Diminished neural adaptation during implicit learning in autism

Neuroimaging studies have shown evidence of disrupted neural adaptation during learning in individuals with autism spectrum disorder (ASD) in several types of tasks, potentially stemming from frontal-posterior cortical underconnectivity (Schipul et al., 2012). The aim of the current study was to examine neural adaptations in an implicit learning task that entails participation of frontal and posterior regions. Sixteen high-functioning adults with ASD and sixteen neurotypical control participants were trained on and performed an implicit dot pattern prototype learning task in a functional magnetic resonance imaging (fMRI) session. During the preliminary exposure to the type of implicit prototype learning task later to be used in the scanner, the ASD participants took longer than the neurotypical group to learn the task, demonstrating altered implicit learning in ASD. After equating task structure learning, the two groups’ brain activation differed during their learning of a new prototype in the subsequent scanning session. The main findings indicated that neural adaptations in a distributed task network were reduced in the ASD group, relative to the neurotypical group, and were related to ASD symptom severity. Functional connectivity was reduced and did not change as much during learning for the ASD group, and was related to ASD symptom severity. These findings suggest that individuals with ASD show altered neural adaptations during learning, as seen in both activation and functional connectivity measures. This finding suggests why many real-world implicit learning situations may pose special challenges for ASD

The Curtate Cycloid Illusion: Cognitive Constraints on the Processing of Rolling Motion

When a wheel rolls along a flat surface, a point on the wheel's perimeter follows a cycloid trajectory. Subjects, however draw the curtate cycloid, characterized by bottom loops, rather than the cycloid to depict the path that a point on a static wheel's perimeter would trace if the wheel were rolling. This is the curtate cycloid illusion. In Experiment 1, we show that animating the wheel does not dispel the illusion and that subjects high in spatial ability are less susceptible to the illusion than are low-spatials. Experiments 2, 3a, and 3b supported the hypothesis that the illusion occurs when subjects reallocate cognitive resources from processing a rolling wheel's translation to computing its instant centers, the point about which the wheel is rotating at a given instant in time. This reallocation occurs only when a reference point on the wheel's perimeter contacts and leaves the surface. We conclude that the illusion does not reflect fundamental perceptual biases, but rather stems from transient shortages of cognitive resources during the higher-level processing of the wheel's translation and rotation

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

Two-Handed Gesture Recognition

Nowadays, computer interaction is mostly done using dedicated devices. But gestures are an easy mean of expression between humans that could be used to communicate with computers in a more natural manner. Most of the current research on hand gesture recognition for Human-Computer Interaction deals with one-handed gestures. But two-handed gestures can provide more efficient and easy to interact with user interfaces. It is particularly the case with two-handed gestures we do in the physical world, such as gestures to manipulate objects. It would be very valuable to permit to the user to interact with virtual objects in the same way that he/she interacts with physical ones. This paper presents a two-handed gesture database to manipulate virtual objects on the screen (mostly rotations) and some recognition experiment using Hidden Markov Models (HMMs). The results obtained with this state-of-the-art algorithm are really encouraging. These gestures would improve the interaction performance between the user and virtual reality applications

A Neurosemantic Theory of Concrete Noun Representation Based on the Underlying Brain Codes

This article describes the discovery of a set of biologically-driven semantic dimensions underlying the neural representation of concrete nouns, and then demonstrates how a resulting theory of noun representation can be used to identify simple thoughts through their fMRI patterns. We use factor analysis of fMRI brain imaging data to reveal the biological representation of individual concrete nouns like apple, in the absence of any pictorial stimuli. From this analysis emerge three main semantic factors underpinning the neural representation of nouns naming physical objects, which we label manipulation, shelter, and eating. Each factor is neurally represented in 3–4 different brain locations that correspond to a cortical network that co-activates in non-linguistic tasks, such as tool use pantomime for the manipulation factor. Several converging methods, such as the use of behavioral ratings of word meaning and text corpus characteristics, provide independent evidence of the centrality of these factors to the representations. The factors are then used with machine learning classifier techniques to show that the fMRI-measured brain representation of an individual concrete noun like apple can be identified with good accuracy from among 60 candidate words, using only the fMRI activity in the 16 locations associated with these factors. To further demonstrate the generativity of the proposed account, a theory-based model is developed to predict the brain activation patterns for words to which the algorithm has not been previously exposed. The methods, findings, and theory constitute a new approach of using brain activity for understanding how object concepts are represented in the mind

Hand Posture Classification and Recognition using the Modified Census Transform

Developing new techniques for human-computer interaction is very challenging. Vision-based techniques have the advantage of being unobtrusive and hands are a natural device that can be used for more intuitive interfaces. But in order to use hands for interaction, it is necessary to be able to recognize them in images. In this paper, we propose to apply to the hand posture classification and recognition tasks an approach that has been successfully used for face detection~\cite{Froba04}. The features are based on the Modified Census Transform and are illumination invariant. For the classification and recognition processes, a simple linear classifier is trained, using a set of feature lookup-tables. The database used for the experiments is a benchmark database in the field of posture recognition. Two protocols have been defined. We provide results following these two protocols for both the classification and recognition tasks. Results are very encouraging

Recognition of Isolated Complex Mono- and Bi-Manual 3D Hand Gestures

In this paper, we address the problem of the recognition of isolated complex mono- and bi-manual hand gestures. In the proposed system, hand gestures are represented by the 3D trajectories of blobs. Blobs are obtained by tracking colored body parts in real-time using the EM algorithm. In most of the studies on hand gestures, only small vocabularies have been used. In this paper, we study the results obtained on a more complex database of mono- and bi-manual gestures. These results are obtained by using a state-of-the-art sequence processing algorithm, namely Hidden Markov Models (HMMs), implemented within the framework of an open source machine learning library

Production and characterization of monoclonal antibodies raised against recombinant human granzymes A and B and showing cross reactions with the natural proteins

The human serine proteases granzymes A and B are expressed in cytotoplasmic granules of activated cytotoxic T lymphocytes and natural killer cells. Recombinant granzyme A and granzyme B proteins were produced in bacteria, purified and then used to raise specific mouse monoclonal antibodies. Seven monoclonal antibodies (mAb) were raised against granzyme A, which all recognized the same or overlapping epitopes. They reacted specifically in an immunoblot of interleukin-2 (IL-2) stimulated PBMNC with a disulfide-linked homodimer of 43 kDa consisting of 28 kDa subunits. Seven mAb against granzyme B were obtained, which could be divided into two groups, each recognizing a different epitope. On an immunoblot, all mAb reacted with a monomer of 33 kDa protein. By immunohistochemistry, these mAb could be used to detect granzymes A and B expression in activated CTL and NK cells. The availability of these mAb may facilitate studies on the role of human cytotoxic cells in various immune reactions and may contribute to a better understanding of the role of granzmes A and B in the cytotoxic response in vivo

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