The selective attention for identification model (SAIM) is an established model of selective visual attention. SAIM implements translation-invariant object recognition, in scenes with multiple objects, using the paral...
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The selective attention for identification model (SAIM) is an established model of selective visual attention. SAIM implements translation-invariant object recognition, in scenes with multiple objects, using the parallel distributed processing (PDP) paradigm. Here, we show that SAIM can be formulated as Bayesian inference. Crucially, SAIM uses excitatory feedback to combine top-down information (i.e. object knowledge) with bottom-up sensory information. By contrast, predictive coding implementations of Bayesian inference use inhibitory feedback. By formulating SAIM as a predictive coding scheme, we created a new version of SAIM that uses inhibitory feedback. Simulation studies showed that both types of architectures can reproduce the response time costs induced by multiple objects-as found in visual search experiments. However, due to the different nature of the feedback, the two SAIM schemes make distinct predictions about the motifs of microcircuits mediating the effects of top-down afferents. We discuss empirical (neuroimaging) methods to test the predictions of the two inference architectures.
This paper describes a method of extracting the relation between the ground-motion characteristics of each area and a seismic source model, based on ground-motion simulation data output in planar form for many earthqu...
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This paper describes a method of extracting the relation between the ground-motion characteristics of each area and a seismic source model, based on ground-motion simulation data output in planar form for many earthquake scenarios, and the construction of a parallel distributed processing system where this method is implemented. The extraction is realized using two-stage clustering. In the first stage, the ground-motion indices and scenario parameters are used as input data to cluster the earthquake scenarios within each evaluation mesh. In the second stage, the meshes are clustered based on the similarity of earthquake-scenario clustering. Because the mesh clusters can be correlated to the geographical space, it is possible to extract the relation between the ground-motion characteristics of each area and the scenario parameters by examining the relation between the mesh clusters and scenario clusters obtained by the two-stage clustering. The results are displayed visually;they are saved as GeoTIFF image files. The system was applied to the long-period ground-motion simulation data for hypothetical megathrust earthquakes in the Nankai Trough. This confirmed that the relation between the extracted ground-motion characteristics of each area and scenario parameters is in agreement with the results of ground-motion simulations.
Analyzing datasets for Risky Decision Making (RDM) is a challenging task involving the identification of varied decision making patterns and the categorization of individuals. Researchers from various fields as divers...
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ISBN:
(纸本)9781509020881
Analyzing datasets for Risky Decision Making (RDM) is a challenging task involving the identification of varied decision making patterns and the categorization of individuals. Researchers from various fields as diverse as psychology and marketing are actively working to identify suitable techniques, which will allow understanding decision making processes better. Researchers have commonly used machine learning algorithms to model decision making processes. However, the high computational costs of most machine learning algorithms make such endeavors challenging for increasingly large datasets. One of the most promising approaches is to use ensemble clustering for RDM analysis. Ensemble clustering is computationally intensive and thus we propose to improve its performance. Our study reveals that computational overhead is introduced through the use of dimensions in ensemble cluster RDM analyses. Improving performance requires more than the parallelization of individual clustering techniques of the ensemble. We therefore propose a FIFO queue based implementation for analyzing RDM datasets using a HPC cluster on a distributed system. Our technique is able to achieve almost a linear speedup (e.g. 44.79x using 48 MPI threads). Possible shortcomings of the proposed method, opportunities for future work, and alternative parallelization scenarios are also discussed in this paper.
Models of reading must explain how orthographic input activates a phonological representation, and elicits the retrieval of word meaning from semantic memory. Comparisons between tasks that theoretically differ with r...
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Models of reading must explain how orthographic input activates a phonological representation, and elicits the retrieval of word meaning from semantic memory. Comparisons between tasks that theoretically differ with respect to the degree to which they rely on connections between orthographic, phonological and semantic systems during reading can thus provide valuable insight into models of reading, but such direct comparisons are not well-represented in the literature. An ALE meta-analysis explored lexicality effects directly contrasting words and pseudowords using the lexical decision task and overt or covert naming, which we assume rely most on the semantic and phonological systems, respectively. Interactions between task and lexicality effects demonstrate that different demands of the lexical decision and naming tasks lead to different manifestations of lexicality effects. (c) 2014 Elsevier Ltd. All rights reserved.
The parallel distributed processing (PDP) framework is built on neural-style computation, and is thus well-suited for simulating the neural implementation of cognition. However, relatively little cognitive modeling wo...
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The parallel distributed processing (PDP) framework is built on neural-style computation, and is thus well-suited for simulating the neural implementation of cognition. However, relatively little cognitive modeling work has concerned neural measures, instead focusing on behavior. Here, we extend a PDP model of reading-related components in the Event-Related Potential (ERP) to simulation of the N400 repetition effect. We accomplish this by incorporating the dynamics of cortical post-synaptic potentials the source of the ERP signal into the model. Simulations demonstrate that application of these dynamics is critical for model elicitation of repetition effects in the time and frequency domains. We conclude that by advancing a neurocomputational understanding of repetition effects, we are able to posit an interpretation of their source that is both explicitly specified and mechanistically different from the well-accepted cognitive one. (C) 2014 Elsevier Inc. All rights reserved.
