The notion of the brain as a predictive organ following Bayesian principles has been steadily gaining favor in neuroscience. This perspective, which has broad theoretical and applicative consequences, suggests also a ...
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The notion of the brain as a predictive organ following Bayesian principles has been steadily gaining favor in neuroscience. This perspective, which has broad theoretical and applicative consequences, suggests also a novel way to look at the mind-body processes mobilized by meditative practices. In this article, the topic is introduced and subsequently explored as a conversation between a neuroscientist (GP) and the abbot of a Zen SAitAi monastery (FTG). We believe that such 'mutual perturbations' between the third-person descriptions provided by scientific research and the phenomenological depth of Buddhist lore have a great potential for advancing our understanding of both brain function and meditation.
Attention is both ubiquitous throughout and key to our cognitive experience. It has been shown to filter out mundane stimuli, while simultaneously communicating specific stimuli from the lowest levels of perception th...
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ISBN:
(纸本)9783319516912;9783319516905
Attention is both ubiquitous throughout and key to our cognitive experience. It has been shown to filter out mundane stimuli, while simultaneously communicating specific stimuli from the lowest levels of perception through to the highest levels of cognition. In this paper we present a connectionist system with mechanisms that produce both exogenous (bottom-up) and endogenous (top-down) attention. The foundational algorithm of our system is the Temporal Pooler (TP), a neocortically inspired algorithm that learns and predicts temporal sequences. We make a number of modifications to the Temporal Pooler and place it in a framework which is inspired by predictive coding. We use a novel technique in which feedback connections elicit endogenous attention by disrupting the learned representations of attended sequences. Our experiments show that this approach successfully filters attended stimuli and suppresses unattended stimuli.
One type of machine learning, text classification, is now regularly applied in the legal matters involving voluminous document populations because it can reduce the time and expense associated with the review of those...
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ISBN:
(纸本)9781538627150
One type of machine learning, text classification, is now regularly applied in the legal matters involving voluminous document populations because it can reduce the time and expense associated with the review of those documents. One form of machine learning - Active Learning - has drawn attention from the legal community because it offers the potential to make the machine learning process even more effective. Active Learning, applied to legal documents, is considered a new technology in the legal domain and is continuously applied to all documents in a legal matter until an insignificant number of relevant documents are left for review. This implementation is slightly different than traditional implementations of Active Learning where the process stops once achieving acceptable model performance. The purpose of this paper is twofold: (i) to question whether Active Learning actually is a superior learning methodology and (ii) to highlight the ways that Active Learning can be most effectively applied to real legal industry data. Unlike other studies, our experiments were performed against large data sets taken from recent, real-world legal matters covering a variety of areas. We conclude that, although these experiments show the Active Learning strategy popularly used in legal document review can quickly identify informative training documents, it becomes less effective over time. In particular, our findings suggest this most popular form of Active Learning in the legal arena, where the highest-scoring documents are selected as training examples, is in fact not the most efficient approach in most instances. Ultimately, a different Active Learning strategy may be best suited to initiate the predictive modeling process but not to continue through the entire document review.
作者:
Shah, PunitCatmur, CarolineBird, GeoffreyUniv London
Inst Psychiat Psychol & Neurosci Dept Neuroimaging Kings Coll London London SE5 8AF England Univ London
Dept Psychol Sci Birkbeck Coll London WC1E 7IIX England Anglia Ruskin Univ
Dept Psychol Cambridge CB1 1PT England Kings Coll London
Dept Psychol Inst Psychiat Psychol & Neurosci London SE5 8AF England Univ London
MRC Social Genet & Dev Psychiat Ctr Inst Psychiat Psychol & Neurosci Kings Coll London London SE5 8AF England UCL
Inst Cognit Neurosci London WC1N 3AR England
This paper shows how a radical approach to enactivism provides a way of clarifying and unifying different varieties of enactivism and enactivist-friendly approaches so as to provide a genuine alternative to classical ...
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This paper shows how a radical approach to enactivism provides a way of clarifying and unifying different varieties of enactivism and enactivist-friendly approaches so as to provide a genuine alternative to classical cognitivism. Section 1 reminds readers of the broad church character of the enactivism framework. Section 2 explicates how radical enactivism is best understood not as a kind of enactivism per se but as a programme for radicalizing and consolidating the many different enactivist offerings. The main work of radical enactivism is to RECtify, existing varieties of enactivism and other cognate approaches so as to strengthen and unify them into a single collective that can rival classical ways of thinking about mind and cognition. Section 3 shows how even seemingly non-enactivist explanatory offerings-such as predictive processing accounts of cognition-might be RECtified and brought within the enactivist explanatory fold. Section 4 reveals why, once RECtified, enactivist offerings, broadly conceived, qualify as genuine and revolutionary alternatives to classical ways of understanding cognition.
To what extent is our perceptual experience influenced by higher cognitive phenomena like beliefs, desires, concepts, templates? Given recent arguments against the possibility of cognitive penetration, we present stri...
