35,571 research outputs found

    A psychometric measure of working memory capacity for configured body movement.

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    Working memory (WM) models have traditionally assumed at least two domain-specific storage systems for verbal and visuo-spatial information. We review data that suggest the existence of an additional slave system devoted to the temporary storage of body movements, and present a novel instrument for its assessment: the movement span task. The movement span task assesses individuals' ability to remember and reproduce meaningless configurations of the body. During the encoding phase of a trial, participants watch short videos of meaningless movements presented in sets varying in size from one to five items. Immediately after encoding, they are prompted to reenact as many items as possible. The movement span task was administered to 90 participants along with standard tests of verbal WM, visuo-spatial WM, and a gesture classification test in which participants judged whether a speaker's gestures were congruent or incongruent with his accompanying speech. Performance on the gesture classification task was not related to standard measures of verbal or visuo-spatial working memory capacity, but was predicted by scores on the movement span task. Results suggest the movement span task can serve as an assessment of individual differences in WM capacity for body-centric information

    Talking Nets: A Multi-Agent Connectionist Approach to Communication and Trust between Individuals

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    A multi-agent connectionist model is proposed that consists of a collection of individual recurrent networks that communicate with each other, and as such is a network of networks. The individual recurrent networks simulate the process of information uptake, integration and memorization within individual agents, while the communication of beliefs and opinions between agents is propagated along connections between the individual networks. A crucial aspect in belief updating based on information from other agents is the trust in the information provided. In the model, trust is determined by the consistency with the receiving agents’ existing beliefs, and results in changes of the connections between individual networks, called trust weights. Thus activation spreading and weight change between individual networks is analogous to standard connectionist processes, although trust weights take a specific function. Specifically, they lead to a selective propagation and thus filtering out of less reliable information, and they implement Grice’s (1975) maxims of quality and quantity in communication. The unique contribution of communicative mechanisms beyond intra-personal processing of individual networks was explored in simulations of key phenomena involving persuasive communication and polarization, lexical acquisition, spreading of stereotypes and rumors, and a lack of sharing unique information in group decisions

    Replicability or reproducibility? On the replication crisis in computational neuroscience and sharing only relevant detail

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    Replicability and reproducibility of computational models has been somewhat understudied by “the replication movement.” In this paper, we draw on methodological studies into the replicability of psychological experiments and on the mechanistic account of explanation to analyze the functions of model replications and model reproductions in computational neuroscience. We contend that model replicability, or independent researchers' ability to obtain the same output using original code and data, and model reproducibility, or independent researchers' ability to recreate a model without original code, serve different functions and fail for different reasons. This means that measures designed to improve model replicability may not enhance (and, in some cases, may actually damage) model reproducibility. We claim that although both are undesirable, low model reproducibility poses more of a threat to long-term scientific progress than low model replicability. In our opinion, low model reproducibility stems mostly from authors' omitting to provide crucial information in scientific papers and we stress that sharing all computer code and data is not a solution. Reports of computational studies should remain selective and include all and only relevant bits of code

    High-performance Kernel Machines with Implicit Distributed Optimization and Randomization

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    In order to fully utilize "big data", it is often required to use "big models". Such models tend to grow with the complexity and size of the training data, and do not make strong parametric assumptions upfront on the nature of the underlying statistical dependencies. Kernel methods fit this need well, as they constitute a versatile and principled statistical methodology for solving a wide range of non-parametric modelling problems. However, their high computational costs (in storage and time) pose a significant barrier to their widespread adoption in big data applications. We propose an algorithmic framework and high-performance implementation for massive-scale training of kernel-based statistical models, based on combining two key technical ingredients: (i) distributed general purpose convex optimization, and (ii) the use of randomization to improve the scalability of kernel methods. Our approach is based on a block-splitting variant of the Alternating Directions Method of Multipliers, carefully reconfigured to handle very large random feature matrices, while exploiting hybrid parallelism typically found in modern clusters of multicore machines. Our implementation supports a variety of statistical learning tasks by enabling several loss functions, regularization schemes, kernels, and layers of randomized approximations for both dense and sparse datasets, in a highly extensible framework. We evaluate the ability of our framework to learn models on data from applications, and provide a comparison against existing sequential and parallel libraries.Comment: Work presented at MMDS 2014 (June 2014) and JSM 201

    Non-intrusive on-the-fly data race detection using execution replay

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    This paper presents a practical solution for detecting data races in parallel programs. The solution consists of a combination of execution replay (RecPlay) with automatic on-the-fly data race detection. This combination enables us to perform the data race detection on an unaltered execution (almost no probe effect). Furthermore, the usage of multilevel bitmaps and snooped matrix clocks limits the amount of memory used. As the record phase of RecPlay is highly efficient, there is no need to switch it off, hereby eliminating the possibility of Heisenbugs because tracing can be left on all the time.Comment: In M. Ducasse (ed), proceedings of the Fourth International Workshop on Automated Debugging (AAdebug 2000), August 2000, Munich. cs.SE/001003

    Learning Output Kernels for Multi-Task Problems

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    Simultaneously solving multiple related learning tasks is beneficial under a variety of circumstances, but the prior knowledge necessary to correctly model task relationships is rarely available in practice. In this paper, we develop a novel kernel-based multi-task learning technique that automatically reveals structural inter-task relationships. Building over the framework of output kernel learning (OKL), we introduce a method that jointly learns multiple functions and a low-rank multi-task kernel by solving a non-convex regularization problem. Optimization is carried out via a block coordinate descent strategy, where each subproblem is solved using suitable conjugate gradient (CG) type iterative methods for linear operator equations. The effectiveness of the proposed approach is demonstrated on pharmacological and collaborative filtering data
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