60,882 research outputs found

    From extinction learning to anxiety treatment: mind the gap

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    Laboratory models of extinction learning in animals and humans have the potential to illuminate methods for improving clinical treatment of fear-based clinical disorders. However, such translational research often neglects important differences between threat responses in animals and fear learning in humans, particularly as it relates to the treatment of clinical disorders. Specifically, the conscious experience of fear and anxiety, along with the capacity to deliberately engage top-down cognitive processes to modulate that experience, involves distinct brain circuitry and is measured and manipulated using different methods than typically used in laboratory research. This paper will identify how translational research that investigates methods of enhancing extinction learning can more effectively model such elements of human fear learning, and how doing so will enhance the relevance of this research to the treatment of fear-based psychological disorders.Published versio

    Evidence for multiple processes contributing to the Perruchet effect: Response priming and associative learning.

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    The Perruchet effect constitutes a robust demonstration that it is possible to dissociate conditioned responding and expectancy in a random partial reinforcement design across a variety of human associative learning paradigms. This dissociation has been interpreted as providing evidence for multiple processes supporting learning, with expectancy driven by cognitive processes that lead to a Gambler's fallacy, and the pattern of conditioned responding (CRs) the result of an associative learning process. An alternative explanation is that the pattern of CRs is the result of exposure to the unconditioned stimulus (US). In three human eyeblink conditioning experiments we examined these competing explanations of the Perruchet effect by employing a differential conditioning design and varying the degree to which the two conditioned stimuli (CS) were discriminable. Across all of these experiments there was evidence for a component of the CRs being strongly influenced by recent reinforcement, in a way that was not demonstrably influenced by manipulations of CS discriminability, which suggests a response priming mechanism contributes to the Perruchet effect. However, the complete pattern of results and an analysis of the results from previously published studies are also consistent with there being an associative contribution to the effect.This research was supported by grant DP1096437 from the Australian Research Council to G. Weidemann, an ESRC Doctoral Training Grant to A. McAndrew and I. P.L. McLaren, and an EPS Study visit grant awarded to A. McAndrew

    Pervasive Parallel And Distributed Computing In A Liberal Arts College Curriculum

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    We present a model for incorporating parallel and distributed computing (PDC) throughout an undergraduate CS curriculum. Our curriculum is designed to introduce students early to parallel and distributed computing topics and to expose students to these topics repeatedly in the context of a wide variety of CS courses. The key to our approach is the development of a required intermediate-level course that serves as a introduction to computer systems and parallel computing. It serves as a requirement for every CS major and minor and is a prerequisite to upper-level courses that expand on parallel and distributed computing topics in different contexts. With the addition of this new course, we are able to easily make room in upper-level courses to add and expand parallel and distributed computing topics. The goal of our curricular design is to ensure that every graduating CS major has exposure to parallel and distributed computing, with both a breadth and depth of coverage. Our curriculum is particularly designed for the constraints of a small liberal arts college, however, much of its ideas and its design are applicable to any undergraduate CS curriculum

    Testing Schenkerian theory: an experiment on the perception of key distances

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    The lack of attention given to Schenkerian theory by empirical research in music is striking when compared to its status in music theory as a standard account of tonality. In this paper I advocate a different way of thinking of Schenkerian theory that can lead to empirically testable claims, and report on an experiment that shows how hypotheses derived from Schenker’s theories explain features of listener’s perception of key relationships. To be relevant to empirical research, Schenker’s theory must be treated as a collection of interrelated but independent theoretical claims rather than a comprehensive analytical method. These discrete theoretical claims can then lead to hypotheses that we can test through empirical methods. This makes it possible for Schenkerian theory improve our scientific understanding of how listeners understand tonal music. At the same time, it opens the possibility of challenging the usefulness of certain aspects of the theory. This paper exemplifies the empirical project with an experiment on the perception of key distance. The results show that two features of Schenkerian theory predict how listeners rate stimuli in terms of key distance. The first is the Schenkerian principle of “composing out” a harmony, and the second is the theory of “voice-leading prolongations.” In a regression analysis, both of these principles significantly improve upon a model of distance ratings based on change of scalar collection alone.Accepted manuscrip

    Common and Distinct Functional Brain Networks for Intuitive and Deliberate Decision Making

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    Reinforcement learning studies in rodents and primates demonstrate that goal-directed and habitual choice behaviors are mediated through different fronto-striatal systems, but the evidence is less clear in humans. In this study, functional magnetic resonance imaging (fMRI) data were collected whilst participants ( n = 20) performed a conditional associative learning task in which blocks of novel conditional stimuli (CS) required a deliberate choice, and blocks of familiar CS required an intuitive choice. Using standard subtraction analysis for fMRI event-related designs, activation shifted from the dorso-fronto-parietal network, which involves dorsolateral prefrontal cortex (DLPFC) for deliberate choice of novel CS, to ventro-medial frontal (VMPFC) and anterior cingulate cortex for intuitive choice of familiar CS. Supporting this finding, psycho-physiological interaction (PPI) analysis, using the peak active areas within the PFC for novel and familiar CS as seed regions, showed functional coupling between caudate and DLPFC when processing novel CS and VMPFC when processing familiar CS. These findings demonstrate separable systems for deliberate and intuitive processing, which is in keeping with rodent and primate reinforcement learning studies, although in humans they operate in a dynamic, possibly synergistic, manner particularly at the level of the striatum.Peer reviewedFinal Published versio

    Analytical methods and experimental approaches for electrophysiological studies of brain oscillations

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    Brain oscillations are increasingly the subject of electrophysiological studies probing their role in the functioning and dysfunction of the human brain. In recent years this research area has seen rapid and significant changes in the experimental approaches and analysis methods. This article reviews these developments and provides a structured overview of experimental approaches, spectral analysis techniques and methods to establish relationships between brain oscillations and behaviour

    Power consumption evaluation of circuit-switched versus packet-switched optical backbone networks

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    While telecommunication networks have historically been dominated by a circuit-switched paradigm, the last decades have seen a clear trend towards packet-switched networks. In this paper we evaluate how both paradigms perform in optical backbone networks from a power consumption point of view, and whether the general agreement of circuit switching being more power-efficient holds. We consider artificially generated topologies of various sizes, mesh degrees and not yet previously explored in this context transport linerates. We cross-validate our findings with a number of realistic topologies. Our results show that, as a generalization, packet switching can become preferable when the traffic demands are lower than half the transport linerate. We find that an increase in the network node count does not consistently increase the energy savings of circuit switching over packet switching, but is heavily influenced by the mesh degree and (to a minor extent) by the average link length
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