Taking a process-oriented approach, the present paper employs two of the principal theories of the semantics of proper names, the description theory and the causal theory, in order to arrive at 'identifying functi...
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Taking a process-oriented approach, the present paper employs two of the principal theories of the semantics of proper names, the description theory and the causal theory, in order to arrive at 'identifying function', the element responsible for retaining the effects of the original proper names into the target text. A continuum with two extremes of 'identifying-function-reduced' and 'identifying-function-embedded' translations is then proposed for the translation of proper names. The paper also attempts to show that, as representatives of individuals with different identifying functions, all the possible coexistent translations on this continuum are coactivated and compete for their realisation in the final actual product of translation. This coexistence, coactivation and competition will lead us to the concept of 'Translation Metamorphosis'.
My discussion focuses on the excellence and complexity of Helen Grebow’s presentation on the therapist’s experience of the uncanny, unsettling, even surreal nature of the verbal and non-verbal resonance that emerges...
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A parallel distributed processing (PDP) or Neural Network model is proposed to re-organize industries so that they can share knowledge and experience among them. This re-organized industry framework, which is called P...
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ISBN:
(纸本)9781614993025;9781614993018
A parallel distributed processing (PDP) or Neural Network model is proposed to re-organize industries so that they can share knowledge and experience among them. This re-organized industry framework, which is called paralleldistributed Engineering here, develops interchangeable components at its intermediate level and combines them into final products to meet the requirements of customers. Thus, it brings forth greater flexibility to adapt to very frequently and extensively changing situations and what is more important, it reduces time, cost and energy consumption and increase productivity considerably. And it will satisfy customers more because their diverse requirements can be more precisely met.
In a seminal 1977 article, Rumelhart argued that perception required the simultaneous use of multiple sources of information, allowing perceivers to optimally interpret sensory information at many levels of representa...
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In a seminal 1977 article, Rumelhart argued that perception required the simultaneous use of multiple sources of information, allowing perceivers to optimally interpret sensory information at many levels of representation in real time as information arrives. Building on Rumelhart's arguments, we present the Interactive Activation hypothesis-the idea that the mechanism used in perception and comprehension to achieve these feats exploits an interactive activation process implemented through the bidirectional propagation of activation among simple processing units. We then examine the interactive activation model of letter and word perception and the TRACE model of speech perception, as early attempts to explore this hypothesis, and review the experimental evidence relevant to their assumptions and predictions. We consider how well these models address the computational challenge posed by the problem of perception, and we consider how consistent they are with evidence from behavioral experiments. We examine empirical and theoretical controversies surrounding the idea of interactive processing, including a controversy that swirls around the relationship between interactive computation and optimal Bayesian inference. Some of the implementation details of early versions of interactive activation models caused deviation from optimality and from aspects of human performance data. More recent versions of these models, however, overcome these deficiencies. Among these is a model called the multinomial interactive activation model, which explicitly links interactive activation and Bayesian computations. We also review evidence from neurophysiological and neuroimaging studies supporting the view that interactive processing is a characteristic of the perceptual processing machinery in the brain. In sum, we argue that a computational analysis, as well as behavioral and neuroscience evidence, all support the Interactive Activation hypothesis. The evidence suggests that contemporar
Individuals with autism spectrum disorder (ASD) show atypical patterns of learning and generalization. We explored the possible impacts of autism-related neural abnormalities on perceptual category learning using a ne...
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Individuals with autism spectrum disorder (ASD) show atypical patterns of learning and generalization. We explored the possible impacts of autism-related neural abnormalities on perceptual category learning using a neural network model of visual cortical processing. When applied to experiments in which children or adults were trained to classify complex two-dimensional images, the model can account for atypical patterns of perceptual generalization. This is only possible, however, when individual differences in learning are taken into account. In particular, analyses performed with a self-organizing map suggested that individuals with high-functioning ASD show two distinct generalization patterns: one that is comparable to typical patterns, and a second in which there is almost no generalization. The model leads to novel predictions about how individuals will generalize when trained with simplified input sets and can explain why some researchers have failed to detect learning or generalization deficits in prior studies of category learning by individuals with autism. On the basis of these simulations, we propose that deficits in basic neural plasticity mechanisms may be sufficient to account for the atypical patterns of perceptual category learning and generalization associated with autism, but they do not account for why only a subset of individuals with autism would show such deficits. If variations in performance across subgroups reflect heterogeneous neural abnormalities, then future behavioral and neuroimaging studies of individuals with ASD will need to account for such disparities.
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