Responses to illness inferences in the precuneus (PC).

Panel A: Percent signal change (PSC) for each condition among the top 5% Illness-Causal > Mechanical-Causal vertices in a left PC mask (Dufour et al., 2013) in individual participants, established via a leave-one-run-out analysis. Panel B: Whole-cortex results (one-tailed) for Illness-Causal > Mechanical-Causal and Illness-Causal > Non-Causal (both versions of non-causal vignettes), corrected for multiple comparisons (p < .05 FWER, cluster-forming threshold p < .01 uncorrected). Vertices are color coded on a scale from p=0.01 to p=0.00001. Panel C: Example stimuli. ‘Magical’ catch trials similar in meaning and structure (e.g., “Sadie forgot to wash her face after she ran in the heat. Now she has a cucumber nose”) enabled the use of a semantic ‘magic detection’ task.

Spatial dissociation between responses to illness inferences and mental state inferences in the precuneus (PC).

The left medial surface of 6 individual participants were selected for visualization purposes. The locations of the top 10% most responsive vertices to Illness-Causal > Mechanical-Causal in a PC mask (Dufour et al., 2013) are shown in red. The locations of the top 10% most responsive vertices to mentalizing stories > physical stories (mentalizing localizer) in the same PC mask are shown in blue. Overlapping vertices are shown in green.

Individual-subjects analysis of language– and logic-responsive vertices.

Panel A: percent signal change (PSC) for each condition among the top 5% most language-responsive vertices (language > math) in a temporal language network mask (Fedorenko et al., 2010). Results from a frontal language mask (Fedorenko et al., 2010) can be found in Supplementary Figure 6. Panel B: PSC among the top 5% most logic-responsive vertices (logic > language) in a logic network mask (Liu et al., 2020). Group maps for each contrast of interest (one-tailed) are corrected for multiple comparisons (p < .05 FWER, cluster-forming threshold p < .01 uncorrected). Vertices are color coded on a scale from p=0.01 to p=0.00001.

Responses to mechanical inferences in anterior medial ventral occipito-temporal cortex (VOTC).

Panel A: Percent signal change (PSC) for each condition among the top 5% Illness-Causal > Mechanical-Causal vertices in a left anterior medial VOTC mask (Hauptman, Elli, et al., 2023) in individual participants, established via a leave-one-run-out analysis. Panel B: The intersection of two whole-cortex contrasts, Mechanical-Causal > Illness-Causal and Mechanical-Causal > Non-Causal, FWER cluster-correction for multiple comparisons (p < .05 FWER, cluster-forming threshold p < .01 uncorrected). Vertices are color coded on a scale from p=0.01 to p=0.00001. Similar to PC responses to illness inferences, anterior medial VOTC is the only region to emerge across both mechanical inference contrasts. The average PPA location from separate study involving perceptual place stimuli (Weiner et al., 2017) is overlaid in black. The average PPA location from separate study involving verbal place stimuli (Hauptman, Elli, et al., 2023) is overlaid in blue.

Functional localization of language (Liu et al., 2020), logical reasoning (Liu et al., 2020), and mentalizing (Dodell-Feder et al., 2011) networks.

Group maps for each contrast of interest (one-tailed) are corrected for multiple comparisons (p < .05 FWER, cluster-forming threshold p < .01 uncorrected). Vertices are color coded on a scale from p=0.01 to p=0.00001.

Overlap between left precuneus (PC) responses to illness inferences in the current study and people stimuli in a separate study (Fairhall & Caramazza, 2013b).

The average location from a separate study comparing people and place concepts (Fairhall & Caramazza, 2013b) is overlaid in blue on the response to illness inferences observed in the current study. Group map (one-tailed) is corrected for multiple comparisons (p < .05 FWER, cluster-forming threshold p < .01 uncorrected). Vertices are color coded on a scale from p=0.01 to p=0.00001.

Spatial dissociation between responses to illness inferences and mental state inferences in the left precuneus (PC).

The left medial surface of all participants (n=20) is shown. The locations of the top 10% most responsive vertices to Illness-Causal > Mechanical-Causal in a PC mask (Dufour et al., 2013) are shown in red. The locations of the top 10% most responsive vertices to mentalizing stories > physical stories (mentalizing localizer) in the same PC mask are shown in blue. Overlapping vertices are shown in green.

Full whole-cortex results for Illness-Causal > Mechanical-Causal.

Group maps (two-tailed) are corrected for multiple comparisons (p < .05 FWER, cluster-forming threshold p < .01 uncorrected). Vertices are color coded on a scale from p=0.01 to p=0.00001.

Searchlight MVPA group maps.

Whole-brain searchlight maps were thresholded using a combination of vertex-wise threshold (p < 0.001 uncorrected) and cluster size threshold (FWER p < 0.05, corrected for multiple comparisons across the entire cortical surface). Vertices are color coded on a scale from 55-65% decoding accuracy.

Responses to causal inference in the language network.

Panel A: Percent signal change (PSC) for each condition among the top 5% most language-responsive vertices (language > math) in a temporal language network mask (Fedorenko et al., 2010). Panel B: The same results in a frontal language mask (Fedorenko et al., 2010).

Responses to illness inferences in bilateral PC and TPJ.

Top 4 plots: percent signal change (PSC) for each condition among the top 5% Illness-Causal > Mechanical-Causal vertices in bilateral PC and TPJ masks (Dufour et al., 2013) in individual participants, established via a leave-one-run-out analysis. Bottom 4 plots: PSC for each condition among the top 5% mentalizing stories > physical stories vertices in the same masks. We hypothesized that the PC would exhibit a preference for illness inferences and report all other responses for completeness (see preregistration https://osf.io/cx9n2/). Significance codes for Illness-Causal > Mechanical-Causal comparison: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘’ 1.

Responses to mentalizing in illness-responsive vertices in bilateral PC and TPJ.

Percent signal change (PSC) for mentalizing stories and physical stories (mentalizing localizer) was extracted from the top 5% Illness-Causal > Mechanical-Causal vertices in bilateral PC and TPJ masks (Dufour et al., 2013) in individual participants. The difference between mentalizing stories and physical stories was significant (all ps < .01) across all analyses.

Comparison of mentalizing localizers used in previous work and in the current study, in 3 pilot participants.

The mentalizing localizer in the current study used the same mentalizing stories as in previous work (Dodell-Feder et al., 2010) but contained new physical stories that included more vivid physical description and did not refer to animate agents. Group maps are shown at p < .01 uncorrected.

Illness types present in the stimulus set.

MVPA results in individual-subject functional ROIs. Each ROI was created by selecting the top 300 vertices for each contrast in each search space.

Accuracy refers to classifier performance against chance (50%) for Illness-Causal vs. Mechanical-Causal. Permuted and Bonferroni-corrected (across ROIs) p-values are reported. Ment_vs_phys: mentalizing stories > physical stories (mentalizing localizer). Caus_vs_rest: Illness-Causal + Illness-Mechanical > Rest. Logic_vs_lang: logic > language (language/logic localizer). Lang_vs_math: language > math (language/logic localizer).