The ability to represent and reason about other people’s perspectives (‘social cognition’, or ‘Theory of Mind’ [ToM]) plays an important role in daily life. It is not currently clear how different types of interaction may impact subsequent perspective taking, and whether this differs with age. Here, we examine how experiences of social inclusion vs. social exclusion (manipulated using the Cyberball task) influence later perspective-taking efficiency, in younger (19-30 years; N = 100; M = 24.69) and older (65-80 years; N = 100; M = 69.63) participants. As expected, significant age differences were found in perspective-taking abilities, with younger participants out-performing older participants. This was specific to false-belief trials, with no difference in memory trials performance. Counter to hypothesized, no interaction between impact of social experience (inclusion vs. exclusion) and age was found, indicating that both younger and older adults are similarly impacted by social experiences. Implications will be discussed.

The Impact of Social Inclusion vs. Exclusion on Subsequent Social Cognition Abilities in Younger and Older Adults

Collaboration can make collective judgments more accurate than individual judgments, but it also comes with costs in time, effort, and social cohesion. But how do we estimate these costs? In two experiments, we introduce children and adults to two teams in which the teammates disagree about the optimal solution to a novel problem, and ask which team would need more time to reach a consensus decision. We find that all ages expect slower decisions from teams with more people or factions, and expect the number of factions to matter more than the number of people. But only adults expect decisions initially endorsed by a stronger faction to be faster than those endorsed by a weaker faction. Results are discussed in context of children’s reasoning about dominance, and models of time-rational collective decision-making.


Herding cats: children’s intuitive theories of persuasion predict slower collective decisions in larger and more diverse groups, but disregard factional power

Social cognition is shaped by cultural and social-psychological contexts. Depending on the social cues displayed, the cognitive processes that emerge during embodied social interactions can vary. This study explored the influence of gender diverse social cues on interpersonal coordination and decision making during dyadic interaction in virtual reality. Participants embodied either a transgender or cisgender avatar and interacted with a transgender confederate (human-controlled virtual agent). Our results suggest that transgender embodied women and men displayed more interpersonal affiliation via their coordination dynamics with the confederate after an explicit gender identity disclosure. Additionally, we found that cognitive flexibility for ambiguous decisions was influenced by attributions of responsibility made toward the confederate.

Embodied Transgender Interactions: Exploring Dyadic Interpersonal Coordination and Decision Making in Virtual Reality

Interpersonal coordination is a key determinant of successful social interaction but is disrupted for people who experience social anxiety. Effective coordination rests on individuals directing their attention toward others, an effect well-documented in previous literature. Yet, little research has considered the concurrent behaviour of interaction partners. Using a novel virtual reality task, we investigated how partner gaze (i.e., direct vs. averted) influenced the emergence of coordination. The results revealed two novel effects: (i) spontaneous coordination was diminished in the averted (cf. direct) gaze condition; (ii) spontaneous coordination was positively related to symptoms of social anxiety, but only when partner gaze was averted. This latter finding contrasts the extant literature and points to a boundary condition of the social anxiety-coordination association (i.e., social distance). More broadly, this work provides further evidence that emergent patterns of interpersonal coordination fluctuate as a function of changes in social context and the behaviour of others.

Investigating the Influence of Partner Gaze on the Relationship Between Interpersonal Coordination and Social Anxiety

How and why do people share opinions online? In research conducted offline, the social identity of the audience is a key factor: whether they are composed of one’s ingroup or outgroup affects what people share and why. Do people behave similarly and for similar reasons online? To test this, we put participants (N = 326) in imaginary forums belonging to their ingroup and outgroup. In each, people selected statements to share, along with reasons for doing so. The results showed a high degree of heterogeneity; people shared nearly all kinds of statements with both audiences, for a variety of reasons. However, there were also consistent patterns. Identity expression was the most common reason for sharing to both audiences, but this led to different things being shared to each. To the ingroup, people preferred to share statements expressing ingroup beliefs, while to the outgroup, they preferred statements expressing universal beliefs.

