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Abstract 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 direct 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 Hyperscanning Python Pipeline, a comprehensive and easy open-source software package that allows (social) neuroscientists to carry-out and to interpret inter-brain connectivity analyses. 

Cognitive science research on learning and instruction is often not directly connected to discipline-based research. In an effort to narrow this gap, this essay integrates research from both fields on five learning and instruction strategies: active retrieval, distributed (spaced) learning, dual coding, concrete examples, and feedback and assessment. These strategies can significantly enhance the effectiveness of science instruction, but they typically do not find their way into the undergraduate classroom. The implementation of these strategies is illustrated through an undergraduate science course for nonmajors called Science in Our Lives. This course provides students with opportunities to use scientific information to solve real-world problems and view science as part of everyday life.