Learning Desire Is Predicted by Similar Neural Processing of Naturalistic Educational Materials

A Brain-To-Brain Mechanism for Social Transmission of Threat Learning

Survival and adaptation in environments require swift and efficacious learning about what is dangerous. Across species, much of such threat learning is acquired socially, e.g., through the observation of others' ("demonstrators'") defensive behaviors. However, the specific neural mechanisms responsible for the integration of information shared between demonstrators and observers remain largely unknown. This dearth of knowledge is addressed by performing magnetoencephalography (MEG) neuroimaging in demonstrator-observer dyads. A set of stimuli are first shown to a demonstrator whose defensive responses are filmed and later presented to an observer, while neuronal activity is recorded sequentially from both individuals who never interacted directly. These results show that brain-to-brain coupling (BtBC) in the fronto-limbic circuit (including insula, ventromedial, and dorsolateral prefrontal cortex) within demonstrator-observer dyads predict subsequent expressions of learning in the observer. Importantly, the predictive power of BtBC magnifies when a threat is imminent to the demonstrator. Furthermore, BtBC depends on how observers perceive their social status relative to the demonstrator, likely driven by shared attention and emotion, as bolstered by dyadic pupillary coupling. Taken together, this study describes a brain-to-brain mechanism for social threat learning, involving BtBC, which reflects social relationships and predicts adaptive, learned behaviors.

Spatiotemporal consistency of neural responses to repeatedly presented video stimuli accounts for population preferences

Population preferences for video advertisements vary across short video clips. What underlies these differences? Repeatedly watching a video clip may produce a consistent spatiotemporal pattern of neural activity that is dependent on the individual and the stimulus. Moreover, such consistency may be associated with the degree of engagement and memory of individual viewers. Since the population preferences are associated with the engagement and memory of the individual viewers, the consistency observed in a smaller group of viewers can be a predictor of population preferences. To test the hypothesis, we measured the degree of inter-trial consistency in participants' electroencephalographic (EEG) responses to repeatedly presented television commercials. We observed consistency in the neural activity patterns across repetitive views and found that the similarity in the spatiotemporal patterns of neural responses while viewing popular television commercials predicts population preferences obtained from a large audience. Moreover, a regression model that used two datasets, including two separate groups of participants viewing different stimulus sets, showed good predictive performance in a leave-one-out cross-validation. These findings suggest that universal spatiotemporal patterns in EEG responses can account for population-level human behaviours.

Interpersonal Neural Synchronization Predicting Learning Outcomes From Teaching-Learning Interaction: A Meta-Analysis

In school education, teaching-learning interaction is deemed as a core process in the classroom. The fundamental neural basis underlying teaching-learning interaction is proposed to be essential for tuning learning outcomes. However, the neural basis of this process as well as the relationship between the neural dynamics and the learning outcomes are largely unclear. With non-invasive technologies such as fNIRS (functional near-infrared spectroscopy), hyperscanning techniques have been developed since the last decade and been applied to the field of educational neuroscience for simultaneous multi-brain scanning. Hyperscanning studies suggest that the interpersonal neural synchronization (INS) during teaching-learning interaction might be an ideal neural biomarker for predicting learning outcomes. To systematically evaluate such a relationship, this meta-analysis ran on a random-effects model on 16 studies with 23 independent samples (effect sizes). Further moderator analyses were also performed to examine the potential influences of the style, mode, content, and the assessment method of learning outcomes. The random-effects modeling results confirmed a robust positive correlation between INS and learning outcomes. Subsequent analyses revealed that such relationship was mainly affected by both interaction style and mode. Therefore, the present meta-analysis provided a confirmatory neurocognitive foundation for teaching-learning interaction, as well as its relation to the learning outcomes, consolidated future learning and teaching studies in various disciplines including second language education with a firm methodological reference.

The Interpersonal Neuroscience of Social Learning

The study of the brain mechanisms underpinning social behavior is currently undergoing a paradigm shift, moving its focus from single individuals to the real-time interaction among groups of individuals. Although this development opens unprecedented opportunities to study how interpersonal brain activity shapes behaviors through learning, there have been few direct connections to the rich field of learning science. Our article examines how the rapidly developing field of interpersonal neuroscience is (and could be) contributing to our understanding of social learning. To this end, we first review recent research extracting indices of brain-to-brain coupling (BtBC) in the context of social behaviors and, in particular, social learning. We then discuss how studying communicative behaviors during learning can aid the interpretation of BtBC and how studying BtBC can inform our understanding of such behaviors. We then discuss how BtBC and communicative behaviors collectively can predict learning outcomes, and we suggest several causative and mechanistic models. Finally, we highlight key methodological and interpretational challenges as well as exciting opportunities for integrating research in interpersonal neuroscience with social learning, and we propose a multiperson framework for understanding how interpersonal transmission of information between individual brains shapes social learning.

Neural alignment predicts learning outcomes in students taking an introduction to computer science course

Despite major advances in measuring human brain activity during and after educational experiences, it is unclear how learners internalize new content, especially in real-life and online settings. In this work, we introduce a neural approach to predicting and assessing learning outcomes in a real-life setting. Our approach hinges on the idea that successful learning involves forming the right set of neural representations, which are captured in canonical activity patterns shared across individuals. Specifically, we hypothesized that learning is mirrored in neural alignment: the degree to which an individual learner's neural representations match those of experts, as well as those of other learners. We tested this hypothesis in a longitudinal functional MRI study that regularly scanned college students enrolled in an introduction to computer science course. We additionally scanned graduate student experts in computer science. We show that alignment among students successfully predicts overall performance in a final exam. Furthermore, within individual students, we find better learning outcomes for concepts that evoke better alignment with experts and with other students, revealing neural patterns associated with specific learned concepts in individuals.