Grace Qiyuan Miao

Little is known about the neurocognitive underpinnings of forming social connections. In this study, we tested whether default mode network (DMN) inter-brain synchrony (which prior work has linked to interpersonal alignment, or ‘seeing eye-to-eye’) predicts feelings of connection during conversations. Seventy pairs of strangers engaged in shallow or deep conversations while brain activity was measured with functional near infrared spectroscopy (fNIRS). Stranger dyads in the deep condition felt more connected than those in the shallow condition. Greater feelings of connection were associated with increased DMN synchrony, with the right temporoparietal junction (TPJ) subregion of the DMN emerging as the strongest individual predictor across regression analyses and machine learning classification. These findings suggest that DMN synchrony in general and TPJ synchrony in particular may play a key role in strangers coming to ‘see eye-to-eye’ during face-to-face interactions and feeling more socially connected.

Successfully making connections with others is crucial to navigating the social world and general well-being, yet little is known about connection formation and its neurocognitive underpinnings. Increasingly, neuroscientists use interpersonal ‘neural synchrony’ within the default mode network (DMN) to measure when two or more people subjectively experience something in similar ways. DMN synchrony as ‘seeing eye-to-eye’ is typically observed when multiple people are passively observing the same stimulus. In this study, we tested whether the same DMN synchrony as ‘seeing eye-to-eye’ pattern holds during social interactions. We conducted a between-subject naturalistic experiment with 70 pairs of strangers engaged in either shallow or deep conversations while brain activity was measured with functional near infrared spectroscopy (fNIRS). Stranger dyads successfully formed connections, as indicated by composite connection scores. Replicating Kardas et al. (2021), those in the deep conversation condition felt more connected than those in the shallow conversation condition. DMN neural synchrony significantly predicted self-reported connection, with synchrony in the DMN subregions of medial prefrontal cortex (mPFC) and right temporoparietal junction (TPJ) each correlating significantly with connection. Using machine learning classification, we distinguished high-versus low-connection dyads based on DMN neural synchrony and the perceived depth of conversation with 64.5% accuracy across 1,000 iterations. This effect was primarily carried by right TPJ, which alone classified connection strength at 62.6% accuracy. We consider implications related to the growing loneliness crisis and the importance of understanding how social connections can be formed and fostered in an era of increased social isolation. Significance Statement The current loneliness epidemic has serious consequences on health and well-being. Forming interpersonal connections is crucial for alleviating loneliness, yet little is known about its neural basis. Neural synchrony, a potential biological marker of people being on the ‘same page’, may be an indicator of social connection. We recorded brain activity as strangers engaged in a get-to-know-you conversation and found that neural synchrony—specifically within the default mode network (DMN) and subregions including medial prefrontal cortex (mPFC), and right temporoparietal junction (TPJ)—predicted self-reported connection. Machine learning accurately classified high- and low-connection dyads based on DMN synchrony and perceived conversation depth. These findings suggest that deeper conversations, though more effortful, may foster stronger social bonds with measurable neural correlates.

This paper relies on 327 video recordings of well-baby visits in U.S. community practice clinics to explore when and how pediatricians transition to assessing children’s growth. Using conversation analysis supplemented by descriptive frequencies, we examine the primary ways in which participants transition into the activity of assessing the child’s growth. We argue that physicians most commonly orient to the discussion of physical growth as disjunctive with the prior activity. However, when physicians wind up at odds with a parent over whether or not there is a cause for concern with the child, they more often rely on conjunctive activity transitions. Surprisingly, the activity transition into growth assessment becomes a resource that physicians rely on to persuade parents of their view.

Long, naturalistic stimuli are effective in evoking meaningfully differential neural response patterns between groups. However, the resulting timeseries data often have a high number of features compared to a limited sample size, increasing the likelihood of overfitting and reducing predictive power. This paper introduces multi-timepoint pattern analysis (MTPA) as a temporal dimension reduction approach for improving prediction accuracy when building models with long neural timeseries data. Using feature selection with elastic net regression, MTPA identifies predictive neural patterns while preserving the temporal structure and interpretability of the data. Across two experiments with distinct populations and objectives, MTPA demonstrated consistent advantages over approaches using principal component analysis, windowed averaging, and no dimension reduction. Experiment 1 predicted persistent work-related psychological states in business professionals, achieving accuracies up to 79.1%. Experiment 2 predicted cognitive load and narrative context during video viewing in undergraduates, with accuracies up to 66.5%. These findings suggest that MTPA may be a useful tool for analysing neural data from extended naturalistic designs, enabling researchers to improve prediction accuracy across diverse outcomes and obtain new insights into the temporal dynamics of neural responses.

As people coordinate in daily interactions, they engage in different patterns of behavior to achieve successful outcomes. This includes both synchrony—the temporal coordination of the same behaviors at the same time—and complementarity—the coordination of the same or different behaviors that may occur at different relative times. Using computational methods, we develop a simple framework to describe the interpersonal dynamics of behavioral synchrony and complementarity over time, and explore their task-dependence. A key feature of this framework is the inclusion of a task context that mediates interactions, and consists of active, inactive, and inhibitory constraints on communication. Initial simulation results show that these task constraints can be a robust predictor of simulated agents’ behaviors over time. We also show that the framework can reproduce some general patterns observed in human interaction data. We describe preliminary theoretical implications from these results, and relate them to broader proposals of synergistic self-organization in communication.

As people coordinate in daily interactions, they engage in different patterns of behavior to achieve successful outcomes. This includes both synchrony - the temporal coordination of the same behaviors at the same time - and complementarity - the coordination of the same or different behaviors that may occur at different relative times. Using computational methods, we develop a simple framework to describe the interpersonal dynamics of behavioral synchrony and complementarity over time, and explore their task dependence. A key feature of this framework is the inclusion of a task context that mediates interactions, and consists of active, inactive, and inhibitory constraints on communication. Initial simulation results show that these task constraints can be a robust predictor of simulated agents' behaviors over time. We also show that the framework can reproduce some general patterns observed in human interaction data. We describe preliminary theoretical implications from these results, and relate them to broader proposals of synergistic self-organization in communication.