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Music, rowing, and the aesthetics of rhythm

SENSES & SOCIETY 2019年第1期14卷 1-14页

作者： Vuoskoski, Jonna K. Reynolds, Dee Univ Oslo RITMO Ctr Interdisciplinary Studies Rhythm Time & Dept Musicol Oslo Norway Univ Oslo Dept Psychol Oslo Norway Univ Manchester French Studies Manchester Lancs England

Having "good rhythm" is essential in both music and competitive rowing, but what exactly constitutes "good rhythm," and how do we achieve it? Although rhythm is often discussed in purely auditory terms, I argue that rhythm is fundamentally a multisensory, kinesthetic phenomenon. By drawing parallels between music and rowing, I illustrate how biological motion principles underlie the parameters of rhythm in both disciplines, and how the cognition and appreciation of rhythm is deeply embodied. I suggest that the two main ways in which rhythms generate pleasure in both music and rowing are by enabling behavioral synchrony between individuals, and by engaging the body in the cognitive process of rhythm perception and prediction. In essence, "good rhythm"-a rhythm that is enjoyed and appreciated-is rhythm that moves.

关键词： Rhythm music rowing embodied cognition synchronization predictive coding

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Machine Learning in Legal Practice Notes from Recent History 19

Machine Learning in Legal Practice Notes from Recent History

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2nd AAAI/ACM Conference on AI, Ethics, and Society (AIES)

作者： Delgado, Fernando A. Cornell Univ Informat Sci Ithaca NY 14853 USA

ISBN: (纸本)9781450363242

Often framed as a relatively new and controversial phenomenon, the application of machine learning (ML) techniques to legal analysis and decision-making in the US justice system has a rich yet under examined history. My research examines how ML came to be adopted as a standard tool for automating fact discovery for high-stakes civil litigation. By analyzing the key controversies and consensuses that emerge during the experimentation and early adoption phase of this technology (2008-2015), a useful case study presents itself in which an expert professional field wrestled with the challenges of integrating ML into sensitive decision-making workflows.

关键词： Applied Machine Learning predictive coding Technology-Assisted Review Electronic Discovery Civil Discovery Sociotechnical Systems Computational Law AI Governance

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A Framework for Explainable Text Classification in Legal Document Review

A Framework for Explainable Text Classification in Legal Doc...

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IEEE International Conference on Big Data (Big Data)

作者： Mahoney, Christian J. Zhang, Jianping Huber-Fliflet, Nathaniel Gronvall, Peter Zhao, Haozhen Cleary Gottlieb Steen & Hamilton LLP E Discovery Washington DC 20037 USA Ankura Consulting Grp LLC Data & Technol Washington DC USA

ISBN: (纸本)9781728108582

Companies regularly spend millions of dollars producing electronically-stored documents in legal matters. Over the past two decades, attorneys have been using a variety of technologies to conduct this exercise, and most recently, parties on both sides of the 'legal aisle' are accepting the use of machine learning techniques like text classification to cull massive volumes of data and to identify responsive documents for use in these matters. While text classification is regularly used to reduce the discovery costs in legal matters, text classification also faces a peculiar perception challenge: amongst lawyers, this technology is sometimes looked upon as a "black box." Put simply, very little information is provided for attorneys to understand why documents are classified as responsive. In recent years, a group of AI and Machine Learning researchers have been actively researching Explainable AI. In an explainable AI system, actions or decisions are human understandable. In legal 'document review' scenarios, a document can be identified as responsive, as long as one or more of the text snippets (small passages of text) in a document are deemed responsive. In these scenarios, if text classification can be used to locate these responsive snippets, then attorneys could easily evaluate the model's document classification decision. When deployed with defined and explainable results, text classification can drastically enhance the overall quality and speed of the document review process by reducing the time it takes to review documents. Moreover, explainable predictive coding provides lawyers with greater confidence in the results of that supervised learning task. This paper describes a framework for explainable text classification as a valuable tool in legal services: for enhancing the quality and efficiency of legal document review and for assisting in locating responsive snippets within responsive documents. This framework has been implemented in our legal analytics pro

关键词： machine learning text categorization text classification explainable AI predictive coding legal document review XAI false negatives

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Listening with great expectations: An investigation of word form anticipations in naturalistic speech 20

Listening with great expectations: An investigation of word ...

