The findings demonstrate treatment related changes observed in the stimulation group when compared to the placebo control group at 2 months post stimulation indicating neuromodulation of N400 as a result of rTMS. No treatment related changes were identified in the stimulation group, when compared to the sham group from baseline to 1 week post stimulation. The electrophysiological results represent the capacity of rTMS to modulate neural language networks and measures of lexical-semantic function in participants with non-fluent aphasia and suggest that time may be an important factor in brain reorganization subsequent to rTMS.