The bulk of social neuroscience takes a ‘stimulus-brain’ approach, typically comparing brain responses to different types of social stimuli, but most of the time in the absence of true social interaction. Over the last two decades, a growing number of researchers have adopted a ‘brain-to-brain’ approach, exploring similarities between brain patterns across participants as a novel way to gain insight into the social brain. This methodological shift has facilitated the introduction of naturalistic social stimuli into the study design (e.g., movies), and, crucially, has spurred the development of new tools to directly study social interaction, both in controlled experimental settings and in more ecologically valid environments. Specifically, hyperscanning setups, which allow the simultaneous recording of brain activity from two or more individuals during social tasks has gained popularity in recent years. However, currently there is no agreed-upon approach to carry out such inter-brain connectivity analysis, resulting in a scattered landscape of analysis techniques. To accommodate a growing demand to standardize analysis approaches in this fast-growing research field, we have developed HyPyP, a comprehensive and easy open-source software package that allows (social) neuroscientists to carry-out and to interpret inter-brain connectivity analyses.

In recent years, the benefits of practicing mindfulness have raised much public and academic interest. Mindfulness emphasizes cultivating awareness of our immediate experience, and has been associated with compassion, empathy and various other prosocial traits. However, experimental evidence pertaining to its prosocial benefits in social settings is lacking. In this study, we investigate neural correlates of trait mindfulness during naturalistic dyadic interactions, using both individual brain and inter-brain coupling measures. We used the Muse headset, a portable electroencephalogram (EEG) device, to record participants' brain activity during a ~10 minutes' naturalistic dyadic interaction (N = 62) in an interactive art setting. They further completed the Mindful Attention Awareness Scale (MAAS) and the Interpersonal Reactivity Index (IRI). This allowed us to ask whether inter-brain coupling during naturalistic interactions can be predicted by dyads' affective traits and trait mindfulness, respectively. First, we failed replicating prior laboratory-based findings with respect to individual brain responses as they relate to mindfulness. Trait mindfulness did, however, predict inter-brain coupling within dyads, in theta (~5-8 Hz, p < 0.001) and beta frequencies (~26-27Hz, p < 0.001). Finally, we found a negative correlation between personal distress and trait mindfulness (t(475) = -5.493, p < 0.001). These findings underscore the importance of conducting social neuroscience research in ecological settings and enrich our understanding of multi-brain neural correlates of mindfulness during social interaction, while raising critical practical considerations regarding the viability of commercially available EEG systems.

We created a dataset of 108 English novel noun-noun compounds (NNCs, e.g., “doctor charity”) by combining nouns with higher and lower agentivity (i.e., the probability of being an agent in a sentence). We collected active and passive interpretations of NNCs from a group of 59 English native speakers. We then measured interpretation time differences between NNCs with active and passive interpretations, using data obtained from a group of 68 English native speakers. Data was collected online using crowdsourcing platforms (SONA and Prolific). The datasets are available at osf.io/gvc2w/ and can be used to address questions about semantic and syntactic composition.

Prior knowledge has long been known to influence retention of newly experienced information. In particular, known semantic associations across items facilitate subsequent memory for these items, and this effect has been shown to increase with measures of semantic relatedness. In the field of categories and concepts, the processing of taxonomic (e.g., cup-fork, dog-bird) versus thematic (e.g., cup-drink, dog-leash) conceptual relations can be differentiated at the behavioral and neural levels. However, the effects of these distinct conceptual relations on memory remain unresolved. The current study used a stimulus set consisting of thematic, taxonomic, and unrelated noun-noun word pairs, to shed light on this issue. Our results indicate that pairs with thematic relations lead to improved cued memory performance, followed by taxonomic relations, and finally unrelated pairs. This study provides evidence that conceptual relations differ in the extent to which they facilitate cued memory performance.

The interpretation of novel noun-noun compounds (NNCs, e.g., “devil salary”) requires the combination of nouns in the absence of syntactic cues, an interesting facet of complex meaning creation. Here we examine unconstrained interpretations of a large set of novel NNCs, to investigate how NNC constituents are combined into novel complex meanings. The data show that words’ lexical-semantic features (e.g., material, agentivity, imageability, semantic similarity) differentially contribute to the syntactic structure and the semantics of NNC interpretations. Further, we demonstrate that passive interpretations incur higher processing cost (longer interpretation times and more eye-movements) than active interpretations. Finally, we show that large language models (GPT-2, BERT, RoBERTa) can predict whether a NNC is interpretable by human participants and estimate differences in processing cost, but do not exhibit sensitivity to more subtle syntactic differences. The experiments illuminate how humans can use lexical-semantic features to interpret NNCs in the absence of explicit syntactic information.