Social interaction is a catalyst for adult human learning in online contexts

Human learning is highly social. Advances in technology have increasingly moved learning online, and the recent coronavirus disease 2019 (COVID-19) pandemic has accelerated this trend. Online learning can vary in terms of how “socially” the material is presented (e.g., live or recorded), but there are limited data on which is most effective, with the majority of studies conducted on children and inconclusive results on adults. Here, we examine how young adults (aged 18–35) learn information about unknown objects, systematically varying the social contingency (live versus recorded lecture) and social richness (viewing the teacher’s face, hands, or slides) of the learning episodes. Recall was tested immediately and after 1 week. Experiment 1 (n = 24) showed better learning for live presentation and a full view of the teacher (hands and face). Experiment 2 (n = 27; pre-registered) replicated the live-presentation advantage. Both experiments showed an interaction between social contingency and social richness: the presence of social cues affected learning differently depending on whether teaching was interactive or not. Live social interaction with a full view of the teacher’s face provided the optimal setting for learning new factual information. However, during observational learning, social cues may be more cognitively demanding and/or distracting,resulting in less learning from rich social information if there is no interactivity. We suggest that being part of a genuine social interaction catalyzes learning, possibly via mechanisms of joint attention, common ground, or (inter-)active discussion, and as such, interactive learning benefits from rich social setting

Autistic adults benefit from and enjoy learning via social interaction as much as neurotypical adults do

Background: Autistic people show poor processing of social signals (i.e. about the social world). But how do they learn via social interaction? // Methods: 68 neurotypical adults and 60 autistic adults learned about obscure items (e.g. exotic animals) over Zoom (i) in a live video-call with the teacher, (ii) from a recorded learner-teacher interaction video and (iii) from a recorded teacher-alone video. Data were analysed via analysis of variance and multi-level regression models. // Results: Live teaching provided the most optimal learning condition, with no difference between groups. Enjoyment was the strongest predictor of learning: both groups enjoyed the live interaction significantly more than other condition and reported similar anxiety levels across conditions. // Limitations: Some of the autistic participants were self-diagnosed—however, further analysis where these participants were excluded showed the same results. Recruiting participants over online platforms may have introduced bias in our sample. Future work should investigate learning in social contexts via diverse sources (e.g. schools). // Conclusions: These findings advocate for a distinction between learning about the social versus learning via the social: cognitive models of autism should be revisited to consider social interaction not just as a puzzle to decode but rather a medium through which people, including neuro-diverse groups, learn about the world around them. // Trial registration: Part of this work has been pre-registered before data collection https://doi.org/10.17605/OSF.IO/5PGA

Integrating Common Ground and Informativeness in Pragmatic Word Learning

Pragmatic inferences are an integral part of language learn-ing and comprehension. To recover the intended meaning ofan utterance, listeners need to balance and integrate differentsources of contextual information. In a series of experiments,we studied how listeners integrate general expectations aboutspeakers with expectations specific to their interactional his-tory with a particular speaker. We used a Bayesian pragmaticsmodel to formalize the integration process. In Experiments1 and 2, we replicated previous findings showing that listenersmake inferences based on speaker-general and speaker-specificexpectations. We then used the empirical measurements fromthese experiments to generate model predictions about howthe two kinds of expectations should be integrated, which wetested in Experiment 3. Experiment 4 replicated and extendedExperiment 3 to a broader set of conditions. In both experi-ments, listeners based their inferences on both types of expec-tations. We found that model performance was also consistentwith this finding; with better fit for a model which incorporatedboth general and specific information compared to baselinesincorporating only one type. Listeners flexibly integrate dif-ferent forms of social expectations across a range of contexts,a process which can be described using Bayesian models ofpragmatic reasoning

Neural and Cognitive Mechanisms of Real-World Interaction during Adult Learning

The goal of this thesis is to understand what makes a social interaction successful, and whether it supports learning of conceptual knowledge. Crucially, it distinguishes learning via the social from learning about the social, and asks the question of how social interaction supports declarative processing of non-social material. In doing so, it priorities ecological validity: all experiments involve relatively unconstrained teacher-learner interaction, and learning material resembled documentary-like content. The first half of the thesis shows a series of studies on how adults learn in online contexts (Study 1 and 2): Study 1 presents two online experiments, where social contingency (i.e. being part of a live interaction vs observing a pre-recorded one) and social cues (i.e. teacher’s webcam on vs off vs showing a slide only) were manipulated. Results showed that learning in live interaction was associated with the best performance, and live social interaction with a full view of the teacher provided the optimal setting for learning, while seeing a slide had greater benefit during recorded sessions specifically. Study 2 replicates the live-learning advantage across three experiments and a large sample of adults with Autistic Spectrum Condition (ASC). The second half of this thesis (Study 3 and 4) investigates face-to-face interaction, using functional Near-Infrared Spectroscopy (fNIRS) hyperscanning and wavelet transform coherence (WTC) analysis, to measure brain synchrony in naturalistic interactions. Study 3 tests the hypothesis that being in the same room and engaging in conversation affects people’s brain response to later novel stimuli. Study 4 asks whether teacher-student brain synchrony can be a marker of learning success and, if so, how it is modulated by social behaviours. Findings reveal a complex dynamic between neural responses and behavioural metrics, in particular mutual gaze and joint attention. Results are discussed in the frame of the mutual-prediction hypothesis, and advocate for a multi-modal investigation of social learning to fully understand its underlying cognitive mechanisms. Overall, this work advances the current understanding of naturalistic social interaction and has theoretical implications for cognitive models of information exchange and mutual prediction, as well as practical significance for educational policies. The novel multi-modal and highly ecological approach used in this thesis makes this work an important example for real-world second person social neuroscience

Integrating common ground and informativeness in pragmatic word learning

Pragmatic inferences are an integral part of language learn-ing and comprehension. To recover the intended meaning ofan utterance, listeners need to balance and integrate differentsources of contextual information. In a series of experiments,we studied how listeners integrate general expectations aboutspeakers with expectations specific to their interactional his-tory with a particular speaker. We used a Bayesian pragmaticsmodel to formalize the integration process. In Experiments1 and 2, we replicated previous findings showing that listenersmake inferences based on speaker-general and speaker-specificexpectations. We then used the empirical measurements fromthese experiments to generate model predictions about howthe two kinds of expectations should be integrated, which wetested in Experiment 3. Experiment 4 replicated and extendedExperiment 3 to a broader set of conditions. In both experi-ments, listeners based their inferences on both types of expec-tations. We found that model performance was also consistentwith this finding; with better fit for a model which incorporatedboth general and specific information compared to baselinesincorporating only one type. Listeners flexibly integrate dif-ferent forms of social expectations across a range of contexts,a process which can be described using Bayesian models ofpragmatic reasoning