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Despite the recent increase in second-person neuroscience research, it is still hard to understand which neurocognitive mechanisms underlie real-time social behaviours. Here, we propose that social signalling can help us understand social interactions both at the single- and two-brain level in terms of social signal exchanges between senders and receivers. First, we show how subtle manipulations of being watched provide an important tool to dissect meaningful social signals. We then focus on how social signalling can help us build testable hypotheses for second-person neuroscience with the example of imitation and gaze behaviour. Finally, we suggest that linking neural activity to specific social signals will be key to fully understand the neurocognitive systems engaged during face-to-face interactions.

Learning in humans is highly embedded in social interaction: since the very early stages of our lives, we form memories and acquire knowledge about the world from and with others. Yet, within cognitive science and neuroscience, human learning is mainly studied in isolation. The focus of past research in learning has been either exclusively on the learner or (less often) on the teacher, with the primary aim of determining developmental trajectories and/or effective teaching techniques. In fact, social interaction has rarely been explicitly taken as a variable of interest, despite being the medium through which learning occurs, especially in development, but also in adulthood. Here, we review behavioural and neuroimaging research on social human learning, specifically focusing on cognitive models of how we acquire semantic knowledge from and with others, and include both developmental as well as adult work. We then identify potential cognitive mechanisms that support social learning, and their neural correlates. The aim is to outline key new directions for experiments investigating how knowledge is acquired in its ecological niche, i.e. socially, within the framework of the two-person neuroscience approach. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.

Learning in humans is highly embedded in social interaction: since the very early stages of our lives, we form memories and acquire knowledge about the world from and with others. Yet, within cognitive science and neuroscience, human learning is mainly studied in isolation. The focus of past research in learning has been either exclusively on the learner or (less often) on the teacher, with the primary aim of determining developmental trajectories and/or effective teaching techniques. In fact, social interaction has rarely been explicitly taken as a variable of interest, despite being the medium through which learning occurs, especially in development, but also in adulthood. Here, we review behavioural and neuroimaging research on social human learning, specifically focusing on cognitive models of how we acquire semantic knowledge from and with others, and include both developmental as well as adult work. We then identify potential cognitive mechanisms that support social learning, and their neural correlates. The aim is to outline key new directions for experiments investigating how knowledge is acquired in its ecological niche, i.e. socially, within the framework of the two-person neuroscience approach. This article is part of the theme issue ‘Concepts in interaction: social engagement and inner experiences’.

Previous studies (Haswell et al. in Nat Neurosci 12:970–972, 2009; Marko et al. in Brain J Neurol 138:784–797, 2015) reported that people with autism rely less on vision for learning to reach in a force field. This suggested a possibility that they have difficulties in extracting force information from visual motion signals, a process called inverse dynamics computation. Our recent study (Takamuku et al. in J Int Soc Autism Res 11:1062–1075, 2018) examined the ability of inverse computation with two perceptual tasks and found similar performances in typical and autistic adults. However, this tested the computation only in the context of sensory perception while it was possible that the suspected disability is specific to the motor domain. Here, in order to address the concern, we tested the use of inverse dynamics computation in the context of motor control by measuring changes in grip timing caused by seeing/not seeing a controlled object. The motion of the object was informative of its inertial force and typical participants improved their grip timing based on the visual feedback. Our interest was on whether the autism participants show the same improvement. While some autism participants showed atypical hand slowing when seeing the controlled object, we found no evidence of abnormalities in the inverse computation in our grip timing task or in a replication of the perceptual task. This suggests that the ability of inverse dynamics computation is preserved not only for sensory perception but also for motor control in adults with autism.

Hyperscanning-the recording of brain activity from multiple individuals-can be hard to interpret. This paper shows how integrating behavioral data and mutual prediction models into hyperscanning studies can lead to advances in embodied social neuroscience.

Anterior prefrontal cortex (PFC, Brodmann area 10) activations are often, but not always, found in neuroimaging studies investigating deception, and the precise role of this area remains unclear. To explore the role of the PFC in face-to-face deception, we invited pairs of participants to play a card game involving lying and lie detection while we used functional near infrared spectroscopy (fNIRS) to record brain activity in the PFC. Participants could win points for successfully lying about the value of their cards or for detecting lies. We contrasted patterns of brain activation when the participants either told the truth or lied, when they were either forced into this or did so voluntarily and when they either succeeded or failed to detect a lie. Activation in the anterior PFC was found in both lie production and detection, unrelated to reward. Analysis of cross-brain activation patterns between participants identified areas of the PFC where the lead player’s brain activity synchronized their partner’s later brain activity. These results suggest that during situations that involve close interpersonal interaction, the anterior PFC supports processing widely involved in deception, possibly relating to the demands of monitoring one’s own and other people’s behaviour.

