Artificial Cognition Overview

• Artificial cognition is the study of designing computational systems that mirror natural cognitive processes like perception, learning, and reasoning.
• It integrates interdisciplinary approaches from dynamical systems, cognitive science, and neuro-inspired models to create embodied and adaptive AI agents.
• Research in artificial cognition focuses on developing hybrid architectures, benchmarking metrics, and self-regulating systems to enhance AI autonomy and real-world performance.

Artificial cognition refers to the engineering, analysis, and evaluation of computational systems that exhibit capacities, processes, and organizational principles analogous to those of natural cognitive systems—perceiving, acting, learning, reasoning, remembering, self-regulating, and adapting—across a broad spectrum of physical substrates and architectural paradigms. The study of artificial cognition integrates perspectives from dynamical systems theory, cognitive science, information theory, biological and phenomenological models, brain-inspired and biologically-inspired computation, and advanced neuro-symbolic architectures. It emphasizes both the theoretical foundations and practical methodologies that shape the design, benchmarking, and deployment of AI systems whose capacities exceed or diverge from classical AI benchmarks, offering a unified framework for understanding, comparing, and advancing artificial agents in research and application contexts.

1. Theoretical Foundations and Models of Artificial Cognition

Artificial cognition is characterized by its inheritance of principles from cognitive science and philosophy, encompassing symbolic, connectionist, Bayesian, and dynamical systems frameworks (Mao et al., 28 Aug 2025). Symbolic approaches treat cognition as discrete symbol manipulation governed by logical rules and ontologies; connectionist approaches emphasize distributed activity in parametrically adaptive neural networks (e.g., recurrent, gated neural architectures); Bayesian accounts formalize cognition as probabilistic inference over latent states and structured environments. In dynamical systems perspectives—exemplified by the Situated Embodied Dynamics (SED) framework—cognition emerges as the nonlinear coupling of brains, bodies, and environments, instantiated through continuous-time state-space equations: $$\dot{x}(t) = f(x(t), u(t), t) \qquad y(t) = g(x(t), u(t))$$ where $x(t)$ denotes the agent's internal state, $u(t)$ external stimuli, and $y(t)$ outputs (Oliveira et al., 2020).

Biologically inspired models expand the notion of cognition to processes of morphological computation, self-assembly, autopoiesis, and active inference, emphasizing continuous adaptation, self-maintenance, and embodied information processing (Dodig-Crnkovic, 2024). Cognitive architectures increasingly integrate memory, metacognitive monitoring, and self-directed goal management, producing systems that continuously assess competence, adapt learning rates, and restructure representations (Golilarz et al., 1 Dec 2025).

Comparative cognition frames artificial systems as new “species” analyzed alongside biological ones, using common theoretical constructs—softmax RL models, signal detection metrics, and direct analogs to animal psychology protocols (e.g., invisible displacement, delayed match-to-sample) (Voudouris et al., 4 Mar 2025).

2. Embodiment, Situatedness, and the Dynamics of Value

Embodiment and situatedness are fundamental in phenomenological and dynamical-systems approaches to artificial cognition. The SED model postulates that true cognition requires the inextricable coupling of perception and action in real time, with value learning integrated as a core dynamical variable rather than a fixed external objective: $$\dot{V}(t) = h(x(t), V(t), u(t), t) + \alpha \cdot r(x(t), u(t))$$ where $V(t)$ represents a vector of values co-evolving with the embodied state (Oliveira et al., 2020). This formulation enables in situ adaptation of priorities (e.g., via value-homeostasis), leading to resilient, real-time alignment with human-injected reward signals and context-specific feedback.

Morphological computation underscores that computation may be distributed into the body's physical properties: compliance, self-organizing material dynamics, or physiological constraints partially offload cognitive burden from centralized control structures. Quantitative measures, such as energy budget decompositions or conditional mutual information

$M = I(A_t; S_{t+1} \mid S_t)$

assess the contribution of the morphology to cognitive tasks (Dodig-Crnkovic, 2024).

