Inter-Regional Brain Communication and Its Disturbance in Autism

In this review article, we summarize recent progress toward understanding disturbances in functional and anatomical brain connectivity in autism. Autism is a neurodevelopmental disorder affecting language, social interaction, and repetitive behaviors. Recent studies have suggested that limitations of frontal–posterior brain connectivity in autism underlie the varied set of deficits associated with this disorder. Specifically, the underconnectivity theory of autism postulates that individuals with autism have a reduced communication bandwidth between frontal and posterior cortical areas, which constrains the psychological processes that rely on the integrated functioning of frontal and posterior brain networks. This review summarizes the recent findings of reduced frontal–posterior functional connectivity (synchronization) in autism in a wide variety of high-level tasks, focusing on data from functional magnetic resonance imaging studies. It also summarizes the findings of disordered anatomical connectivity in autism, as measured by a variety of techniques, including distribution of white matter volumes and diffusion tensor imaging. We conclude with a discussion of the implications of these findings for autism and future directions for this line of research

Production System Models of Complex Cognition

There have been a number of production system models which have recently made substantial advances in modeling higher-level cognition. These type of model offers only comprehensive approaches to the modeling of higher level cognition. This symposium will involve presentations by four exemplars of this approach to cognitive modeling (ACT, CAPS, EPIC, and SOAR). The presentations will try to illustrate the range of applications to which such models are appropriate, what the similarities and differences are among the various architectures, and what some of the interesting research questions are within each architecture

Brain activation modulated by sentence comprehension.

The comprehension of visually presented sentences produces brain activation that increases with the linguistic complexity of the sentence. The volume of neural tissue activated (number of voxels) during sentence comprehension was measured with echoplanar functional magnetic resonance imaging. The modulation of the volume of activation by sentence complexity was observed in a network of four areas: the classical left-hemisphere language areas (the left laterosuperior temporal cortex, or Wernicke's area, and the left inferior frontal gyrus, or Broca's area) and their homologous righthemisphere areas, although the right areas had much smaller volumes of activation than did the left areas. These findings generally indicate that the amount of neural activity that a given cognitive process engenders is dependent on the computational demand that the task imposes. This study examines what it means to be "thinking harder" in the course of sentence comprehension, in terms of functional magnetic resonance imaging (fMRI)-measured brain activation. One of the challenges of brain science is to relate the dynamics of higher level cognition to the equally dynamic activity of brain-level events. A possible meeting ground between these two levels is the modLulation in the amount of neuronal activity (at the brain level) in a given task, measured as a function of the amount of computational demand that the task places on cognitive resources (1). In particular, we examined whether sentences that were more computationally demanding also engender more brain activation (2, 3). At the cognitive level, sentence comprehension requires combining information from a sequence of words and phrases, computing their syntactic and thematic relations, and using world knowledge to construct a representation of the sentence meaning. These processes require the consumption of computational resources to perform the comprehension operations and also to maintain the representations of the component word meanings, propositions, and relational structures in an activated state during the processing (1). At the brain level, sentence comprehension entails activation in a network of cortical areas, most prominent of which are the left laterosuperior temporal cortex (Wernicke's area) (4) and the left inferio

Using fMRI Brain Activation to Identify Cognitive States Associated with Perception of Tools and Dwellings

Previous studies have succeeded in identifying the cognitive state corresponding to the perception of a set of depicted categories, such as tools, by analyzing the accompanying pattern of brain activity, measured with fMRI. The current research focused on identifying the cognitive state associated with a 4s viewing of an individual line drawing (1 of 10 familiar objects, 5 tools and 5 dwellings, such as a hammer or a castle). Here we demonstrate the ability to reliably (1) identify which of the 10 drawings a participant was viewing, based on that participant's characteristic whole-brain neural activation patterns, excluding visual areas; (2) identify the category of the object with even higher accuracy, based on that participant's activation; and (3) identify, for the first time, both individual objects and the category of the object the participant was viewing, based only on other participants' activation patterns. The voxels important for category identification were located similarly across participants, and distributed throughout the cortex, focused in ventral temporal perceptual areas but also including more frontal association areas (and somewhat left-lateralized). These findings indicate the presence of stable, distributed, communal, and identifiable neural states corresponding to object concepts

Decoding Brain Activity Associated with Literal and Metaphoric Sentence Comprehension Using Distributional Semantic Models

Recent years have seen a growing interest within the natural language processing (NLP)community in evaluating the ability of semantic models to capture human meaning representation in the brain. Existing research has mainly focused on applying semantic models to de-code brain activity patterns associated with the meaning of individual words, and, more recently, this approach has been extended to sentences and larger text fragments. Our work is the first to investigate metaphor process-ing in the brain in this context. We evaluate a range of semantic models (word embeddings, compositional, and visual models) in their ability to decode brain activity associated with reading of both literal and metaphoric sentences. Our results suggest that compositional models and word embeddings are able to capture differences in the processing of literal and metaphoric sentences, providing sup-port for the idea that the literal meaning is not fully accessible during familiar metaphor comprehension

Reflexive Memory Authenticator: A Proposal for Effortless Renewable Biometrics

International audienceToday’s biometric authentication systems are still struggling with replay attacks and irrevocable stolen credentials. This paper introduces a biometric protocol that addresses such vulnerabilities. The approach prevents identity theft by being based on memory creation biometrics. It takes inspiration from two different authentication methods, eye biometrics and challenge systems, as well as a novel biometric feature: the pupil memory effect. The approach can be adjusted for arbitrary levels of security, and credentials can be revoked at any point with no loss to the user. The paper includes an analysis of its security and performance, and shows how it could be deployed and improved