Observing repetitive finger movements modulates response times of auditorily cued finger movements

Our motor and perceptual representations of actions seem to be intimately linked and the human mirror neuron system (MNS) has been proposed as the mediator. In two experiments, we presented biological or non-biological movement stimuli that were either congruent or incongruent to a required response prompted by a tone. When the tone occurred with the onset of the last movement in a series, i.e., it was perceived during the movement presentation, congruent biological stimuli resulted in faster reaction times than congruent non-biological stimuli. The opposite was observed for incongruent stimuli. When the tone was presented after visual movement stimulation, however, no such interaction was present. This implies that biological movement stimuli only affect motor behaviour during visual processing but not thereafter. These data suggest that the MNS is an “online” system; longstanding repetitive visual stimulation (Experiment 1) has no benefit in comparison to only one or two repetitions (Experiment 2).

From the medicalisation of dementia to the politics of memory and identity in three Spanish documentary films:Bicicleta, cullera, poma, Las voces de la memoria and Bucarest: la memòria perduda

This paper explores how the concept of Alzheimer’s disease (AD) is constructed through Spanish media and documentary films and how it is represented. The article analyses three documentary films and the cultural and social contexts in and from which they emerged: Solé´s Bucarest: la memòria perduda [Bucharest: Memory Lost] (2007), Bosch´s Bicicleta, cullera, poma [Bicycle, Spoon, Apple] (2010) , and Frabra’s Las voces de la memoria [Memory´s Voices] (2011). The three documentary films approach AD from different perspectives, creating well-structured discourses of what AD represents for contemporary Spanish society, from medicalisation of AD to issues of personhood and citizenship. These three films are studied from an interdisciplinary perspective, in an effort to strengthen the links between ageing and dementia studies and cultural studies. Examining documentary film representations of AD from these perspectives enables semiotic analyses beyond the aesthetic perspectives of film studies, and the exploration of the articulation of knowledge and power in discourses about AD in contemporary Spain

Lexico-grammatical portraits of vulnerable women in war:the 1641 depositions

The 1641 Depositions are testimonies collected from (mainly Protestant) witnesses documenting their experiences of the Irish uprising that began in October 1641. As news spread across Europe of the events unfolding in Ireland, reports of violence against women became central to the ideological construction of the barbarism of the Catholic rebels. Against a backdrop of women's subordination and firmly defined gender roles, this article investigates the representation of women in the Depositions, creating what we have termed "lexico-grammatical portraits" of particular categories of woman. In line with other research dealing with discursive constructions in seventeenth-century texts, a corpus-assisted discourse analytical approach is taken. Adopting the assumptions of Critical Discourse Analysis, the discussion is extended to what the findings reveal about representations of the roles of women, both in the reported events and in relation to the dehumanisation of the enemy in atrocity propaganda more generally. © John Benjamins Publishing Company.

Improving distributed representation of word sense via WordNet Gloss composition and context clustering

In recent years, there has been an increas-ing interest in learning a distributed rep-resentation of word sense. Traditional context clustering based models usually require careful tuning of model parame-ters, and typically perform worse on infre-quent word senses. This paper presents a novel approach which addresses these lim-itations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned represen-tations outperform the publicly available embeddings on 2 out of 4 metrics in the word similarity task, and 6 out of 13 sub tasks in the analogical reasoning task.

'Er's from off:the indexicalization and enregisterment of Black Country dialect

This article considers how conscious use of dialect in writing is an intentional act and can be accounted for through the notion of enregisterment. It does this by exploring the value of dialect in social and ideological contexts in relation to a regional dialect of British speech, that of the Black Country in the West Midlands region of England. The article provides a summary of recent directions in sociolinguistic research and an overview of the Black Country speech community, including a summary of its distinctive linguistic variables. This description is then used as an external evaluation of the authenticity of written representations of Black Country speech and the items enregistered in writing. Analysis of three written texts taken from three different genres across a time span of 30 years reveals the extent to which identified linguistic features are drawn upon in each one of the three texts and the extent to which any one is enregisterd across all three. The article discusses the social and linguistic contexts within which the writing occurs by way of accounting for their enregisterment as markers of identity linked to region and place. It also considers the ways in which the texts juxtapose norms and values of those "within" the community with those from "outside" the community in ways that subvert traditional notions of linguistic hierarchy.

