A brain-based account of “basic-level” concepts

Abstract

This study provides a brain-based account of how object concepts at an intermediate (basic) level of specificity are represented, offering an enriched view of what it means for a concept to be a basic-level concept, a research topic pioneered by Rosch and others (Rosch et al., 1976). Applying machine learning techniques to fMRI data, it was possible to determine the semantic content encoded in the neural representations of object concepts at basic and subordinate levels of abstraction. The representation of basic-level concepts (e.g. bird) was spatially broad, encompassing sensorimotor brain areas that encode concrete object properties, and also language and heteromodal integrative areas that encode abstract semantic content. The representation of subordinate-level concepts (robin) was less widely distributed, concentrated in perceptual areas that underlie concrete content. Furthermore, basic-level concepts were representative of their subordinates in that they were neurally similar to their typical but not atypical subordinates (bird was neurally similar to robin but not woodpecker). The findings provide a brain-based account of the advantages that basic-level concepts enjoy in everyday life over subordinate-level concepts: the basic level is a broad topographical representation that encompasses both concrete and abstract semantic content, reflecting the multifaceted yet intuitive meaning of basic-level concepts.

Putative protective neural mechanisms in prereaders with a family history of dyslexia who subsequently develop typical reading skills

“…The distinct activation patterns between groups could be reflected in the multi-voxel spatial pattern and/or a systemic difference across voxels (Jimura & Poldrack, 2012;Kragel, Carter, & Huettel, 2012). Therefore, follow-up analyses were conducted in regions with significant MVPA results to evaluate whether the potential group differences in activation levels contributed to the distinct activation patterns observed between FHD−Typical and FHD+Typical children (Bauer & Just, 2017;Coutanche, 2013). Since the MVPA considers information across multiple voxels (i.e., activation pattern) as a whole, the subsequent analyses of the group differences in individual voxels were conducted within the context of their contributions to the whole classification model (see a similar analysis in Evans et al, 2014).…”

A Human Visual System Based Temporal Model for Semantic Levels Categorization

“…At higher levels, more areas are included in the categorization which was introduced by Bamer and Just in 2017 [31]. They examined two levels of basic and subordinate and expressed the basic level advantages.…”

Similarities and differences in the neural representations of abstract concepts across English and Mandarin

“…Mean PSCs were normalized across voxels for each trial (MPSC). Previous studies have reported that the mean activation across these four images (as opposed to a GLM measure) yields a high classification accuracy obtained by a classifier that relates the activation pattern to the concept (Bauer & Just, 2017 ; Just et al, 2010 ; Mason & Just, 2016 ).…”