Representation of associative and affective semantic similarity of abstract words in the lateral temporal perisylvian language regions

Dual coding of knowledge in the human brain

“…These properties allow language to support representations about knowledge that are inaccessible to sensory experience (i.e., where sensory-derived representations are absent or insufficient), including those central to human abstract reasoning and modern scientific advances (e.g., knowledge about magnetic fields, infinity, linguistic rules, and abstract object functions; see also Box 2 for the case of number). The findings of the abstract semantic space in the brain's language system are in line with this hypothesis [53,54].…”

Inferior parietal lobule is sensitive to different semantic similarity relations for concrete and abstract words

“…Together, the results of our study indicate that the IPL represents distributional and affective information depending on the word concreteness, implying that semantic representations of abstract and concrete concepts are characterized along different organizational principles in the IPL. Although some fMRI studies found a similarity pattern between the word association‐based model and other brain regions (Liuzzi et al., 2019; Meersmans et al., 2020), we did not find a significant similarity pattern between this model and activity pattern in the IPL. These results suggest that the association strength for written words could represent less similarity between concepts compared to the other measures in the IPL.…”

“…The selection of experimental stimuli was restricted to the nouns for which the following English norms and measures were available that were necessary to compute the similarity measures for the RSA analysis (see below): semantic norms (Buchanan et al., 2013), lexical British National Corpus (Leech et al., 1994), association norms (De Deyne et al., 2019); affective norms (Warriner et al., 2013). In order to obtain a more representative set of abstract and concrete concepts while maximizing their variability (and, thus, RSA efficiency; see e.g., Meersmans et al., 2020), abstract and concrete concepts were selected based on a series of clustering analyses performed on the measures mentioned above, so that they could be grouped into four categories each, composed by a variable number of words (for the abstract concepts: social constructs, social attributes, cognitive events/states, and other abstract constructs; for the concrete concepts: professions, animals, vehicles, and buildings). The small number of words for each category, and their uneven number across categories, prevented us to further investigate between‐categories differences.…”

“…However, we refrained to balance them for further semantic‐lexical variables. Indeed, as already noted, it is important to select variable stimuli for RSA (e.g., Meersmans et al., 2020) and matching abstract and concrete concepts on all semantic‐lexical variables would have resulted in a stimuli set including highly specific concepts, which would have lowered RSA efficiency.…”

“…Each RDM is a symmetric n × n matrix, where n is the number of experimental conditions (i.e., n = 160 words in this study) and each off‐diagonal element indicates the distance for each pair of words in a given measure. We maximized the range in semantic similarity within the concrete and abstract stimulus group as we assumed that a wider range of semantic similarities might increase the sensitivity of the representational similarity analysis of the fNIRS patterns (e.g., Meersmans et al., 2020).…”