Strudel: A corpus-based semantic model based on properties and types

Computational models of meaning trained on naturally occurring text successfully model human performance on tasks involving simple similarity measures, but they characterize meaning in terms of undifferentiated bags of words or topical dimensions. This has led some to question their psychological plausibility (Murphy, 2002; Schunn, 1999). We present here a fully automatic method for extracting a structured and comprehensive set of concept descriptions directly from an English part-of-speech-tagged corpus. Concepts are characterized by weighted properties, enriched with concept-property types that approximate classical relations such as hypernymy and function. Our model outperforms comparable algorithms in cognitive tasks pertaining not only to concept-internal structures (discovering properties of concepts, grouping properties by property type) but also to inter-concept relations (clustering into superordinates), suggesting the empirical validity of the property-based approach. Copyright © 2009 Cognitive Science Society, Inc. All rights reserved.

EEG decoding of semantic category reveals distributed representations for single concepts

Achieving a clearer picture of categorial distinctions in the brain is essential for our understanding of the conceptual lexicon, but much more fine-grained investigations are required in order for this evidence to contribute to lexical research. Here we present a collection of advanced data-mining techniques that allows the category of individual concepts to be decoded from single trials of EEG data. Neural activity was recorded while participants silently named images of mammals and tools, and category could be detected in single trials with an accuracy well above chance, both when considering data from single participants, and when group-training across participants. By aggregating across all trials, single concepts could be correctly assigned to their category with an accuracy of 98%. The pattern of classifications made by the algorithm confirmed that the neural patterns identified are due to conceptual category, and not any of a series of processing-related confounds. The time intervals, frequency bands and scalp locations that proved most informative for prediction permit physiological interpretation: the widespread activation shortly after appearance of the stimulus (from 100. ms) is consistent both with accounts of multi-pass processing, and distributed representations of categories. These methods provide an alternative to fMRI for fine-grained, large-scale investigations of the conceptual lexicon. © 2010 Elsevier Inc.

A latent feature analysis of the neural representation of conceptual knowledge

Bayesian probabilistic analysis offers a new approach to characterize semantic representations by inferring the most likely feature structure directly from the patterns of brain activity. In this study, infinite latent feature models [1] are used to recover the semantic features that give rise to the brain activation vectors when people think about properties associated with 60 concrete concepts. The semantic features recovered by ILFM are consistent with the human ratings of the shelter, manipulation, and eating factors that were recovered by a previous factor analysis. Furthermore, different areas of the brain encode different perceptual and conceptual features. This neurally-inspired semantic representation is consistent with some existing conjectures regarding the role of different brain areas in processing different semantic and perceptual properties. © 2012 Springer-Verlag.

Age discrimination before the ECJ - Conceptual and theoretical issues

Although only addressed by EU law from 2000, age discrimination has been the theme of quite a few cases before the Court of Justice, with a high proportion decided by the Grand Chamber recently. This is due to the conceptual and theoretical challenges that a prohibition to use age as differentiating factor poses. After all, age has been an important stratifier used to synchronize life courses through welfare State regimes in Europe. Partly due to these traditions, there are stereotypes associated with old age, and young age, that in turn lead to disadvantage in employment. For the same reason, age discrimination frequently intersects with discrimination on other grounds, such as sex, race or disability. EU legislation on age discrimination has sought to accommodate the traditional role of age in employment policy by allowing wider justifications than for other forms of discrimination. This leads to contradictions within the larger field of discrimination law, which may even threaten to dilute its efficiency. This article analyses how recent case law of the Court of Justice, and in particular its Grand Chamber, deals with the theoretical challenges posed by these conflicting demands on age discrimination and on discrimination law at large.

Cross-participant modelling based on joint or disjoint feature selection: an fMRI conceptual decoding study

Multivariate classification techniques have proven to be powerful tools for distinguishing experimental conditions in single sessions of functional magnetic resonance imaging (fMRI) data. But they are vulnerable to a considerable penalty in classification accuracy when applied across sessions or participants, calling into question the degree to which fine-grained encodings are shared across subjects. Here, we introduce joint learning techniques, where feature selection is carried out using a held-out subset of a target dataset, before training a linear classifier on a source dataset. Single trials of functional MRI data from a covert property generation task are classified with regularized regression techniques to predict the semantic class of stimuli. With our selection techniques (joint ranking feature selection (JRFS) and disjoint feature selection (DJFS)), classification performance during cross-session prediction improved greatly, relative to feature selection on the source session data only. Compared with JRFS, DJFS showed significant improvements for cross-participant classification. And when using a groupwise training, DJFS approached the accuracies seen for prediction across different sessions from the same participant. Comparing several feature selection strategies, we found that a simple univariate ANOVA selection technique or a minimal searchlight (one voxel in size) is appropriate, compared with larger searchlights.

Emotions and Power: Reconciling Conceptual Twins

While the existence of an ‘emotional turn’ within the social sciences is now widely acknowledged, some areas have garnered less specific attention than others. Perhaps the most significant absence within this literature is an explicit exploration of the relationship between emotions and relations of power and domination. This article will attempt such an endeavour. In doing so, I will draw on some key work from within the sociology of emotions, such as Barbalet, Collins, Kemper and Turner, and from the power literature within social theory more generally, including Dahl, Elias, Foucault, Giddens, Gramsci and Lukes. The main thrust of the argument is that power and emotion are conceptual twins in need of a serious theoretical reunion, and that emotions have played a largely unacknowledged, ‘under-labouring’ role within most theories of power. The need for a more unified approach to these two concepts is highlighted.