Discriminating Taxonomic Categories and Domains in Mental Simulations of Concepts of Varying Concreteness

Most studies of conceptual knowledge in the brain focus on a narrow range of concrete conceptual categories, rely on the researchers' intuitions about which object belongs to these categories, and assume a broadly taxonomic organization of knowledge. In this fMRI study, we focus on concepts with a variety of concreteness levels; we use a state of the art lexical resource (WordNet 3.1) as the source for a relatively large number of category distinctions and compare a taxonomic style of organization with a domain-based model (associating concepts with scenarios). Participants mentally simulated situations associated with concepts when cued by text stimuli. Using multivariate pattern analysis, we find evidence that all Taxonomic categories and Domains can be distinguished from fMRI data and also observe a clear concreteness effect: Tools and Locations can be reliably predicted for unseen participants, but less concrete categories (e.g., Attributes, Communications, Events, Social Roles) can only be reliably discriminated within participants. A second concreteness effect relates to the interaction of Domain and Taxonomic category membership: Domain (e.g., relation to Law vs. Music) can be better predicted for less concrete categories. We repeated the analysis within anatomical regions, observing discrimination between all/most categories in the left middle occipital and temporal gyri, and more specialized discrimination for concrete categories Tool and Location in the left precentral and fusiform gyri, respectively. Highly concrete/abstract Taxonomic categories and Domain were segregated in frontal regions. We conclude that both Taxonomic and Domain class distinctions are relevant for interpreting neural structuring of concrete and abstract concepts.

Homological Localisation of Model Categories

One of the most useful methods for studying the stable homotopy category is localising at some spectrum E. For an arbitrary stable model category we introduce a candidate for the E–localisation of this model category. We study the properties of this new construction and relate it to some well–known categories.

Inferring and Exploiting Categories for Next Location Prediction

Predicting the next location of a user based on their previous visiting pattern is one of the primary tasks over data from location based social networks (LBSNs) such as Foursquare. Many different aspects of these so-called “check-in” profiles of a user have been made use of in this task, including spatial and temporal information of check-ins as well as the social network information of the user. Building more sophisticated prediction models by enriching these check-in data by combining them with information from other sources is challenging due to the limited data that these LBSNs expose due to privacy concerns. In this paper, we propose a framework to use the location data from LBSNs, combine it with the data from maps for associating a set of venue categories with these locations. For example, if the user is found to be checking in at a mall that has cafes, cinemas and restaurants according to the map, all these information is associated. This category information is then leveraged to predict the next checkin location by the user. Our experiments with publicly available check-in dataset show that this approach improves on the state-of-the-art methods for location prediction.

Splitting monoidal stable model categories

If C is a stable model category with a monoidal product then the set of homotopy classes of self-maps of the unit forms a commutative ring, [S,S]C. An idempotent e of this ring will split the homotopy category: [X,Y]C≅e[X,Y]C⊕(1−e)[X,Y]C. We prove that provided the localised model structures exist, this splitting of the homotopy category comes from a splitting of the model category, that is, C is Quillen equivalent to LeSC×L(1−e)SC and [X,Y]LeSC≅e[X,Y]C. This Quillen equivalence is strong monoidal and is symmetric when the monoidal product of C is.