Representation of Semantic Similarity in the Left Intraparietal Sulcus: Functional Magnetic Resonance Imaging Evidence

Abstract

According to a recent study, semantic similarity between concrete entities correlates with the similarity of activity patterns in left middle IPS during category naming. We examined the replicability of this effect under passive viewing conditions, the potential role of visuoperceptual similarity, where the effect is situated compared to regions that have been previously implicated in visuospatial attention, and how it compares to effects of object identity and location. Forty-six subjects participated. Subjects passively viewed pictures from two categories, musical instruments and vehicles. Semantic similarity between entities was estimated based on a concept-feature matrix obtained in more than 1,000 subjects. Visuoperceptual similarity was modeled based on the HMAX model, the AlexNet deep convolutional learning model, and thirdly, based on subjective visuoperceptual similarity ratings. Among the IPS regions examined, only left middle IPS showed a semantic similarity effect. The effect was significant in hIP1, hIP2, and hIP3. Visuoperceptual similarity did not correlate with similarity of activity patterns in left middle IPS. The semantic similarity effect in left middle IPS was significantly stronger than in the right middle IPS and also stronger than in the left or right posterior IPS. The semantic similarity effect was similar to that seen in the angular gyrus. Object identity effects were much more widespread across nearly all parietal areas examined. Location effects were relatively specific for posterior IPS and area 7 bilaterally. To conclude, the current findings replicate the semantic similarity effect in left middle IPS under passive viewing conditions, and demonstrate its anatomical specificity within a cytoarchitectonic reference frame. We propose that the semantic similarity effect in left middle IPS reflects the transient uploading of semantic representations in working memory.

No evidence for a difference in lateralization and distinctiveness level of transcranial magnetic stimulation-derived cortical motor representations over the adult lifespan

“…To do so, firstly the Euclidean distance between the CoG locations of FDI and ADM within one hemisphere was calculated: . As a second measure of similarity, the cosine similarity (for details see Figure 1 ) was computed to quantify the (dis)similarity of the FDI and ADM motor representation volumes, thus referring to their 3-dimensional topography ( Mitchell et al, 2008 ; Bruffaerts et al, 2013 ; Neyens et al, 2017 ). Individual muscle representations for both FDI and ADM were expressed as a vector with 121 dimensions (equivalent to the number of the 11 × 11 = 121 grid points) with the mean MEP amplitude of each grid point as the 121 elements of the vector.…”

The Emotional Facet of Subjective and Neural Indices of Similarity

“…In these studies (Goldstone et al 1997; Golonka and Estes 2009; Greene et al 2014; Iordan et al 2015; King et al 2019), participants were involved in explicit similarity judgement tasks. In others (Haxby et al 2001; Kriegeskorte et al 2008a, b, Haxby et al 2011; Bruffaerts et al 2013; Clarke and Tyler 2014; Guntupalli et al 2016; Neyens et al 2017), the main interest was the neural similarity, namely the similarity among neural representations associated with non-emotional stimuli during tasks not related to the similarity judgements. By contrast, less is known about what makes people perceive richer, life-like events as similar, and even less when these are emotional.…”

How Does the “Default Mode” Network Contribute to Semantic Cognition?

“…Compared to univariate approaches, multivoxel pattern analysis (MVPA) methods provide a more specific assessment of the information content encoded in a region of the brain during a task (Kriegeskorte, Mur, & Bandettini, 2008). Studies using MVPA approaches confirm and extend the findings discussed above, further implicating the DMN in semantic processing (e.g., Clarke & Tyler, 2014;Devereux, Clarke, Marouchos, & Tyler, 2013;Fairhall & Caramazza, 2013;Neyens, Bruffaerts, Liuzzi, Kalfas, Peeters, et al, 2017;Viganò, Borghesani, & Piazza, 2021). In an encoding-model MVPA analysis of the same data set from Fernandino, Binder, et al (2016a), Fernandino, Humphries, et al (2016b provided further support for the idea that semantic representations in association cortical areas, including the DMN, encode sensory-motor information from multiple modalities during semantic word processing.…”