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Human collaboration often involves a decision to pursue one out of multiple comparable goals, in which case it is challenging to remain committed to the same goal collectively. Philosophical theories as well as empirical evidence from developmental psychology suggest that humans, having shared intentionality as an underlying cognitive structure, may be able to form joint commitment in pursuing a collective goal without communication. By conducting experiments in a real-time cooperative hunting game that heavily relies on visual perception, we demonstrated that humans established and maintained robust cooperation with high-quality hunting, even with a large number of potential targets. Additionally, we showed that a Bayesian imagined “We” (IW) model within a joint commitment framework, could capture humans’ robustness in resisting alternative targets with relatively high quality of hunting. This poses a contrast with a Reward Sharing (RS) model that, despite performing proficiently in pursuing a single goal, mostly exhibited low-quality hunting and whose teaming fell apart as available targets increased. In a hybrid team simulation experiment, the IW model could better mimic the intentions of human hunters compared to the RS model. Together, the success of the persevered group commitment in humans suggests that shared intentionality is a pivotal element in human cooperation. Moreover, the similarity between the performance of humans and the IW model sheds light on the computational formulation of shared intentionality and further advances our understanding of the nature of cooperation.

The ancient Greek hero Ulysses chose to bind himself to resist the temptation of Sirens, highlighting the fact that humans may voluntarily sacrifice their freedom of choice to achieve committed goals. In this work, we propose a computational model for such commitment under the framework of Bayesian Theory of Mind. The model is based on the idea that even when alone, humans act to better demonstrate their intentions to an imagined third-party observer (ITO) censoring their actions. Our model successfully captures the Ulysses-constraint of freedom, as the freedom confuses the ITO’s inference of their intention. We further show that, trajectories generated both by human actors and actors modeled with ITO censorship are easy to interpret both in the eyes of an actual human ob- server and an ITO. The results demonstrate that under conflict- ing desires, humans achieve commitment by spontaneously censoring their actions with an internalized theory of mind.

Coordinated hunting is widely observed in animals, and sharing rewards is often considered a major incentive for this success. However, it is unclear what causal roles are played by this reward-sharing mechanism. In order to systematically examine the effects of sharing rewards in animal coordinated hunting, we conduct a suite of modeling experiments using a state-of-the-art multi-agent reinforcement learning algorithm. The models are trained and evaluated with a task that simulates real-world collective hunting. We manipulate four evolutionarily important variables: reward distribution, hunting party size, free-rider problems, and hunting difficulty. Our results indicate that individually rewarded predators outperform predators that share rewards, especially when the hunting is difficult, the group size is large, and the action cost is high. Moreover, predators with shared rewards suffer from the free-rider problem. We conclude that sharing reward is neither necessary nor sufficient for modeling animal coordinated hunting through reinforcement learning.

Communication is highly overloaded. Despite this, even young children are good at leveraging context to understandambiguous signals. We propose a computational shared agency account of signaling that we call the Imagined We (IW)framework. We leverage Bayesian Theory of Mind to provide mechanisms for rational action planning and inverse actioninterpretation. In order to expand this framework for communication, we first treat signals as rational actions. We thenincorporate our rich understanding of intuitive utilities to constrain the scope of affordable actions. Finally, we treatcommunication as a cooperative act, subject to constraints of maximizing a shared utility. We implement this modelin two completely different behavioral psychology works to demonstrate the generality of the IW under different typesof uncertainty in cooperative communication. Additionally, we demonstrate that the IW outperforms multiple baselinemodels in a novel task across a series of simulation conditions.

Remaining committed to a joint goal in the face of many entic-ing alternatives is challenging. Doing so while cooperatingwith others under uncertainty is even more so. Despite this,agents can successfully and robustly use bootstrapping to con-verge on a joint intention from randomness under the ImaginedWe framework. We demonstrate the power of this model in areal-time cooperative hunting task. Additionally, we run a suiteof model experiments to answer some of the potential chal-lenges to converging that this model could face under imperfectconditions. Specifically, we ask what happens when (1) thereare increasingly many equivalent choices? (2) I only have anapproximate model of you? and (3) my perception is noisy? Weshow through a set of model experiments that this frameworkis robust to all three of these manipulations.

Language has been widely acknowledged as the benchmark of intelligence. However, evidence from cognitive science shows that intelligent behaviors in robust social interactions preexist the mastery of language. This review approaches human‐unique intelligence, specifically cooperation and communication, from an agency‐based theory of mind (ToM) account, emphasizing the ability to understand others' behaviors in terms of their underlying mental states. This review demonstrates this viewpoint by first reviewing a series of empirical works on the socio‐cognitive development of young children and non‐human primates in terms of their capacities in communication and cooperation, strongly suggesting that these capacities constitute the origin of human‐unique intelligence. Following, it reviews how ToM can be formalized as a Bayesian inference of the mental states given observed actions. Then, it reviews how Bayesian ToM can be extended to model the interaction of minds in cooperation and communication. The advantage of this approach is that non‐linguistic knowledge such as the visual environment can serve as the contextual constraint for multiple agents to coordinate with sparse and limited signals, thus demonstrating certain cognitive architectures underlying human communication. This article is categorized under:Applications of Computational Statistics > PsychometricsStatistical Models > Bayesian ModelsStatistical Models > Agent‐Based Models [ABSTRACT FROM AUTHOR]

Volatile organic compounds (VOCs) are ubiquitous in vehicle cabin environments, which can significantly impact the health of drivers and passengers, whereas quick and intelligent prediction methods are lacking. In this study, we firstly analyzed the variations of environmental parameters, VOC levels and potential sources inside a new car during 7 summer workdays, indicating that formaldehyde had the highest concentration and about one third of the measurements exceeded the standard limit for in-cabin air quality. Feature importance analysis reveals that the most important factor affecting in-cabin VOC emission behaviors is the material surface temperature rather than the air temperature. By introducing the attention mechanism and ensemble strategy, we present an LSTM-A-E deep learning model to predict the concentrations of 12 observed typical VOCs, together with other five deep learning models for comparison. By comparing the prediction-observation discrepancies and five evaluation metrics, the LSTM-A-E model demonstrates better performance, which is more consistent with field measurements. Extension of the developed model for predicting the 10-day VOC concentrations in a realistic residence further illustrates its excellent environmental adaptation. This study probes the not-well-explored in-cabin VOC dynamics via observation and deep learning approaches, facilitating rapid prediction and exposure assessment of VOCs in the vehicle micro-environment., (© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)