Online communication to the ingroup and the outgroup: the role of identity in the “what” and “why” of information sharing

A growing body of literature has revealed ordinary causal judgement to be sensitive to normative factors, such that a norm violating agent is regarded more causal than their non-norm violating counterpart. The present paper aims to tease apart two competing explanations: the Responsibility View and the Bias View. The Bias View, but not the Responsibility View, predicts features peripheral to the agent’s responsibility to impact causal attributions. In a series of three preregistered experiments (N = 1162), we present new evidence that the Norm Effect arises from such peripheral features, namely from nonpertinent or entirely silly norm violations. Further, we show that this effect cannot be explained by recourse to the agent’s foreknowledge or desire of the outcome, nor by its foreseeability: the Norm Effect arises even when participants judge the norm violating agent’s doing as equally foreseeable. This, we argue, provides evidence in favour of the Bias View.


Causation, Foreseeability, and Norms

In two-person asymmetric coordination dilemmas, both people are better off if they coordinate, but one person benefits more than the other. When these interactions repeatedly recur, people can form expectations to avoid failing to coordinate, and to balance who is better off over time. What does it mean when asymmetric social interactions recur, and what can we learn from how people solve these dilemmas? We hypothesize that people expect social interactions to recur when two people are in a social relationship, and that knowing about the symmetry of the social relationship influences the stable solution to asymmetric coordination dilemmas over time. We report two experiments where participants read stories and answered questions about social interactions between two people. In Experiment 1, participants infer that two people are in a social relationship when there is a sequence of altruistic interactions between them, and specifically infer an asymmetric relationship when one person always performs the altruistic action, and a symmetric relationship when the two people alternate performing the altruistic action. In Experiment 2, participants equally expect alternating and repeating altruistic actions when the relationship is symmetric, but expect repeating actions (following a precedent) when the relationship is asymmetric. Our results suggest that people are able to use knowledge of relationships to generate shared expectations for coordinating on recurring altruistic social interactions, and vice versa.

People have systematic expectations linking social relationships to patterns of reciprocal altruism

We conducted two experiments to investigate what triggers sarcasm between close friends and whether the factors prompting sarcastic comments in production are also shared with an external observer. In Experiment 1, participants freely reacted to different types of situations in written form and rated their perception of the given contexts, the level of sarcasm of their responses, and the intentions behind their responses. Results showed that the intentions to say clever things or to mock the addressee in a hilarious or friendly manner triggered a higher number of sarcastic answers. In contrast, the intentions to be direct or be nice to the addressee triggered less sarcastic answers. In Experiment 2, a new group of participants rated the responses collected in Experiment 1 on the same dimensions. Overall, we observed similar patterns in both experiments. However, the intentions to criticize the addressee softly and to say clever things were stronger predictors of sarcasm for the observers than for the producer of the statement.

Intended and Perceived Sarcasm Between Close Friends: What Triggers Sarcasm and What Gets Conveyed?

Collaboration in shared environments requires human agents to coordinate their behaviour according to the machines’ actions. In this study, we compared the performance and behaviour of Human-Machine (HM) and Human-Human (HH) teams. While HH teaming behaviour is sensitive to Collaborative contexts, little is known about HM teaming behaviour. Furthermore, teaming behaviour may impact the team’s Joint Capacity – the team’s ability to handle teamwork processes and task demands. To assess teaming behaviour at every moment of a trial we used three distinct spatiotemporal measures (Momentary Distance, Highly Correlated Segments, and Running Correlation). To assess the team’s joint performance, we adopted the Capacity Coefficient (Townsend & Nozawa, 1995). For both HH and HM teams, behavioural measures predicted Joint Capacity. HH teams demonstrated greater performance and less synchronous behaviour than HM teams. The reduced synchrony of HH teams likely improved their performance as they could complement each other’s behaviour rather than duplicate inefficiencies.

