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

Abstract

Abstract■ Most studies of conceptual knowledge in the brain focus on a narrow range of concrete conceptual categories, rely on the researchersʼ 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 (an example domain is Law). 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 mid occipital and left mid 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. ■

Semantic variability predicts neural variability of object concepts

“…While many studies limit their analyses to voxels with the most stable activation profiles (e.g., Mitchell et al, 2008; Anderson et al, 2014), the present work examines voxels that exhibit maximally different responses across stimulus presentations. In our subjects’ gray matter masks, there is only a 0.001% overlap in the top 500 voxels selected by these two criteria.…”

Neural representations of the concepts in simple sentences: Concept activation prediction and context effects

“…Predictive modeling of brain activity associated with concepts was enabled by the postulation of a mediating layer of perceptual and semantic features of the objects, resulting in the decoding from their fMRI signature pictures or text concerning objects (Anderson et al, 2015; Mitchell et al, 2008; Pereira et al, 2011), natural images (Naselaris et al, 2009), faces (Cowen et al, 2014), objects and actions in video clips (Huth et al, 2012; Nishimoto et al, 2011) and in speech (Huth et al, 2016). Other studies have found distinct activation patterns associated with the neural representations of concepts of varying degrees of semantic abstractness (Anderson et al, 2014; Ghio et al, 2016; Wang et al, 2013). A few studies have further demonstrated the ability to associate brain activation patterns with inter-concept relations in a proposition (Frankland and Greene, 2015; Wang et al, 2016).…”

Sentential negation of abstract and concrete conceptual categories: a brain decoding multivariate pattern analysis study

“…independently of the semantic meanings on which negation operates), an aspect for which limited evidence obtained by means of univariate analysis techniques is available [14,22]. CP4 and CP5 tackled the important question of replicability of previous results for concreteness [21] and fine-grained conceptual categories [23,24].…”