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1. Utility Theory based Cognitive Modeling in the Application of Robotics: A Survey

   https://doi.org/10.2139/ssrn.6435083  

   Yang, Qin (January 2026, SSRN)

   Cognitive modeling, which explores the essence of cognition, including motivation, emotion, and perception, has been widely applied in the artificial intelligence (AI) agent domains, such as robotics. From the computational perspective, various cognitive functionalities have been developed through utility theory to provide a detailed and process-based understanding for specifying corresponding computational models of representations, mechanisms, and processes. Especially for decision-making and learning in multi-agent/robot systems (MAS/MRS), a suitable cognitive model can guide agents in choosing reasonable strategies to achieve their current needs and learning to cooperate and organize their behaviors, optimizing the system’s utility, building stable and reliable relationships, and guaranteeing each group member’s sustainable development, similar to the human society. This survey examines existing robotic systems for developmental cognitive models in the context of utility theory. We discuss the evolution of cognitive modeling in robotics from behavior-based robotics (BBR) and cognitive architectures to the properties of value systems in robots, such as the studies on motivations as artificial value systems, and the utility theory based cognitive modeling for generating and updating strategies in robotic interactions. Then, we examine the extent to which existing value systems support the application of robotics from an AI agent cognitive modeling perspective, including single-agent and multi-agent systems, trust among agents, and human-robot interaction. Finally, we survey the existing literature of current value systems in relevant fields and propose several promising research directions, along with some open problems that we deem necessary for further investigation. 

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2. Early Emergence of Metacognition in Rhesus Monkeys

   https://doi.org/10.1111/desc.70012  

   Huang, Yiyun; Rosati, Alexandra G (May 2025, Developmental Science)

   ABSTRACT Metacognition, or monitoring and controlling one's knowledge, is a key feature of human cognition. Accumulating evidence shows that foundational forms of metacognition are already present in young infants and then scaffold later‐emerging skills. Although many animals exhibit cognitive processes relevant to metacognition, it is unclear if other species share the developmental trajectories seen in humans. Here, we examine the emergence of metacognitive information‐seeking in rhesus monkeys (Macaca mulatta). We presented a large sample of semi‐free‐ranging monkeys, ranging from juvenility to adulthood, with a one‐shot task where they could seek information about a food reward by bending down to peer into a center vantage point in an array of tubes. In thehiddencondition, information‐seeking was necessary as no food was visible on the apparatus, whereas in thevisiblecontrol, condition information‐seeking was not necessary to detect the location of the reward. Monkeys sought information at the center vantage point more often when it was necessary than in the control condition, and younger monkeys already showed competency similar to adults. We also tracked additional monkeys who voluntarily chose not to approach to assess monkeys’ ability to actively infer opportunities for information‐seeking, and again found similar performance in juveniles and adults. Finally, we found that monkeys were overall slower to make metacognitive inferences than to approach known reward, and that younger monkeys were specifically slower to detect opportunities for information‐seeking compared to adults. These results indicate that many features of mature metacognition are already detectable in young monkeys, paralleling evidence for “core metacognition” in infant humans. 

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3. Beyond the matrix: Experimental approaches to studying cognitive agents in social-ecological systems

   https://doi.org/10.1016/j.cognition.2024.105993  

   Hertz, Uri; Köster, Raphael; Janssen, Marco A; Leibo, Joel Z (January 2025, Cognition)

   Studying social-ecological systems, in which agents interact with each other and their environment are important both for sustainability applications and for understanding how human cognition functions in context. In such systems, the environment shapes the agents' experience and actions, and in turn collective action of agents changes social and physical aspects of the environment. Here we review current investigation approaches, which rely on a lean design, with discrete actions and outcomes and little scope for varying environmental parameters and cognitive demands. We then introduce a multiagent reinforcement learning (MARL) approach, which builds on modern artificial intelligence techniques, and provides new avenues to model complex social worlds, while preserving more of their characteristics, and allowing them to capture a variety of social phenomena. These techniques can be fed back to the laboratory where they make it easier to design experiments in complex social situations without compromising their tractability for computational modeling. We showcase the potential MARL by discussing several recent studies that have used it, detailing the way environmental settings and cognitive constraints can lead to the emergence of complex cooperation strategies. This novel approach can help researchers bring together insights from human cognition, sustainability, and AI, to tackle real world problems of social-ecological systems. 

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4. Neurodynamical Computing at the Information Boundaries of Intelligent Systems

   https://doi.org/10.1007/s12559-022-10081-9  

   Monaco, Joseph D.; Hwang, Grace M. (December 2022, Cognitive Computation)

   Artificial intelligence has not achieved defining features of biological intelligence despite models boasting more parameters than neurons in the human brain. In this perspective article, we synthesize historical approaches to understanding intelligent systems and argue that methodological and epistemic biases in these fields can be resolved by shifting away from cognitivist brain-as-computer theories and recognizing that brains exist within large, interdependent living systems. Integrating the dynamical systems view of cognition with the massive distributed feedback of perceptual control theory highlights a theoretical gap in our understanding of nonreductive neural mechanisms. Cell assemblies—properly conceived as reentrant dynamical flows and not merely as identified groups of neurons—may fill that gap by providing a minimal supraneuronal level of organization that establishes a neurodynamical base layer for computation. By considering information streams from physical embodiment and situational embedding, we discuss this computational base layer in terms of conserved oscillatory and structural properties of cortical-hippocampal networks. Our synthesis of embodied cognition, based in dynamical systems and perceptual control, aims to bypass the neurosymbolic stalemates that have arisen in artificial intelligence, cognitive science, and computational neuroscience. 

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5. Testing the greater male variability phenomenon: male mountain chickadees exhibit larger variation in reversal learning performance compared with females

   https://doi.org/10.1098/rspb.2020.0895  

   Branch, Carrie L.; Sonnenberg, Benjamin R.; Pitera, Angela M.; Benedict, Lauren M.; Kozlovsky, Dovid Y.; Bridge, Eli S.; Pravosudov, Vladimir V. (July 2020, Proceedings of the Royal Society B: Biological Sciences)

   null (Ed.)

   The greater male variability phenomenon predicts that males exhibit larger ranges of variation in cognitive performance compared with females; however, support for this pattern has come exclusively from studies of humans and lacks mechanistic explanation. Furthermore, the vast majority of the literature assessing sex differences in cognition is based on studies of humans and a few other mammals. In order to elucidate the underpinnings of cognitive variation and the potential for fitness consequences, we must investigate sex differences in cognition in non-mammalian systems as well. Here, we assess the performance of male and female food-caching birds on a spatial learning and memory task and a reversal spatial task to address whether there are sex differences in mean cognitive performance or in the range of variation in performance. For both tasks, male and female mean performance was similar across four years of testing; however, males did exhibit a wider range of variation in performance on the reversal spatial task compared with females. The implications for mate choice and sexual selection of cognitive abilities are discussed and future directions are suggested to aid in the understanding of sex-related cognitive variation. 

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