The Relationship Between Teaming Behaviours and Joint Capacity of Hybrid Human-Machine Teams

Over the past few years, artificial intelligence (AI) has achieved great success in a variety of applications, such as image classification and recommendation systems. This suc- cess has often been achieved by training machine learning models on static datasets, where inputs and desired outputs are provided. However, we are now seeing a shift in this paradigm. Instead of learning from static datasets, machine learning models are increasingly being trained through feed- back from their interactions with the world. This is particu- larly important when machine learning models are deployed in the real world, as their decisions can often have an im- pact on other agents, turning the decision-making process into a multi-agent problem. As a result, multi-agent learning in complex environments is a critical area of research for the next generation of AI, particularly in the context of cooper- ative tasks. Cooperative multi-agent learning is an essential problem for practitioners to consider as it has the potential to enable a wide range of multi-agent tasks.
In this presentation, we will review the background and challenges of cooperative multi-agent learning. We will sur- vey our research that aims to address challenges in the fol- lowing areas:
• Scalability and cooperation in multi-agent learning, in- cluding studies on (Han et al. 2019; Du et al. 2019; Xu et al. 2020; Du et al. 2021a; Fang et al. 2019; Mguni et al. 2021). • Evaluating AI agents, including studies on (Yan et al. 2022; Du et al. 2021b).
• Human-in-the-loop interactive learning for robust and trusted decision making, including studies on (Kazantzidis et al. 2022; Gu et al. 2022; Liu et al. 2022).
• Various applications of cooperative multi-agent learning, including (Ruan et al. 2022; Xu et al. 2020; Wang et al. 2021; Chen et al. 2021).
We will also discuss future directions for research in this field.

Cooperative Multi-Agent Learning in a Complex World: Challenges and Solutions

Safe and reliable decision-making is critical for long-term deployment of autonomous systems. Despite the recent advances in artificial intelligence, ensuring safe and reliable operation of human-aligned autonomous systems in open-world environments remains a challenge. My research focuses on developing planning and learning algorithms that support reliable autonomy in fully and partially observable environments, in the presence of uncertainty, limited information, and limited resources. In this talk, I will cover (1) how agents can learn to recognize and plan to avoid the Markovian and non-Markovian negative side effects of their actions, and (2) how explanations can be used to verify and learn human-aligned rewards from demonstrations.

Planning and Learning for Reliable Autonomy in the Open World

Humans perceive surrounding scenes through multiple senses with multisensory integration. For example, hearing helps capture the spatial location of a racing car behind us; seeing peoples' talking faces can strengthen our perception of their speech. However, today's state-of-the-art scene understanding systems are usually designed to rely on a single audio or visual modality. Ignoring multisensory cooperation has become one of the key bottlenecks in creating intelligent systems with human-level perception capability, which impedes the real-world applications of existing scene understanding models. To address this limitation, my research has pioneered marrying computer vision with computer audition to create multimodal systems that can learn to understand audio and visual data. In particular, my current research focuses on asking and solving fundamental problems in a fresh research area: audio-visual scene understanding and strives to develop unified, explainable, and robust multisensory perception machines. The three themes are distinct yet interconnected, and all of them are essential for designing powerful and trustworthy perception systems. In my talk, I will give a brief overview about this new research area and then introduce our works in the research thrusts.

Underline digital video library

The Impact of Social Inclusion vs. Exclusion on Subsequent Social Cognition Abilities in Younger and Older Adults

Herding cats: children’s intuitive theories of persuasion predict slower collective decisions in larger and more diverse groups, but disregard factional power

Embodied Transgender Interactions: Exploring Dyadic Interpersonal Coordination and Decision Making in Virtual Reality

Investigating the Influence of Partner Gaze on the Relationship Between Interpersonal Coordination and Social Anxiety

Online communication to the ingroup and the outgroup: the role of identity in the “what” and “why” of information sharing

Causation, Foreseeability, and Norms

People have systematic expectations linking social relationships to patterns of reciprocal altruism

Intended and Perceived Sarcasm Between Close Friends: What Triggers Sarcasm and What Gets Conveyed?

The Relationship Between Teaming Behaviours and Joint Capacity of Hybrid Human-Machine Teams

Cooperative Multi-Agent Learning in a Complex World: Challenges and Solutions

Planning and Learning for Reliable Autonomy in the Open World

Audio-Visual Scene UnderstandingTowards Unified and Robust Multisensory Perception

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