Embodiment thereby transcends classical computationalist boundaries, grounding meaning, memory, and performance in continuous sensorimotor loops and environmental interaction (Mao et al., 28 Aug 2025, Dodig-Crnkovic, 2024).

3. Architectures: Memory, Reasoning, Multi-Agent Systems, and Emergence

Modern artificial cognitive systems integrate explicit memory architectures, diverse reasoning modalities, and orchestrated multi-agent interactions. The Memory Bear system exemplifies a biologically inspired architecture combining three layers—Storage, Orchestration, and Application—where memory extraction, long-term graph-based and implicit memories, and dynamic activation-based retrieval coalesce to support multi-hop reasoning and adaptive planning (Wen et al., 17 Dec 2025). Cognitive cycles in such systems embed chains of memory retrieval, context serialization, LLM-driven generation, and memory update, closing the perception–memory–action loop.

Open, modular cognitive frameworks orchestrate LLMs, symbolic reasoning engines, external expert “tools” (e.g., chess engines, medical APIs), and retrieval-augmented generation pipelines, achieving stepwise reasoning and explainability (Adnan et al., 2024, Spivack et al., 2024). Architectures may employ dual layers, with a Cognitive Layer managing planning, memory, meta-cognition, and agent roles above a Conversational Layer of LLMs (Spivack et al., 2024).

Agentic exocortices aggregate diverse specialist AI agents into swarms whose emergent behavior, via hierarchical feedback, distributed communication, and self-critique, realizes collective artificial cognition beyond individual agent capabilities (Yager, 2024). Multi-agent systems instantiate functions ranging from experimental design to literature review and ideation, with inter-agent protocols crafted for transparency and extensibility.

Novel dynamical system architectures such as COGENT3 instantiate cognition via emergent structures: pattern-forming triads, role permutations, temperature-modulated exploration/exploitation, and memory-kernel driven non-Markovian adaptation. Cognitive order parameters, susceptibility, and synchronization metrics quantify the emergence and stabilization of computational modules (Salazar, 5 Apr 2025).

4. Measurement, Benchmarking, and Comparative Methodology

A critical goal of artificial cognition is the development of implementation-independent, multidimensional metrics for quantifying information processing, cognitive work, and cognitive augmentation (Fulbright, 2022). Approaches include:

• Entropy-based metrics: Shannon and Rényi entropy, mutual information, and Kullback–Leibler divergence evaluate unpredictability, although pure entropy metrics may mischaracterize value-increasing transformations such as sorting.
• Algorithmic and Kolmogorov–Chaitin complexity: Minimal program length (K(s)), algorithmic probability $m(s)$, and block decomposition methods measure structural complexity and compressibility, validating cognitive plausibility via behavioral complexity profiles (Gauvrit et al., 2015).
• Emergence and structural information: Human concept-learning and emergent-capacity metrics capture the non-additive, multiscale rise in structural complexity and capacity for novelty.
• Processing-effort and cognitive work: Quantify representational change, efficiency, and gain.
• Comparative cognition protocols: Standardized tasks and cross-species/cross-architecture model fitting isolate cognitive mechanisms and facilitate rigorous comparison with biological agents (Voudouris et al., 4 Mar 2025).

Metrics are extended to analyze cognition in hybrid systems, assessing augmentation, joint emergent behavior, and persistent learning trajectories in human–AI collaborations.

5. Organizational and Informational Dimensions: Cognition Spaces

Artificial cognition can be represented in multidimensional “cognition spaces” parameterized by organizational (embodiment, agency, developmental complexity, interaction) and informational (computational complexity, richness of internal state, inter-agent exchange) axes. The cognition space framework allows placement and comparison of systems from minimal basal agents (bacteria, protozoans, xenobots) to neural systems (deep nets, multi-agent RL), to human–AI hybrids (LLM co-trainers, brain–computer interfaces) (Solé et al., 19 Jan 2026).