Conversation between two people involves subtle nonverbal coordination in addition to speech. However, the precise parameters and timing of this coordination remain unclear, which limits our ability to theorize about the neural and cognitive mechanisms of social coordination. In particular, it is unclear if conversation is dominated by synchronization (with no time lag), rapid and reactive mimicry (with lags under 1 s) or traditionally observed mimicry (with several seconds lag), each of which demands a different neural mechanism. Here we describe data from high-resolution motion capture of the head movements of pairs of participants (n = 31 dyads) engaged in structured conversations. In a pre-registered analysis pathway, we calculated the wavelet coherence of head motion within dyads as a measure of their nonverbal coordination and report two novel results. First, low-frequency coherence (0.2–1.1 Hz) is consistent with traditional observations of mimicry, and modeling shows this behavior is generated by a mechanism with a constant 600 ms lag between leader and follower. This is in line with rapid reactive (rather than predictive or memory-driven) models of mimicry behavior, and could be implemented in mirror neuron systems. Second, we find an unexpected pattern of lower-than-chance coherence between participants, or hypo-coherence, at high frequencies (2.6–6.5 Hz). Exploratory analyses show that this systematic decoupling is driven by fast nodding from the listening member of the dyad, and may be a newly identified social signal. These results provide a step towards the quantification of real-world human behavior in high resolution and provide new insights into the mechanisms of social coordination. Electronic supplementary material The online version of this article (10.1007/s10919-019-00320-3) contains supplementary material, which is available to authorized users.

Humans frequently imitate each other's actions with high fidelity, and different reasons have been proposed for why they do so. Here we test the hypothesis that imitation can act as a social signal, with imitation occurring with greater fidelity when a participant is being watched. In a preregistered study, 30 pairs of naïve participants played an augmented-reality game involving moving blocks in space. We compared imitation fidelity between trials where the leader watched the followers' action, and trials where the leader did not watch. Followers imitated the trajectory height demonstrated by the leader, and critically, the strength of this correlation was greater in trials where the follower knew the leader was watching them. This suggests that followers spontaneously used imitation as a social signal in a nonverbal interaction task, supporting socio-communicative hypotheses of imitation.

Summary Human learning is highly social. 1 , 2 , 3 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 4 , 5 , 6 , 7 , 8 and inconclusive results on adults. 9 , 10 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 11 and/or distracting, 12 , 13 , 14 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, 15 common ground, 16 or (inter-)active discussion, and as such, interactive learning benefits from rich social settings. 17 , 18

Pairs of participants mutually communicated (or not) biographical information to each other. By combining simultaneous eye-tracking, face-tracking and functional near-infrared spectroscopy, we examined how this mutual sharing of information modulates social signalling and brain activity. When biographical information was disclosed, participants directed more eye gaze to the face of the partner and presented more facial displays. We also found that spontaneous production and observation of facial displays was associated with activity in the left SMG and right dlPFC/IFG, respectively. Moreover, mutual information-sharing increased activity in bilateral TPJ and left dlPFC, as well as cross-brain synchrony between right TPJ and left dlPFC. This suggests that a complex long-range mechanism is recruited during information-sharing. These multimodal findings support the second-person neuroscience hypothesis, which postulates that communicative interactions activate additional neurocognitive mechanisms to those engaged in non-interactive situations. They further advance our understanding of which neurocognitive mechanisms underlie communicative interactions.

Communication with others relies on coordinated exchanges of social signals, such as eye gaze and facial displays. However, this can only happen when partners are able to see each other. Although previous studies report that autistic individuals have difficulties in planning eye gaze and making facial displays during conversation, evidence from real-life dyadic tasks is scarce and mixed. Across two studies, here we investigate how eye gaze and facial displays of typical and high-functioning autistic individuals are modulated by the belief in being seen and potential to show true gaze direction. Participants were recorded with an eye-tracking and video-camera system while they completed a structured Q&A task with a confederate under three social contexts: pre-recorded video, video-call and face-to-face. Typical participants gazed less to the confederate and produced more facial displays when they were being watched and when they were speaking. Contrary to our hypotheses, eye gaze and facial motion patterns in autistic participants were overall similar to the typical group. This suggests that high-functioning autistic participants are able to use eye gaze and facial displays as social signals. Future studies will need to investigate to what extent this reflects spontaneous behaviour or the use of compensation strategies. Lay abstract When we are communicating with other people, we exchange a variety of social signals through eye gaze and facial expressions. However, coordinated exchanges of these social signals can only happen when people involved in the interaction are able to see each other. Although previous studies report that autistic individuals have difficulties in using eye gaze and facial expressions during social interactions, evidence from tasks that involve real face-to-face conversations is scarce and mixed. Here, we investigate how eye gaze and facial expressions of typical and high-functioning autistic individuals are modulated by the belief in being seen by another person, and by being in a face-to-face interaction. Participants were recorded with an eye-tracking and video-camera system while they completed a structured Q&A task with a confederate under three social contexts: pre-recorded video (no belief in being seen, no face-to-face), video-call (belief in being seen, no face-to-face) and face-to-face (belief in being seen and face-to-face). Typical participants gazed less to the confederate and made more facial expressions when they were being watched and when they were speaking. Contrary to our hypotheses, eye gaze and facial expression patterns in autistic participants were overall similar to the typical group. This suggests that high-functioning autistic participants are able to use eye gaze and facial expressions as social signals. Future studies will need to investigate to what extent this reflects spontaneous behaviour or the use of compensation strategies.

The INTERSPEECH 2020 Computational Paralinguistics Challenge addresses three different problems for the first time in a research competition under well-defined conditions: In the Elderly Emotion Sub-Challenge, arousal and valence in the speech of elderly individuals have to be modelled as a 3-class problem; in the Breathing Sub-Challenge, breathing has to be assessed as a regression problem; and in the Mask Sub-Challenge, speech without and with a surgical mask has to be told apart. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the ‘usual’ COMPARE and BoAW features as well as deep unsupervised representation learning using the AUDEEP toolkit, and deep feature extraction from pre-trained CNNs using the DEEP SPECTRUM toolkit; in addition, we partially add deep end-to-end sequential modelling, and, for the first time in the challenge, linguistic analysis