Neural similarity between mentalizing and live social interaction during the transition to adolescence

Abstract

Social interactions are essential for human development, yet little neuroimaging research has examined their underlying neurocognitive mechanisms using socially interactive paradigms during childhood and adolescence. Recent neuroimaging research has revealed activity in the mentalizing network when children engage with a live social partner, even when mentalizing is not required. While this finding suggests that social-interactive contexts may spontaneously engage mentalizing, it is not a direct test of how similarly the brain responds to these two contexts. The current study used representational similarity analysis on data from 8- to 14-year-olds who made mental and nonmental judgments about an abstract character and a live interaction partner during fMRI. A within-subject, 2 (Mental/Nonmental) × 2 (Peer/Character) design enabled us to examine response pattern similarity between conditions, and estimate fit to three conceptual models of how the two contexts relate: (1) social interaction and mentalizing about an abstract character are represented similarly; (2) interactive peers and abstract characters are represented differently regardless of the evaluation type; and (3) mental and nonmental states are represented dissimilarly regardless of target. We found that the temporal poles represent mentalizing and peer interactions similarly (Model 1), suggesting a neurocognitive link between the two in these regions. Much of the rest of the social brain exhibits different representations of interactive peers and abstract characters (Model 2). Our findings highlight the importance of studying social-cognitive processes using interactive approaches, and the utility of pattern-based analyses for understanding how social-cognitive processes relate to each other.

Keywords: development; fMRI; mentalizing; social cognition; social interaction.

FIGURE 1

Schematic of the social‐interactive fMRI task (a). Participants were given a half‐second cue indicating if they would be answering questions provided by their interaction partner (peer) or answering questions presented by the computer about a story character (character). Hints were provided for 3.5 s and either required using mental state information about the target (mental), or nonmental, physical information (nonmental). This yielded a fully within‐subject, 2 (peer/character) × 2 (mental/nonmental) design (b). PM, peer mental; PNM, peer nonmental; CM, character mental, and CNM, character nonmental. Model 1 = interaction engages mentalizing (c), model 2 = interaction model (d), and model 3 = mentalizing model (e)

FIGURE 2

Steps for model‐based representational similarity analysis: We first estimate the voxel‐wise response pattern for each condition for a given brain region using unsmoothed subject‐level models (a), then calculate the Euclidean distance between response patterns for each pair of conditions to construct our neural dissimilarity matrices (which can also be visualized as dendrograms; b), and estimate fit to each of our models by calculating Kendall's tau‐a (c). For the representational connectivity analyses, you examine the fit between the neural dissimilarities between brain regions (d)

FIGURE 3

Visualizations of the 2 × 2 analysis of covariance for accuracy (a) and reaction time (b). Plots use median split on age to visualize the interactions even though the analyses were conducted using age as a continuous variable

FIGURE 4

(a) Social interaction ROIs obtained from Neurosynth, (b) plots from Bayesian multilevel models on model fit for each ROI (b), and (c) scatter plots visualizing the correlation of model 2 fit and age for the left caudate, left TPJ, and right vlPFC. Colors in the BML graph indicate which quantile level each ROI falls in, with green = 97.5% quantile or more (very strong evidence), orange = 95%–97.5% quantile range (strong evidence), gray = 90%–95% quantile range (moderate evidence)

FIGURE 5

Social brain regions of interest (ROI) used in the representational connectivity analysis (a), and the results from the exploratory factor analysis and hierarchical clustering (b). Dendrograms of neural dissimilarity for each cluster averaged across the ROIs comprising the cluster (c)