Agency is formalized as: $$A = \left|\frac{\partial J}{\partial \pi}\right|,\qquad J(\pi) = E_\pi\left[ \int_0^T V(x(t), E(t))\,dt \right]$$ highlighting sensitivity of viability to policy changes.

Artificial agents cluster in subregions of high computational capacity but low agency and social coupling, with large unoccupied regions corresponding to unrealized organizational forms. Promising frontiers include high-agency artificial agents (self-repair, metabolic adaptation), basal–neural hybrids (morphological-computational synergy), and high-feedback human–AI collectives (Solé et al., 19 Jan 2026).

6. Challenges, Biases, and Critical Perspectives

The study and evaluation of artificial cognition faces methodological and epistemic challenges:

• Anthropocentric bias: Two forms—auxiliary oversight (Type I) and mechanistic chauvinism (Type II)—can lead to over- or under-attribution of cognitive capacities in AI by misinterpreting behavioral benchmarks or discounting non-human-like strategies. Properly mapping task performance to underlying competence demands iterative behavioral–mechanistic co-evaluation, formalizing auxiliary factors, and adopting species-fair protocols (Millière et al., 2024).
• Structural limitations: Absence of self-monitoring, meta-cognition, plasticity, adaptive goal restructuring, representational maintenance, and embodied feedback constrains the autonomy, adaptability, and reliability of current AI (Golilarz et al., 1 Dec 2025). Roadmaps emphasize integrating these capacities, drawn from neurocognitive principles and systems neuroscience.
• Interpretability and theoretical underpinning: Classical deep learning architectures retain black-box aspects, lacking semantic disentanglement and explicit representational transparency (Diamant, 2018). Biologically-inspired frameworks underscore the management and association of physical and semantic information, with emphasis on narrative memory, information duality, and Kolmogorov-based complexity (Diamant, 2018).
• Measurement limitations: Single-dimension or entropy-centric metrics are insufficient; a suite of formally grounded, context-sensitive, and semantically informed measures is needed for principled evaluation and optimization (Fulbright, 2022).

7. Future Directions: Integration, Personalization, and Autonomy

Strategic research directions consolidate artificial cognition as an integrative paradigm:

• Hybrid architectures: Neuro-symbolic, memory-augmented, and dynamically reconfigurable systems (e.g., COGENT3, exocortices) enable modular, adaptive cognition (Yager, 2024, Salazar, 5 Apr 2025).
• Personalization and co-evolution: Parameterization by user profiles, multimodal signal encoding, and cognitive co-evaluation in ethics foster AI systems attuned to individual and societal goals (Mao et al., 28 Aug 2025).
• Empirical and epistemic bridging: Artificial cognition serves as both a design paradigm (for building generalist agents) and an epistemic tool (for investigating the boundary conditions and emergent patterns of cognitive phenomena) (Mao et al., 28 Aug 2025).
• Standardized evaluation and comparative datasets: Advancing a comparative science of cognition—across species, architectures, and hybrid collectives—supports systematic discovery of general principles and emergent behaviors (Voudouris et al., 4 Mar 2025, Solé et al., 19 Jan 2026).
• Autonomous, self-directed systems: Bridging the gap between static predictors and dynamic, self-modifying “organisms,” future architectures will integrate continuous learning, agentic initiative, and regulatory mechanisms for robust alignment and safety (Golilarz et al., 1 Dec 2025).

In summary, artificial cognition synthesizes advanced organisational, informational, dynamical, and epistemic frameworks to create, analyze, and extend the capabilities of artificial agents. By uniting real-time embodiment, memory, meta-cognition, and adaptive reasoning within context-sensitive, multi-agent, and hybridized systems, the field progresses toward artificial systems that not only perform tasks but also exhibit emergent, interpretable, and robust cognitive properties comparable to—and continuously co-evolving with—the natural intelligences that inspired them.