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Abstract As artificial intelligence (AI) becomes embedded in critical decisions involving health, safety, finance, and governance, the key challenge is no longer whether humans and AI will collaborate, but rather how to structure this collaboration to achieve true complementarity. Human–AI complementarity refers to the conditions under which human–AI teams outperform either humans alone or AI systems alone. This paper advances the science of human–AI teaming for decision making by integrating insights from cognitive science, AI, human factors, organizational behavior, and ethics. We propose a framework grounded in collective intelligence and anchored in the foundational cognitive processes–reasoning, memory, and attention–to understand and engineer effective human–AI teams. We examine the sociotechnical factors that shape team effectiveness, including team composition, trust calibration, shared mental models, training, and task structure. We then outline design principles for achieving complementarity: defining goals and constraints, partitioning roles, orchestrating attention and interrogation, building knowledge infrastructures, and establishing continuous training and evaluation. We conclude with theoretical, practical, and policy implications, emphasizing alignment with human values, accountability, and equity. Together, these insights offer a roadmap for building human–AI teams that are not only high-performing and adaptive, but also transparent, trustworthy, and fundamentally human-centered. 

IntroductionGenerative Artificial Intelligence has made significant impacts in many fields, including computational cognitive modeling of decision making, although these applications have not yet been theoretically related to each other. This work introduces a categorization of applications of Generative Artificial Intelligence to cognitive models of decision making. MethodsThis categorization is used to compare the existing literature and to provide insight into the design of an ablation study to evaluate our proposed model in three experimental paradigms. These experiments used for model comparison involve modeling human learning and decision making based on both visual information and natural language, in tasks that vary in realism and complexity. This comparison of applications takes as its basis Instance-Based Learning Theory, a theory of experiential decision making from which many models have emerged and been applied to a variety of domains and applications. ResultsThe best performing model from the ablation we performed used a generative model to both create memory representations as well as predict participant actions. The results of this comparison demonstrates the importance of generative models in both forming memories and predicting actions in decision-modeling research. DiscussionIn this work, we present a model that integrates generative and cognitive models, using a variety of stimuli, applications, and training methods. These results can provide guidelines for cognitive modelers and decision making researchers interested in integrating Generative AI into their methods. 

Abstract Under long-standing threat of seasonal influenza outbreaks, it remains imperative to understand the drivers of influenza dynamics which can guide mitigation measures. While the role of absolute humidity and temperature is extensively studied, the possibility of ambient ozone (O₃) as an environmental driver of influenza has received scant attention. Here, using state-level data in the USA during 2010–2015, we examined such research hypothesis. For rigorous causal inference by evidence triangulation, we applied 3 distinct methods for data analysis: Convergent Cross Mapping from state-space reconstruction theory, Peter-Clark-momentary-conditional-independence plus as graphical modeling algorithms, and regression-based Generalised Linear Model. The negative impact of ambient O₃on influenza activity at 1-week lag is consistently demonstrated by those 3 methods. With O₃commonly known as air pollutant, the novel findings here on the inhibition effect of O₃on influenza activity warrant further investigations to inform environmental management and public health protection. 

Abstract What were relevant predictors of individuals' proclivity to adhere to recommended health‐protective behaviors during the COVID‐19 pandemic in Denmark? Applying machine learning (namely, lasso regression) to a repeated cross‐sectional survey spanning 10 months comprising 25 variables (Study 1;N= 15,062), we found empathy toward those most vulnerable to COVID‐19, knowledge about how to protect oneself from getting infected, and perceived moral costs of nonadherence to be strong predictors of individuals' self‐reported adherence to recommended health‐protective behaviors. We further explored the relations between these three factors and individuals' self‐reported proclivity for adherence to recommended health‐protective behaviors as they unfold between and within individuals over time in a second study, a Danish panel study comprising eight measurement occasions spanning eight months (N = 441). Results of this study suggest that the relations largely occurred at the trait‐like interindividual level, as opposed to at the state‐like intraindividual level. Together, the findings provide insights into what were relevant predictors for individuals' overall level of adherence to recommended health‐protective behaviors (in Denmark) as well as how these predictors might (not) be leveraged to promote public adherence in future epidemics or pandemics. 

Abstract Small unmanned aerial systems (sUAS) are used more regularly and widely in disaster response. Like other personnel involved in disaster response, the sUAS pilots work for long periods, experience extreme stress and fatigue. They often arrive at the disaster fatigued (due to long drives to get there). However, unlike other personnel in this domain, there is little research on the effects of fatigue on sUAS pilots. Our experiences with a series of three real-world deployments highlight the challenges of conducting human factors research during disaster response and recovery. We specifically present lessons learned from having participant researchers embedded in three disasters with the sUAS pilot teams. These lessons result in a set of feasible and non-interruptive methods and metrics for conducting human factors research during field events. Preliminary results and recommended next steps are presented.