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1. Coupled dynamics of behaviour and disease contagion among antagonistic groups

   https://doi.org/10.1017/ehs.2021.22  

   Smaldino, Paul E.; Jones, James Holland (January 2021, Evolutionary Human Sciences)

   null (Ed.)

   Abstract Disease transmission and behaviour change are both fundamentally social phenomena. Behaviour change can have profound consequences for disease transmission, and epidemic conditions can favour the more rapid adoption of behavioural innovations. We analyse a simple model of coupled behaviour change and infection in a structured population characterised by homophily and outgroup aversion. Outgroup aversion slows the rate of adoption and can lead to lower rates of adoption in the later-adopting group or even behavioural divergence between groups when outgroup aversion exceeds positive ingroup influence. When disease dynamics are coupled to the behaviour-adoption model, a wide variety of outcomes are possible. Homophily can either increase or decrease the final size of the epidemic depending on its relative strength in the two groups and on R 0 for the infection. For example, if the first group is homophilous and the second is not, the second group will have a larger epidemic. Homophily and outgroup aversion can also produce dynamics suggestive of a ‘second wave’ in the first group that follows the peak of the epidemic in the second group. Our simple model reveals dynamics that are suggestive of the processes currently observed under pandemic conditions in culturally and/or politically polarised populations such as the USA. 

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2. The Physics of Sensing and Decision-Making by Animal Groups

   https://doi.org/10.1146/annurev-biophys-061824-110733  

   Chase, Danielle L; Peleg, Orit (May 2025, Annual Review of Biophysics)

   To ensure survival and reproduction, individual animals navigating the world must regularly sense their surroundings and use this information for important decision-making. The same is true for animals living in groups, where the roles of sensing, information propagation, and decision-making are distributed on the basis of individual knowledge, spatial position within the group, and more. This review highlights key examples of temporal and spatiotemporal dynamics in animal group decision-making, emphasizing strong connections between mathematical models and experimental observations. We start with models of temporal dynamics, such as reaching consensus and the time dynamics of excitation-inhibition networks. For spatiotemporal dynamics in sparse groups, we explore the propagation of information and synchronization of movement in animal groups with models of self-propelled particles, where interactions are typically parameterized by length and timescales. In dense groups, we examine crowding effects using a soft condensed matter approach, where interactions are parameterized by physical potentials and forces. While focusing on invertebrates, we also demonstrate the applicability of these results to a wide range of organisms, aiming to provide an overview of group behavior dynamics and identify new areas for exploration. 

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3. Social divisions and risk perception drive divergent epidemics and large later waves

   https://doi.org/10.1017/ehs.2023.2  

   Harris, Mallory J.; Cardenas, Kimberly J.; Mordecai, Erin A. (January 2023, Evolutionary Human Sciences)

   Abstract During infectious disease outbreaks, individuals may adopt protective measures like vaccination and physical distancing in response to awareness of disease burden. Prior work showed how feedbacks between epidemic intensity and awareness-based behaviour shape disease dynamics. These models often overlook social divisions, where population subgroups may be disproportionately impacted by a disease and more responsive to the effects of disease within their group. We develop a compartmental model of disease transmission and awareness-based protective behaviour in a population split into two groups to explore the impacts of awareness separation (relatively greater in- vs. out-group awareness of epidemic severity) and mixing separation (relatively greater in- vs. out-group contact rates). Using simulations, we show that groups that are more separated in awareness have smaller differences in mortality. Fatigue (i.e. abandonment of protective measures over time) can drive additional infection waves that can even exceed the size of the initial wave, particularly if uniform awareness drives early protection in one group, leaving that group largely susceptible to future infection. Counterintuitively, vaccine or infection-acquired immunity that is more protective against transmission and mortality may indirectly lead to more infections by reducing perceived risk of infection and therefore vaccine uptake. Awareness-based protective behaviour, including awareness separation, can fundamentally alter disease dynamics. Social media summary: Depending on group division, behaviour based on perceived risk can change epidemic dynamics & produce large later waves. 

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4. The unobtrusive group interaction (UGI) corpus

   https://doi.org/10.1145/3304109.3325816  

   Bhattacharya, Indrani; Foley, Michael; Ku, Christine; Zhang, Ni; Zhang, Tongtao; Mine, Cameron; Li, Manling; Ji, Heng; Riedl, Christoph; Welles, Brooke Foucault; et al (June 2019, Proceedings of the 10th ACM Multimedia Systems Conference)

   Studying group dynamics requires fine-grained spatial and temporal understanding of human behavior. Social psychologists studying human interaction patterns in face-to-face group meetings often find themselves struggling with huge volumes of data that require many hours of tedious manual coding. There are only a few publicly available multi-modal datasets of face-to-face group meetings that enable the development of automated methods to study verbal and non-verbal human behavior. In this paper, we present a new, publicly available multi-modal dataset for group dynamics study that differs from previous datasets in its use of ceiling-mounted, unobtrusive depth sensors. These can be used for fine-grained analysis of head and body pose and gestures, without any concerns about participants' privacy or inhibited behavior. The dataset is complemented by synchronized and time-stamped meeting transcripts that allow analysis of spoken content. The dataset comprises 22 group meetings in which participants perform a standard collaborative group task designed to measure leadership and productivity. Participants' post-task questionnaires, including demographic information, are also provided as part of the dataset. We show the utility of the dataset in analyzing perceived leadership, contribution, and performance, by presenting results of multi-modal analysis using our sensor-fusion algorithms designed to automatically understand audio-visual interactions. 

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5. A Multimodal-Sensor-Enabled Room for Unobtrusive Group Meeting Analysis

   https://doi.org/10.1145/3242969.3243022  

   Bhattacharya, Indrani; Foley, Michael; Zhang, Ni; Zhang, Tongtao; Ku, Christine; Mine, Cameron; Ji, Heng; Riedl, Christoph; Welles, Brooke Foucault; Radke, Richard J. (October 2018, Proceedings of the 20th ACM International Conference on Multimodal Interaction)

   Group meetings can suffer from serious problems that undermine performance, including bias, "groupthink", fear of speaking, and unfocused discussion. To better understand these issues, propose interventions, and thus improve team performance, we need to study human dynamics in group meetings. However, this process currently heavily depends on manual coding and video cameras. Manual coding is tedious, inaccurate, and subjective, while active video cameras can affect the natural behavior of meeting participants. Here, we present a smart meeting room that combines microphones and unobtrusive ceiling-mounted Time-of-Flight (ToF) sensors to understand group dynamics in team meetings. We automatically process the multimodal sensor outputs with signal, image, and natural language processing algorithms to estimate participant head pose, visual focus of attention (VFOA), non-verbal speech patterns, and discussion content. We derive metrics from these automatic estimates and correlate them with user-reported rankings of emergent group leaders and major contributors to produce accurate predictors. We validate our algorithms and report results on a new dataset of lunar survival tasks of 36 individuals across 10 groups collected in the multimodal-sensor-enabled smart room. 

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