Approximately at the same time, based on the ideas This work proposes an approach that uses an evolutionary presented by Newell, Rosenbloom and Laird (1989), Laird algorithm along traditional Machine Learning methods released early versions of the SOAR cognitive architecture to build a digital, distributed cognitive agent capable of (Laird and Rosenbloom, 1996; Laird, 2012). By the end of emulating the potential actions (input-output behavior) of the 1990s, a large group of researchers involved in the Simulation a user while allowing further analysis and experimentation of Adaptive Behavior shaped the concept of Cognitive - at a certain level - of its internal structures. We focus Architecture as an essential set of structures and processes on the usage of simple devices and the automation of this necessary for the generation of a computational, cognitive building process, rather than manually designing the agent.

Many studies have evaluated the cognitive alignment of Pre-trained Language Models (PLMs), i.e., their correspondence to adult performance across a range of cognitive domains. Recently, the focus has expanded to the developmental alignment of these models: identifying phases during training where improvements in model performance track improvements in children's thinking over development. However, there are many challenges to the use of PLMs as cognitive science theories, including different architectures, different training data modalities and scales, and limited model interpretability. In this paper, we distill lessons learned from treating PLMs, not as engineering artifacts but as cognitive science and developmental science models. We review assumptions used by researchers to map measures of PLM performance to measures of human performance. We identify potential pitfalls of this approach to understanding human thinking, and we end by enumerating criteria for using PLMs as credible accounts of cognition and cognitive development.

With the rapid advancement of AI systems, their abilities to store, retrieve, and utilize information over the long term - referred to as long-term memory - have become increasingly significant. These capabilities are crucial for enhancing the performance of AI systems across a wide range of tasks. However, there is currently no comprehensive survey that systematically investigates AI's long-term memory capabilities, formulates a theoretical framework, and inspires the development of next-generation AI long-term memory systems. This paper begins by introducing the mechanisms of human long-term memory, then explores AI long-term memory mechanisms, establishing a mapping between the two. Based on the mapping relationships identified, we extend the current cognitive architectures and propose the Cognitive Architecture of Self-Adaptive Long-term Memory (SALM). SALM provides a theoretical framework for the practice of AI long-term memory and holds potential for guiding the creation of next-generation long-term memory driven AI systems. Finally, we delve into the future directions and application prospects of AI long-term memory.

Model and how we arrived at this proposal. The subsequent The Common Model of Cognition (Rosenbloom, Lebiere & two sections provide more details on two new modules that Laird, 2022) - née the Standard Model of the Mind (Laird, are proposed for inclusion into the Common Model - one for Lebiere & Rosenbloom, 2017) - is a developing consensus emotion and one for metacognitive assessment - and how concerning what must be in a cognitive architecture to they interact with the rest of the model.

Immersion in a task is a prerequisite for creativity. However, excessive arousal in a single task has drawbacks, such as overlooking events outside of the task. To examine such a negative aspect, this study constructs a computational model of arousal dynamics where the excessively increased arousal makes the task transition difficult. The model was developed using functions integrated into the cognitive architecture Adaptive Control of Thought-Rational (ACT-R). Under the framework, arousal is treated as a coefficient affecting the overall activation level in the model. In our simulations, we set up two conditions demanding low and high arousal, trying to replicate corresponding human experiments. In each simulation condition, two sets of ACT-R parameters were assumed from the different interpretations of the human experimental settings. The results showed consistency of behavior between humans and models both in the two different simulation settings. This result suggests the validity of our assumptions and has implications of controlling arousal in our daily life.

Traditionally, cognition has been considered a uniquely human capability involving perception, memory, learning, reasoning, and problem-solving. However, recent research shows that cognition is a fundamental ability shared by all living beings, from single cells to complex organisms. This chapter takes an info-computational approach (ICON), viewing natural structures as information and the processes of change in these structures as computations. It is a relational framework dependent on the perspective of a cognizing observer/cognizer. Informational structures are properties of the material substrate, and when focusing on the behavior of the substrate, we discuss morphological computing (MC). ICON and MC are complementary perspectives for a cognizer. Information and computation are inseparably connected with cognition. This chapter explores research connecting nature as a computational structure for a cognizer, with morphological computation, morphogenesis, agency, extended cognition, and extended evolutionary synthesis, using examples of the free energy principle and active inference. It introduces theoretical and practical approaches challenging traditional computational models of cognition limited to abstract symbol processing, highlighting the computational capacities inherent in the material substrate (embodiment). Understanding the embodiment of cognition through its morphological computational basis is crucial for biology, evolution, intelligence theory, AI, robotics, and other fields.