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Interspeech Conference

作者： Bentum, M. ten Bosch, L. van den Bosch, A. Ernestus, M. Radboud Univ Nijmegen Ctr Language Studies Nijmegen Netherlands Max Planck Inst Psycholinguist Nijmegen Netherlands KNAW Meertens Inst Amsterdam Netherlands

The event-related potential (ERP) component named phonological mismatch negativity (PMN) arises when listeners hear an unexpected word form in a spoken sentence [1]. The PMN is thought to reflect the mismatch between expected and perceived auditory speech input. In this paper, we use the PMN to test a central premise in the predictive coding framework [2], namely that the mismatch between prior expectations and sensory input is an important mechanism of perception. We test this with natural speech materials containing approximately 50,000 word tokens. The corresponding EEG-signal was recorded while participants (n = 48) listened to these materials. Following [3], we quantify the mismatch with two word probability distributions (WPD): a WPD based on preceding context, and a WPD that is additionally updated based on the incoming audio of the current word. We use the between-WPD cross entropy for each word in the utterances and show that a higher cross entropy correlates with a more negative PMN. Our results show that listeners anticipate auditory input while processing each word in naturalistic speech. Moreover, complementing previous research, we show that predictive language processing occurs across the whole probability spectrum.

关键词： phonological mismatch negativity speech perception statistical language models predictive coding electroencephalography

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Quantifying expectation modulation in human speech processing 20

Quantifying expectation modulation in human speech processin...

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Interspeech Conference

The mismatch between top-down predicted and bottom-up perceptual input is an important mechanism of perception according to the predictive coding framework (Friston, [1]). In this paper we develop and validate a new information-theoretic measure that quantifies the mismatch between expected and observed auditory input during speech processing. We argue that such a mismatch measure is useful for the study of speech processing. To compute the mismatch measure, we use naturalistic speech materials containing approximately 50,000 word tokens. For each word token we first estimate the prior word probability distribution with the aid of statistical language modelling, and next use automatic speech recognition to update this word probability distribution based on the unfolding speech signal. We validate the mismatch measure with multiple analyses, and show that the auditory-based update improves the probability of the correct word and lowers the uncertainty of the word probability distribution. Based on these results, we argue that it is possible to explicitly estimate the mismatch between predicted and perceived speech input with the cross entropy between word expectations computed before and after an auditory update.

关键词： speech perception predictive coding statistical language modelling automatic speech recognition

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Hierarchical models of pain: Inference, information-seeking, and adaptive control

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NEUROIMAGE 2020年 222卷 117212-117212页

作者： Seymour, Ben Mancini, Flavia Univ Cambridge Dept Engn Computat & Biol Learning Lab Cambridge England Natl Inst Informat & Commun Technol Ctr Informat & Neural Networks Osaka Japan

Computational models of pain consider how the brain processes nociceptive information and allow mapping neural circuits and networks to cognition and behaviour. To date, they have generally have assumed two largely independent processes: perceptual inference, typically modelled as an approximate Bayesian process, and action control, typically modelled as a reinforcement learning process. However, inference and control are intertwined in complex ways, challenging the clarity of this distinction. Here, we consider how they may comprise a parallel hierarchical architecture that combines inference, information-seeking, and adaptive value-based control. This sheds light on the complex neural architecture of the pain system, and takes us closer to understanding from where pain 'arises' in the brain.

