How different are the visual representations used for object recognition in middle childhood and adulthood?

Recent experimental studies have shown that development towards adult performance levels in configural processing in object recognition is delayed through middle childhood. Whilst partchanges to animal and artefact stimuli are processed with similar to adult levels of accuracy from 7 years of age, relative size changes to stimuli result in a significant decrease in relative performance for participants aged between 7 and 10. Two sets of computational experiments were run using the JIM3 artificial neural network with adult and 'immature' versions to simulate these results. One set progressively decreased the number of neurons involved in the representation of view-independent metric relations within multi-geon objects. A second set of computational experiments involved decreasing the number of neurons that represent view-dependent (nonrelational) object attributes in JIM3's Surface Map. The simulation results which show the best qualitative match to empirical data occurred when artificial neurons representing metric-precision relations were entirely eliminated. These results therefore provide further evidence for the late development of relational processing in object recognition and suggest that children in middle childhood may recognise objects without forming structural description representations.

To move or not to move, that is the question! Body schema and non-action oriented body representations:an fMRI meta-analytic study

Many studies have attempted to identify the different cognitive components of body representation (BR). Due to methodological issues, the data reported in these studies are often confusing. Here we summarize the fMRI data from previous studies and explore the possibility of a neural segregation between BR supporting actions (body-schema, BS) or not (non-oriented-to-action-body-representation, NA). We performed a general activation likelihood estimation meta-analysis of 59 fMRI experiments and two individual meta-analyses to identify the neural substrates of different BR. Body processing involves a wide network of areas in occipital, parietal, frontal and temporal lobes. NA selectively activates the somatosensory primary cortex and the supramarginal gyrus. BS involves the primary motor area and the right extrastriate body area. Our data suggest that motor information and recognition of body parts are fundamental to build BS. Instead, sensory information and processing of the egocentric perspective are more important for NA. In conclusion, our results strongly support the idea that different and segregated neural substrates are involved in body representations orient or not to actions.

Resisting the final word:challenging stale media and policy representations of students’ performative technological encounters in university education

This article explores powerful, constraining representations of encounters between digital technologies and the bodies of students and teachers, using corpus-based Critical Discourse Analysis (CDA). It discusses examples from a corpus of UK Higher Education (HE) policy documents, and considers how confronting such documents may strengthen arguments from educators against narrow representations of an automatically enhanced learning. Examples reveal that a promise of enhanced ‘student experience’ through information and communication technologies internalizes the ideological constructs of technology and policy makers, to reinforce a primary logic of exchange value. The identified dominant discursive patterns are closely linked to the Californian ideology. By exposing these texts, they provide a form of ‘linguistic resistance’ for educators to disrupt powerful processes that serve the interests of a neoliberal social imaginary. To mine this current crisis of education, the authors introduce productive links between a Networked Learning approach and a posthumanist perspective. The Networked Learning approach emphasises conscious choices between political alternatives, which in turn could help us reconsider ways we write about digital technologies in policy. Then, based on the works of Haraway, Hayles, and Wark, a posthumanist perspective places human digital learning encounters at the juncture of non-humans and politics. Connections between the Networked Learning approach and the posthumanist perspective are necessary in order to replace a discourse of (mis)representations with a more performative view towards the digital human body, which then becomes situated at the centre of teaching and learning. In practice, however, establishing these connections is much more complex than resorting to the typically straightforward common sense discourse encountered in the Critical Discourse Analysis, and this may yet limit practical applications of this research in policy making.

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.

Towards flexible parsing of structured textual model representations

Existing parsers for textual model representation formats such as XMI and HUTN are unforgiving and fail upon even the smallest inconsistency between the structure and naming of metamodel elements and the contents of serialised models. In this paper, we demonstrate how a fuzzy parsing approach can transparently and automatically resolve a number of these inconsistencies, and how it can eventually turn XML into a human-readable and editable textual model representation format for particular classes of models.