Matthew D. Lieberman

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.

Naturalistic interactions involve dynamic, nonlinear coordination that unfolds across multiple timescales, yet most hyperscanning studies rely on analytical methods designed for passive, stimulus-locked contexts. We introduce cross-recurrence quantification analysis (CRQA)—a method that treats signals as a coupled dynamical system and characterizes the patterns of their joint trajectories across time—to measure brain-to-brain coupling during a free-flowing negotiation task. Dyads were scanned with fNIRS as they negotiated budget allocations to solve a public health crisis. We measured neural coupling in regions critical for social cognition—medial prefrontal cortex and temporal parietal junction—and related coupling patterns to both objective negotiation behaviors and subjective experiences. While conventional measures of neural synchrony, such as inter-subject correlation and wavelet coherence, showed no relationships with outcomes, CRQA revealed systematic associations between dynamic coupling patterns and successful interaction. We found that balanced neural coordination, where leading and lagging flowed symmetrically between partners, predicted greater collaborative allocation adjustment and more positive social experiences, including shared understanding, cooperation, and liking. Longer delays in neurocognitive coordination, as opposed to immediate alignment, were associated with greater feelings of motivation during the interaction. Finally, greater complexity, or entropy, of neural coupling was linked to more parity in how partners moved toward the joint solution rather than a one-sided accommodation. These findings demonstrate that real social interaction can be captured through analytical methods that account for the dynamic, nonlinear processes being studied, creating new possibilities for understanding how minds connect during natural human interaction.

Persuasive communication in marketing, political, and health domains influences sales, elections, and public health. We present a mega-analysis (a pooled analysis of raw data) of 16 functional MRI datasets (572 participants, 739 messages, and 21,688 experimental trials) assessing the neural correlates of the effectiveness of messages in individual message receivers and at scale (in large groups of message receivers who did not undergo neuroimaging). Existing theories suggest that decision-making is driven by expected rewards and perceived social relevance associated with the expected outcomes of a given choice. Consistent with these theories, we find that (i) brain activity implicated in reward and social processing is associated with message effectiveness in individuals and at scale across diverse domains (e.g. marketing and health campaigns); (ii) exploratory analysis further suggests language, emotion, and sensorimotor processes as pertinent to message effectiveness; and (iii) brain activity provides complementary information on message effectiveness at scale beyond self-reports provided by the same neuroimaging participants. This study offers novel insights into the neurocognitive mechanisms underlying effective messaging, highlights a path toward greater unity and efficiency in persuasion research, and suggests practical intervention targets for message design.

Common workplace challenges such as feeling overwhelmed, burned out, or disengaged often remain hidden due to fear of judgment or social norms, contributing to rising mental health crises and organizational dysfunction. This study presents a brain-based framework for predicting these hidden and persistent psychological states through noninvasive neuroimaging. We used functional near-infrared spectroscopy to record neural activity from 67 executives in the field as they watched a video about workplace attitudes. We then applied a multitimepoint pattern analysis (MTPA) approach to reduce timeseries dimensionality and successfully classify whether individuals were feeling overwhelmed (72.8% accuracy) or in need of a new or different challenge (79.1% accuracy) in their careers using the temporal parietal junction (TPJ) and dorsal medial prefrontal cortex (dmPFC), respectively. The MTPA framework also allowed us to reverse-engineer specific thematic properties of the stimulus that evoked differential neural responses linked to these predicted outcomes. Emotional content in the video (e.g., reported distress) corresponded to the selected TPJ features that predicted whether someone was overwhelmed, while socially relevant content (e.g., missing social gatherings) aligned with the selected features from the dmPFC that predicted the need for a new or different challenge. These findings demonstrate the ability of neural measures to unobtrusively identify hidden and persistent psychological states in real-world settings, enabling targeted interventions that can improve well-being and engagement.

Hyperscanning, the simultaneous recording of multiple brains during social interaction, is an emerging method in social neuroscience. Among available techniques, functional near-infrared spectroscopy (fNIRS) is particularly promising for studies outside laboratory environments. fNIRS hyperscanning presents opportunities and challenges in key domains including data processing, the quantification and interpretation of inter-brain synchrony, and increasing interest in multimodal approaches. To assess the field’s current status and trajectory, we developed a questionnaire for fNIRS hyperscanning researchers, addressing both best practices (“what has been done”) and future priorities (“what ought to be done”). Survey responses were compared with the empirical literature to identify unmet needs, align community perspectives, and chart a course for the field’s progression. Findings showed (i) broad agreement on preprocessing workflows, (ii) widespread use of wavelet transform coherence to quantify inter-brain synchrony, and (iii) growing enthusiasm—and a rapidly expanding pool of datasets—for multimodal recording. Meanwhile, (iv) systematic training in fNIRS hyperscanning is largely absent from university curricula, (v) theoretical frameworks for interpreting neural synchrony remain underdeveloped, and (vi) methodological pipelines integrating multimodal behavioral and neural signals are still lacking. By linking survey insights with existing literature, we highlight solutions and propose concrete steps for advancing fNIRS hyperscanning research.

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.

Common challenges such as feeling overwhelmed, burned out, or disengaged often remain hidden due to fear of judgment or social norms, contributing to rising mental health crises and organizational dysfunction. This study presents a brain-based framework for predicting these hidden and persistent psychological states through noninvasive neuroimaging. We used functional near infrared spectroscopy (fNIRS) to record neural activity from 67 businesspeople in the field as they watched a video about workplace attitudes. We then applied a novel multi-timepoint pattern analysis (MTPA) approach to reduce timeseries dimensionality and successfully classify whether individuals were feeling overwhelmed (72.84% accuracy) or in need of a new or different challenge (79.13% accuracy) in their careers using the TPJ and dmPFC, respectively. The MTPA framework also allowed us to reverse-engineer specific thematic properties of the stimulus that evoked differential neural responses linked to these predicted outcomes. Emotional content in the video (e.g., reported distress) corresponded to the selected TPJ features that predicted whether someone felt overwhelmed, while socially relevant content (e.g., missing social gatherings) aligned with the selected features from the dmPFC that predicted the need for a new or different challenge. These findings demonstrate the ability of neural measures to unobtrusively identify hidden and persistent psychological states in real-world settings, enabling targeted interventions that can improve well-being and engagement.

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 (PCA), 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 analyzing 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.

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.