关键词： Pain Nociception Information theory Reinforcement learning Optimal control predictive coding Epistemic value Free energy principle Endogenous modulation

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Prediction-error signals to violated expectations about person identity and head orientation are doubly-dissociated across dorsal and ventral visual stream regions

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NEUROIMAGE 2020年第0期206卷 116325-000页

作者： Robinson, Jonathan E. Woods, Will Leung, Sumie Kaufman, Jordy Breakspear, Michael Young, Andrew W. Johnston, Patrick J. Queensland Univ Technol Sch Psychol & Counselling Victoria Pk Rd Kelvin Grove Qld 4059 Australia Inst Hlth & Biomed Innovat 60 Musk Ave Kelvin Grove Qld 4059 Australia QIMR Berghofer Med Res Inst 300 Herston Rd Herston Qld 4006 Australia Swinburne Univ Technol Brain & Psychol Sci Res Ctr Burwood Rd Hawthorn Vic 3123 Australia Univ Newcastle Sch Psychol Univ Dr Callaghan NSW 2308 Australia Univ York Dept Psychol Heslington YO10 5DD England

predictive coding theories of perception highlight the importance of constantly updated internal models of the world to predict future sensory inputs. Importantly, such theories suggest that prediction-error signalling should be specific to the violation of predictions concerning distinct attributes of the same stimulus. To interrogate this as yet untested prediction, we focused on two different aspects of face perception (identity and orientation) and investigated whether cortical regions which process particular stimulus attributes also signal prediction violations with respect to those same stimulus attributes. We employed a paradigm using sequential trajectories of images to create perceptual expectations about face orientation and identity, and then parametrically violated each attribute. Using MEG data, we identified double dissociations of expectancy violations in the dorsal and ventral visual streams, such that the right fusiform gyrus showed greater prediction-error signals to identity violations than to orientation violations, whereas the left angular gyrus showed the converse pattern of results. Our results suggest that perceptual prediction-error signalling is directly linked to regions associated with the processing of different stimulus properties.

关键词： Face processing MEG source localisation Prediction-error Visual processing hierarchy predictive coding

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Object recognition is enabled by an experience-dependent appraisal of visual features in the brain's value system

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NEUROIMAGE 2020年 221卷 117143-117143页

作者： Kozunov, Vladimir V. West, Timothy O. Nikolaeva, Anastasia Y. Stroganova, Tatiana A. Friston, Karl J. Moscow State Univ Psychol & Educ MEG Ctr 29 Sretenka Moscow Russia Univ Oxford John Radcliffe Hosp Nuffield Dept Clin Neurosci Oxford OX3 9DU England UCL Wellcome Trust Ctr Neuroimaging 12 Queen Sq London WC1N 3AR England

This paper addresses perceptual synthesis by comparing responses evoked by visual stimuli before and after they are recognized, depending on prior exposure. Using magnetoencephalography, we analyzed distributed patterns of neuronal activity - evoked by Mooney figures - before and after they were recognized as meaningful objects. Recognition induced changes were first seen at 100-120 ms, for both faces and tools. These early effects - in right inferior and middle occipital regions - were characterized by an increase in power in the absence of any changes in spatial patterns of activity. Within a later 210-230 ms window, a quite different type of recognition effect appeared. Regions of the brain's value system (insula, entorhinal cortex and cingulate of the right hemisphere for faces and right orbitofrontal cortex for tools) evinced a reorganization of their neuronal activity without an overall power increase in the region. Finally, we found that during the perception of disambiguated face stimuli, a face-specific response in the right fusiform gyrus emerged at 240-290 ms, with a much greater latency than the well-known N170m component, and, crucially, followed the recognition effect in the value system regions. These results can clarify one of the most intriguing issues of perceptual synthesis, namely, how a limited set of high-level predictions, which is required to reduce the uncertainty when resolving the ill-posed inverse problem of perception, can be available before category-specific processing in visual cortex. We suggest that a subset of local spatial features serves as partial cues for a fast re-activation of object-specific appraisal by the value system. The ensuing top-down feedback from value system to visual cortex, in particular, the fusiform gyrus enables high levels of processing to form category-specific predictions. This descending influence of the value system was more prominent for faces than for tools, the fact that reflects different dependence

