Article Figures & Data
Figures
- Figure 1.
Experimental methods. a, Forty-five subjects listened to four auditory stories during fMRI scanning (Nastase et al., 2021). b, Human ratings were used to assign a continuous value of concreteness (i.e., position along the concrete–abstract axis) for as many words as possible within each story. This process was repeated with other linguistic properties including frequency, valence, and arousal (data not shown). c, Any apparent variation across subjects in neural representations of word properties could stem from two possible underlying patterns: neural representations could be reliably idiosyncratic within subjects, evidenced by high similarity of representations within the same subject across distinct experiences (here, stories), or these representations could be unstable both within and across subjects, evidenced by variability within the same subject across stories. d, Example procedure for calculating reliability and identifiability for one word property. For each story, voxel-wise beta values were estimated within a general linear model. Then, within each of 200 parcels (Schaefer parcellation), beta values were correlated between all subjects for each pair of stories (6 unique pairs). These story similarity matrices were then averaged and used to estimate two indices of stable, individualized neural representations: (1) reliability, defined as the difference between within-subject and average across-subject similarity, and (2) identifiability, defined as the fingerprinting accuracy of discriminating one subject from all other subjects based on their neural representations. This process was repeated for each word property.
- Figure 2.
Group-level univariate activation to sensory and linguistic properties. Across stories and subjects, multiple regions exhibited significant activation to the intensity of sound- and word-level linguistic properties including the concrete–abstract axis, frequency, valence, and arousal. Results shown are from a single linear mixed-effects model containing fixed effects for all properties plus random effects for story and subject. Results are displayed at a voxel-wise threshold of qFDR < 0.05.
- Figure 3.
Within- and across-subject reliability of neural representations of word properties. We compared representations of word properties across four naturalistic stories both within and across subjects. a, Across stories, all properties except valence exhibited high within-subject reliability across much of the cortex (qFDR < 0.05; null, 10,000 permutations). While a simple sensory property, loudness, exhibited the highest reliability, representations of the concrete–abstract axis were more reliable than other linguistic properties (frequency, valence, arousal). b, At the whole-brain level, across all properties, within-subject across–story similarity was consistently higher than across-subject similarity, even when comparing representations across subjects within the same stories. Each data point represents average similarity value in one parcel of the Schaefer parcellation (200 total). c, Representations of all properties enabled accurate identification of subjects across much of the cortex. All plots are thresholded at chance (2.22%). d, Out of tested linguistic properties, subjects were most identifiable from their representations of the concrete–abstract axis. Each dot indicates identifiability within one parcel. *p < 0.05; **p < 0.01; ***p < 0.001; n.s. p > 0.05.
- Figure 4.
Within-subject reliability of neural representations of concrete and abstract words. a, We selected concrete and abstract words as the top/bottom 30% of nouns within the concrete–abstract axis and estimated neural responses to each set of words in a second GLM analysis. b, While both concreteness and abstractness exhibited reliable representations within subjects across stories, representations of concreteness were more reliable than representations of abstractness across much of the cortex (qFDR < 0.05; n = 10,000 permutations). c, d, Representations of concreteness provided a greater ability to identify subjects than representations of abstractness (qFDR < 0.05; n = 10,000 permutations).
- Figure 5.
Stability of concrete and abstract concept cluster representations within and across subjects. a, We clustered the top 30% concrete and top 30% abstract words within a high-dimensional semantic space (GloVe). We then estimated voxel-wise beta values for each of six clusters (3 concrete, 3 abstract) within each subject and story. Next, within each parcel (200 total), we correlated beta values between all sets of clusters across stories and averaged the across-story similarity of clusters. b, Visualization of concept clusters within a two-dimensional projection using UMAP, plus example words from each cluster. c, Within semantic space, words within abstract clusters were more similar (i.e., less distant) than words within concrete clusters. Each dot represents the average similarity of a given word to other words within a given comparison. In contrast, (d) within-subject neural representations of concrete clusters were more similar across stories than representations of abstract clusters. Each dot indicates the average similarity of one subject's concept cluster representations within a given comparison. *p < 0.05; **p < 0.01; ***p < 0.001; n.s. p > 0.05.
