Children with Autism do not Show Sequence Effects with Auditory Stimuli

Categorization decisions that reflect constantly changing memory representations may be an important adaptive response to dynamic environments. We assessed one such influence from memory, sequence effects, on categorization decisions made by individuals with autism. A model of categorization (i.e. Memory and Contrast model, Stewart, Brown, & Chater, 2002) assumes that contextual influences in the form of sequence effects drive categorization performance in individuals with typical development. Difficulties with contextual processing in autism, described by the weak central coherence account (Frith, 1989; Frith & Happé, 1994) imply reduced sequence effects for this participant group. The experiment reported here tested this implication. High functioning children and adolescents with autism (aged 10 to 15 years), matched on age and IQ with typically developing children, completed a test that measures sequence effects (i.e. category contrast effect task, Stewart et al., 2002) using auditory tones. Participants also completed a pitch discrimination task to measure any potential confound arising from possible enhanced discrimination sensitivity within the ASD group. The typically developing group alone demonstrated a category contrast effect. The data suggest that this finding cannot be attributed readily to participant group differences in discrimination sensitivity, perseveration, difficulties on the associated binary categorization task, or greater reliance upon long term memory. We discuss the broad methodological implication that comparison between autism and control group responses to sequential perceptual stimuli may be confounded by the influence of preceding trials. We also discuss implications for the weak central coherence account and models of typical cognition

Enterprise IoT Security and Scalability : How Unikernels can Improve the Status Quo

Postprin

The diagnosis of mental disorders: the problem of reification

A pressing need for interrater reliability in the diagnosis of mental disorders emerged during the mid-twentieth century, prompted in part by the development of diverse new treatments. The Diagnostic and Statistical Manual of Mental Disorders (DSM), third edition answered this need by introducing operationalized diagnostic criteria that were field-tested for interrater reliability. Unfortunately, the focus on reliability came at a time when the scientific understanding of mental disorders was embryonic and could not yield valid disease definitions. Based on accreting problems with the current DSM-fourth edition (DSM-IV) classification, it is apparent that validity will not be achieved simply by refining criteria for existing disorders or by the addition of new disorders. Yet DSM-IV diagnostic criteria dominate thinking about mental disorders in clinical practice, research, treatment development, and law. As a result, the modernDSMsystem, intended to create a shared language, also creates epistemic blinders that impede progress toward valid diagnoses. Insights that are beginning to emerge from psychology, neuroscience, and genetics suggest possible strategies for moving forward

Understanding the Mind or Predicting Signal-Dependent Action? Performance of Children With and Without Autism on Analogues of the False-Belief Task

To evaluate the claim that correct performance on unexpected transfer false-belief tasks specifically involves mental-state understanding, two experiments were carried out with children with autism, intellectual disabilities, and typical development. In both experiments, children were given a standard unexpected transfer false-belief task and a mental-state-free, mechanical analogue task in which participants had to predict the destination of a train based on true or false signal information. In both experiments, performance on the mechanical task was found to correlate with that on the false-belief task for all groups of children. Logistic regression showed that performance on the mechanical analogue significantly predicted performance on the false-belief task even after accounting for the effects of verbal mental age. The findings are discussed in relation to possible common mechanisms underlying correct performance on the two tasks

Simple mindreading abilities predict complex theory of mind: developmental delay in autism spectrum disorders

Theory of Mind (ToM) is impaired in individuals with Autism Spectrum Disorders (ASD). The aims of this study were to: i) examine the developmental trajectories of ToM abilities in two different mentalizing tasks in children with ASD compared to TD children; and ii) to assess if a ToM simple test known as Eyes-test could predict performance on the more advanced ToM task, i.e. Comic Strip test. Based on a sample of 37 children with ASD and 55 TD children, our results revealed slower development at varying rates in all ToM measures in children with ASD, with delayed onset compared to TD children. These results could stimulate new treatments for social abilities, which would lessen the social deficit in ASD

Spatial interactions in agent-based modeling

Agent Based Modeling (ABM) has become a widespread approach to model complex interactions. In this chapter after briefly summarizing some features of ABM the different approaches in modeling spatial interactions are discussed. It is stressed that agents can interact either indirectly through a shared environment and/or directly with each other. In such an approach, higher-order variables such as commodity prices, population dynamics or even institutions, are not exogenously specified but instead are seen as the results of interactions. It is highlighted in the chapter that the understanding of patterns emerging from such spatial interaction between agents is a key problem as much as their description through analytical or simulation means. The chapter reviews different approaches for modeling agents' behavior, taking into account either explicit spatial (lattice based) structures or networks. Some emphasis is placed on recent ABM as applied to the description of the dynamics of the geographical distribution of economic activities, - out of equilibrium. The Eurace@Unibi Model, an agent-based macroeconomic model with spatial structure, is used to illustrate the potential of such an approach for spatial policy analysis.Comment: 26 pages, 5 figures, 105 references; a chapter prepared for the book "Complexity and Geographical Economics - Topics and Tools", P. Commendatore, S.S. Kayam and I. Kubin, Eds. (Springer, in press, 2014

Data-efficient performance learning for configurable systems

Many software systems today are configurable, offering customization of functionality by feature selection. Understanding how performance varies in terms of feature selection is key for selecting appropriate configurations that meet a set of given requirements. Due to a huge configuration space and the possibly high cost of performance measurement, it is usually not feasible to explore the entire configuration space of a configurable system exhaustively. It is thus a major challenge to accurately predict performance based on a small sample of measured system variants. To address this challenge, we propose a data-efficient learning approach, called DECART, that combines several techniques of machine learning and statistics for performance prediction of configurable systems. DECART builds, validates, and determines a prediction model based on an available sample of measured system variants. Empirical results on 10 real-world configurable systems demonstrate the effectiveness and practicality of DECART. In particular, DECART achieves a prediction accuracy of 90% or higher based on a small sample, whose size is linear in the number of features. In addition, we propose a sample quality metric and introduce a quantitative analysis of the quality of a sample for performance prediction