关键词： Visual perception Object recognition Mooney figure disambiguation Magnetoencephalography Value system Prior experience predictive coding Region-based multivariate pattern analysis Representational similarity analysis Temporal cross-decoding generalization

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Empirical Comparisons of CNN with Other Learning Algorithms for Text Classification in Legal Document Review

Empirical Comparisons of CNN with Other Learning Algorithms ...

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IEEE International Conference on Big Data (Big Data)

作者： Keeling, Robert Chhatwal, Rishi Huber-Fliflet, Nathaniel Zhang, Jianping Wei, Fusheng Zhao, Haozhen Shi, Ye Qin, Han Sidley Austin LLP Complex Commercial Litigat Washington DC 20005 USA AT&T Serv Inc Legal Washington DC USA Ankura Data & Technol Washington DC USA

ISBN: (纸本)9781728108582

Research has shown that Convolutional Neural Networks (CNN) can be effectively applied to text classification as part of a predictive coding protocol. That said, most research to date has been conducted on data sets with short documents that do not reflect the variety of documents in real world document reviews. Using data from four actual reviews with documents of varying lengths, we compared CNN with other popular machine learning algorithms for text classification, including Logistic Regression, Support Vector Machine, and Random Forest. For each data set, classification models were trained with different training sample sizes using different learning algorithms. These models were then evaluated using a large randomly sampled test set of documents, and the results were compared using precision and recall curves. Our study demonstrates that CNN performed well, but that there was no single algorithm that performed the best across the combination of data sets and training sample sizes. These results will help advance research into the legal profession's use of machine learning algorithms that maximize performance.

关键词： text classification predictive coding CNN legal document review machine learning

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Achieving Human-Robot Collaboration with Dynamic Goal Inference by Gradient Descent 26th

Achieving Human-Robot Collaboration with Dynamic Goal Infere...

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26th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS)

作者： Murata, Shingo Masuda, Wataru Chen, Jiayi Arie, Hiroaki Ogata, Tetsuya Sugano, Shigeki Natl Inst Informat Principles Informat Res Div Tokyo Japan Grad Univ Adv Studies SOKENDAI Dept Informat Tokyo Japan Waseda Univ Dept Modern Mech Engn Tokyo Japan Waseda Univ Future Robot Org Tokyo Japan Waseda Univ Dept Intermedia Art & Sci Tokyo Japan

ISBN: (纸本)9783030367114;9783030367107

Collaboration with a human partner is a challenging task expected of intelligent robots. To realize this, robots need the ability to share a particular goal with a human and dynamically infer whether the goal state is changed by the human. In this paper, we propose a neural network-based computational framework with a gradient-based optimization of the goal state that enables robots to achieve this ability. The proposed framework consists of convolutional variational autoencoders (ConvVAEs) and a recurrent neural network (RNN) with a long short-term memory (LSTM) architecture that learns to map a given goal image for collaboration to visuomotor predictions. More specifically, visual and goal feature states are first extracted by the encoder of the respective ConvVAEs. Visual feature and motor predictions are then generated by the LSTM based on their current state and are conditioned according to the extracted goal feature state. During collaboration after the learning process, the goal feature state is optimized by gradient descent to minimize errors between the predicted and actual visual feature states. This enables the robot to dynamically infer situational (goal) changes of the human partner from visual observations alone. The proposed framework is evaluated by conducting experiments on a human-robot collaboration task involving object assembly. Experimental results demonstrate that a robot equipped with the proposed framework can collaborate with a human partner through dynamic goal inference even when the situation is ambiguous.

关键词： Human-robot collaboration Robot learning Deep learning predictive coding Prediction error minimization

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