This study aims to place Lorenzo Magnanis Eco-Cognitive Computationalism within the broader context of current work on information, computation, and cognition. Traditionally, cognition was believed to be exclusive to humans and a result of brain activity. However, recent studies reveal it as a fundamental characteristic of all life forms, ranging from single cells to complex multicellular organisms and their networks. Yet, the literature and general understanding of cognition still largely remain human-brain-focused, leading to conceptual gaps and incoherency. This paper presents a variety of computational (information processing) approaches, including an info-computational approach to cognition, where natural structures represent information and dynamical processes on natural structures are regarded as computation, relative to an observing cognizing agent. We model cognition as a web of concurrent morphological computations, driven by processes of self-assembly, self-organisation, and autopoiesis across physical, chemical, and biological domains. We examine recent findings linking morphological computation, morphogenesis, agency, basal cognition, extended evolutionary synthesis, and active inference. We establish a connection to Magnanis Eco-Cognitive Computationalism and the idea of computational domestication of ignorant entities. Novel theoretical and applied insights question the boundaries of conventional computational models of cognition. The traditional models prioritize symbolic processing and often neglect the inherent constraints and potentialities in the physical embodiment of agents on different levels of organization. Gaining a better info-computational grasp of cognitive embodiment is crucial for the advancement of fields such as biology, evolutionary studies, artificial intelligence, robotics, medicine, and more.

Recent advances in artificial intelligence (AI) have produced systems capable of increasingly sophisticated performance on cognitive tasks. However, AI systems still struggle in critical ways: unpredictable and novel environments (robustness), lack of transparency in their reasoning (explainability), challenges in communication and commitment (cooperation), and risks due to potential harmful actions (safety). We argue that these shortcomings stem from one overarching failure: AI systems lack wisdom. Drawing from cognitive and social sciences, we define wisdom as the ability to navigate intractable problems - those that are ambiguous, radically uncertain, novel, chaotic, or computationally explosive - through effective task-level and metacognitive strategies. While AI research has focused on task-level strategies, metacognition - the ability to reflect on and regulate one's thought processes - is underdeveloped in AI systems. In humans, metacognitive strategies such as recognizing the limits of one's knowledge, considering diverse perspectives, and adapting to context are essential for wise decision-making. We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety. By focusing on developing wise AI, we suggest an alternative to aligning AI with specific human values - a task fraught with conceptual and practical difficulties. Instead, wise AI systems can thoughtfully navigate complex situations, account for diverse human values, and avoid harmful actions. We discuss potential approaches to building wise AI, including benchmarking metacognitive abilities and training AI systems to employ wise reasoning. Prioritizing metacognition in AI research will lead to systems that act not only intelligently but also wisely in complex, real-world situations.

This paper presents a novel approach to multi-robot planning and collaboration. We demonstrate a cognitive strategy for robots in human-robot teams that incorporates metacognition, natural language communication, and explainability. The system is embodied using the HARMONIC architecture that flexibly integrates cognitive and control capabilities across the team. We evaluate our approach through simulation experiments involving a joint search task by a team of heterogeneous robots (a UGV and a drone) and a human. We detail the system's handling of complex, real-world scenarios, effective action coordination between robots with different capabilities, and natural human-robot communication. This work demonstrates that the robots' ability to reason about plans, goals, and attitudes, and to provide explanations for actions and decisions are essential prerequisites for realistic human-robot teaming.

This paper investigates the computational mechanisms underlying a type of metacognitive monitoring known as detached mindfulness, a particularly effective therapeutic technique within cognitive psychology. While research strongly supports the capacity of detached mindfulness to reduce depression and anxiety, its cognitive and computational underpinnings remain largely unexplained. We employ a computational model of metacognitive skill to articulate the mechanisms through which a detached perception of affect reduces emotional reactivity.