Transgressing community:the case of Muslims in a twenty-first-century British city

This paper draws on ethnographic research carried out in Birmingham, UK – a city significant for its sizeable Muslim population and its iconic role in the history of minority ethnic settlement in Britain – to consider how associations of place and ethnicity work in different ways to inform ideas about ‘Muslim community’ in twenty-first-century Britain. The paper charts happenings around a local event in an area of majority Asian settlement and how representations of the area as a place of Muslim community were used to implicate it in the ‘war on terror’. The paper goes on to show how this sensibility is disrupted by Muslims themselves through alternative engagements with space and ethnicity. The paper argues that these offer a ground for making Muslim community in ways that actively engage with histories and patterns of ethnic settlement in the city rather than being determined by them.

Projected sequential Gaussian processes:a C++ tool for interpolation of large datasets with heterogeneous noise

Heterogeneous datasets arise naturally in most applications due to the use of a variety of sensors and measuring platforms. Such datasets can be heterogeneous in terms of the error characteristics and sensor models. Treating such data is most naturally accomplished using a Bayesian or model-based geostatistical approach; however, such methods generally scale rather badly with the size of dataset, and require computationally expensive Monte Carlo based inference. Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential Bayesian framework for inference in such projected processes is presented. The observations are considered one at a time which avoids the need for high dimensional integrals typically required in a Bayesian approach. A C++ library, gptk, which is part of the INTAMAP web service, is introduced which implements projected, sequential estimation and adds several novel features. In particular the library includes the ability to use a generic observation operator, or sensor model, to permit data fusion. It is also possible to cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the covariance parameters is explored, including the impact of the projected process approximation on likelihood profiles. We illustrate the projected sequential method in application to synthetic and real datasets. Limitations and extensions are discussed. © 2010 Elsevier Ltd.

Transgressing community:the case of Muslims in a twenty-first-century British city

This paper draws on ethnographic research carried out in Birmingham, UK - a city significant for its sizeable Muslim population and its iconic role in the history of minority ethnic settlement in Britain - to consider how associations of place and ethnicity work in different ways to inform ideas about 'Muslim community' in twenty-first-century Britain. The paper charts happenings around a local event in an area of majority Asian settlement and how representations of the area as a place of Muslim community were used to implicate it in the 'war on terror'. The paper goes on to show how this sensibility is disrupted by Muslims themselves through alternative engagements with space and ethnicity. The paper argues that these offer a ground for making Muslim community in ways that actively engage with histories and patterns of ethnic settlement in the city rather than being determined by them.

Learning user and product distributed representations using a sequence model for sentiment analysis

In product reviews, it is observed that the distribution of polarity ratings over reviews written by different users or evaluated based on different products are often skewed in the real world. As such, incorporating user and product information would be helpful for the task of sentiment classification of reviews. However, existing approaches ignored the temporal nature of reviews posted by the same user or evaluated on the same product. We argue that the temporal relations of reviews might be potentially useful for learning user and product embedding and thus propose employing a sequence model to embed these temporal relations into user and product representations so as to improve the performance of document-level sentiment analysis. Specifically, we first learn a distributed representation of each review by a one-dimensional convolutional neural network. Then, taking these representations as pretrained vectors, we use a recurrent neural network with gated recurrent units to learn distributed representations of users and products. Finally, we feed the user, product and review representations into a machine learning classifier for sentiment classification. Our approach has been evaluated on three large-scale review datasets from the IMDB and Yelp. Experimental results show that: (1) sequence modeling for the purposes of distributed user and product representation learning can improve the performance of document-level sentiment classification; (2) the proposed approach achieves state-of-The-Art results on these benchmark datasets.