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To what extent is our perceptual experience influenced by higher cognitive phenomena like beliefs, desires, concepts, templates? Given recent arguments against the possibility of cognitive penetration, we present striking evidence against the impenetrability claims. The weak impenetrability claim cannot account for (1) extensive structural feedback organization of the brain, (2) temporally very early feedback loops and (3) functional top-down processes modulating-early visual processes by category-specific information. The strong impenetrability claim could incorporate these data by widening the "perceptual module" such that it includes rich but still internal processing in a very large perceptual module. We argue that this latter view leads to an implausible version of a module. Therefore, we have to accept cognitive penetration of our perceptual experience as the best theoretical account so far given the available empirical evidence. We outline that this does not have any problematic consequences for the relation between perception and cognition. (C) 2016 Elsevier Inc. All rights reserved.
Human working memory is capable to generate dynamically robust and flexible neuronal sequences for action planning, problem solving and decision making. However, current neurocomputational models of working memory fin...
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ISBN:
(纸本)9783319590721;9783319590714
Human working memory is capable to generate dynamically robust and flexible neuronal sequences for action planning, problem solving and decision making. However, current neurocomputational models of working memory find hard to achieve these capabilities since intrinsic noise is difficult to stabilize over time and destroys global synchrony. As part of the principle of free-energy minimization proposed by Karl Friston, we propose a novel neural architecture to optimize the free-energy inherent to spiking recurrent neural networks to regulate their activity. We show for the first time that it is possible to stabilize iteratively the long-range control of a recurrent spiking neurons network over long sequences. We identify our architecture as the working memory composed by the Basal Ganglia and the Intra-Parietal Lobe for action selection and we make some comparisons with other networks such as deep neural networks and neural Turing machines. We name our architecture INFERNO for Iterative Free-Energy Optimization for Recurrent Neural Network. abstract environment.
We introduce a compressible representation of 3D geometry (including its attributes, such as color texture) intermediate between polygonal meshes and point clouds called a polygon cloud. Polygon clouds, compared to po...
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ISBN:
(纸本)9781509041176
We introduce a compressible representation of 3D geometry (including its attributes, such as color texture) intermediate between polygonal meshes and point clouds called a polygon cloud. Polygon clouds, compared to polygonal meshes, are more robust to live capture noise and artifacts. Furthermore, dynamic polygon clouds, compared to dynamic point clouds, are easier to compress, if certain challenges are addressed. In this paper, we propose methods for compressing dynamic polygon clouds using transform coding of color and motion residuals. We find that, compared to static polygon clouds and a fortiori static point clouds, dynamic polygon clouds can improve color compression by up to 2-3 dB in fidelity, and can improve geometry compression up to a factor of 2-5 in bit rate.
Pawe Gadziejewski has recently argued that the framework of predictive processing (PP) postulates genuine representations. His focus is on establishing that certain structures posited by PP actually play a representat...
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Pawe Gadziejewski has recently argued that the framework of predictive processing (PP) postulates genuine representations. His focus is on establishing that certain structures posited by PP actually play a representational role. The goal of this paper is to promote this discussion by exploring the contents of representations posited by PP. Gadziejewski already points out that structural theories of representational content can successfully be applied to PP. Here, I propose to make the treatment slightly more rigorous by invoking Francis Egan's distinction between mathematical and cognitive contents. Applying this distinction to representational contents in PP, I first show that cognitive contents in PP are (partly) determined by mathematical contents, at least in the sense that computational descriptions in PP put constraints on ascriptions of cognitive contents. After that, I explore to what extent these constraints are specific (i.e., whether PP puts unique constraints on ascriptions of cognitive contents). I argue that the general mathematical contents posited by PP do not constrain ascriptions of cognitive content in a specific way (because they are not relevantly different from mathematical contents entailed by, for instance, emulators in Rick Grush's emulation theory). However, there are at least three aspects of PP that constrain ascriptions of cognitive contents in more specific ways: (i) formal PP models posit specific mathematical contents that define more specific constraints;(ii) PP entails claims about how computational mechanisms underpin cognitive phenomena (e.g. attention);(iii) the processing hierarchy posited by PP goes along with more specific constraints.
With the introduction of the psychophysical method of reverse correlation, a holy grail of social psychology appears to be within reach - visualising mental representations. Reverse correlation is a data-driven method...
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With the introduction of the psychophysical method of reverse correlation, a holy grail of social psychology appears to be within reach - visualising mental representations. Reverse correlation is a data-driven method that yields visual proxies of mental representations, based on judgements of randomly varying stimuli. This review is a primer to an influential reverse correlation approach in which stimuli vary by applying random noise to the pixels of images. Our review suggests that the technique is an invaluable tool in the investigation of social perception (e.g., in the perception of race, gender and personality traits), with ample potential applications. However, it is unclear how these visual proxies are best interpreted. Building on advances in cognitive neuroscience, we suggest that these proxies are visual reflections of the internal representations that determine how social stimuli are perceived. In addition, we provide a tutorial on how to perform reverse correlation experiments using R.
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