Editorial: Neurobiological foundations of cognition and progress toward artificial general intelligence

Abstract

The problems are as follows: a) what is intelligence, b) how human intelligence differs from that in the other species, c) how could intelligence emerge in the physical universe, d) what is the future of AI? e) The last decade has witnessed breakthrough advances in machine learning methods rooted in the ideas of digital neurons and perceptron dating back to the mid 20 th century, what factors have been responsible for these advances? To facilitate discussion, we outline opinions and hypothetical solutions that are intended as discussion starters and encompass theoretical foundations, synopsis of contributions in this volume and a new synthesis.First, the opinions. a) According to a definition attributed to Jean Piaget "intelligence is what you use when you don't know what to do, when neither innateness nor learning has prepared you for the particular situation" (Calvin, 1996, p.1)) Novel situations engage the understanding capacity in a dual function of constructing adequate responses and overcoming the inertia of prior learning (the legacy mind set) interfering with the construction. b) All species are capable of learning while the understanding capacity is uniquely human. c) Intelligence mechanisms are hypothesized to be integral to those underlying the emergence of life and involving self-organization in networks of interacting elements where parts of networks fold into stable units separated by an active surface ("membrane") from their surrounds (Friston, 2010). d) The future of AI involves transition from machine learning to machine understanding centered on the approximation of meaning creation in self-organizing neuronal networks. e) Machine learning methods are computationally demanding. Their successes have been due to a confluence of two developments: ingenious heuristics to minimize computational demands and a trillion-fold increase of hardware efficiency. Machine understanding will employ moderate capacity platforms with low energy demands to deliver functions inaccessible to learning (Yufik et al, 2026). Next, the hypotheses.1. The foundations.In the history of psychology, behaviorism was reducing intelligence to conditioning while cognitivism has been equating intelligence to computation. The "Bayesian intelligence" school of thought conceptualizes brain as a "prediction machine". Theories of understanding conceptualize human brain as a biophysical system for the production of meaning and pivot on the ideas of "neuronal packets" and "wave packets" (Yufik et al, 2026;Yufik 1998;Freeman, 2000;Kozma et al, 2026). The former idea coheres with the key insight in Gestalt psychology: "Sensory units have acquired names, have become symbolic and known to have practical uses while they have existed as units before any of these further facts were added. … it is precisely the original segregation of circumscribed wholes which makes it possible for the sensory world to appear so utterly imbued with meaning" (Kohler, 1947, p.82) The "wave packets" idea attributes production of meaning to amplitude-and phase-modulated waves of activity percolating through systems of interacting neuronal populations. Suggestions for the integration of the two conceptions are stated in Section 3.Understanding involves "apprehending general relations in multitudes of particulars" (Webster's Dictionary). A parable of blind men and an elephant comes in handy: meaning is produced in constructing relations that integrate disparate reports into a coherent whole (assume a blind analyst charged with the task). Integration enables explanation and prediction: elephants behave differently from any of the entities in the reports. Piaget's theory characterizes understanding as "the system of operational co-ordinations that transform objects of thought in the same way as actions transform material objects… in order to understand, one must be able to construct in thought indefinite series … and treat the series one has actually observed as just one sector of an unlimited range of possibilities" (Piaget 1978, p. 222).Another critical insight in psychology concerns the dynamics of (the "movement of consciousness" in) coordinating some object Z with other objects, starting will accessing Z: "Through hovering of the attention in the neighborhood of the desired object, the accumulation of associates becomes so great that the combined tension of their neural processes break through the bar, and the nervous wave pours into the tract which has so long been awaiting its advent…The activities in Z will at first be a mere tension …until combined irradiations upon Z succeed in helping the tension there to overcome the resistance and is arousing Z to full activity" (James, 1950, v.1, p. 586).Finally, the most fundamental, cornerstone idea in psychology and neuroscience is the idea of neuronal assemblies by Donald Hebb, conceptualizing groups of neurons as the basic functional units in the neuronal system. A critical insight concerns interaction between assembles in the formation of percepts (e.g. assemblies a, b, c interact in forming the percept of triangle ABC):"…simultaneous activity in a, b, c would establish facilitation between them, through their chance anatomical interconnections and the enlargement of synaptic knobs… The resulting superordinate system must be essentially a new one, by no means a sum of hooking together of a, b and c…a better notation for the new structure is t: the assembly of cells whose activity…is perception of the triangle as a distinctive whole" Hebb, 1947, p. 97) In the emerging integrated picture so far, bringing together in imagination two "segregated wholes" appears to be one of the basic operations in meaning creation. Unsurprisingly, a received theory of language understanding places operation Merge (combining some objects Z and some X into a unit) at the core of the capacity (Chomsky 2004(Chomsky , 2014;;Berwick, 2016). We will return to these notions in Section 3, after re-centering the discussion as follows.Production of meaning is coextensive with making choices (which Z, which X?): automatisms admit no choices, choices are drilled in from the outside in learning and are decided internally in understanding. An array of 10 objects affords 1,024 choices, accounting for permutations (A after B is not the same as B after A) increases that number to 3, 628, 800. Boundary barriers (or "bars" James, 1950, v.1, p. 586) turn culling through combinations into steeplechase running, multiple objects (e.g. 32 chess pieces) yield astronomically large combinatorial spaces: how can the human system run steeplechase marathons and real time sprints in such spaces while subsisting on miniscule energy budgets? "Operational coordination" (Piaget 1978, p. 222) transforms combinations into coherent structures (models) amenable to coordination with other models: what mechanisms drive the expanding coherence? Section 3 ventures a hypothesis: understanding compresses combinatorial spaces into minute volumes imbued with meaning. Section 2 highlights connections between the articles in this volume, the questions above and the theory in Section 3.Davis, Shuebeler and Kirk https://www.frontiersin.org/journals/systems-neuroscience/articles/10.3389/fnsys.2025.1718733/full analyze EEG data to assess the degree of coherence in neuronal processes, on the assumption that high coherence corresponds to achieving mental clarity and entering into a meditative state of mind. Following the Global Workspace Theory, the paper attributes achieving coherence to global information broadcasting and active integration, with the spotlight of consciousness illuminating the salient contents. Conscious awareness can also include "silent" elements having no reportable sensory content . Global coherence is equated to synchronization entailing reduction of entropy. Dodig -Crnkovich https://www.frontiersin.org/journals/systemsneuroscience/articles/10.3389/fnsys.2026.1730097/full presents a broadly conceptualized analysis of biological cognition and AI, addressing the possible origins and observable manifestation of intelligence in the physical universe. Within this all-encompassing framework (Info-Computational Framework, or ICON) the mind is viewed as a material phenomenon in a continuum stretching from inanimate substances to primitive organisms and up to sentient beings. The article includes definitions of general notions, e.g. "information is a difference that makes a difference for the regulation of system environment interactions", etc. The intended meaning of such definitions is explicated in the analysis, including validation approaches.Wright, David and Bourke https://www.frontiersin.org/journals/systems-neuroscience/articles/10.3389/fnsys.2025.1683448/full offer an elegant interpretation of embryogenesis dynamics in terms of predictive error minimization. The predictive error minimization framework is given a dual application: an explanatory principle in a theory of selforganization dynamics in the embryo and a mathematical tool for predicting that dynamics from data (embryo viability, etc.) The proposed theory views synaptically linked neurons as coupled oscillators, self-organization drives synaptic fluxes towards equilibrium in a synchronized state where each couple functions as an element in Markov blanket that maintains equilibrium in the system and causes convergence towards error minimization.Vavrecka and Wermter https://www.frontiersin.org/journals/systems-neuroscience/articles/10.3389/fnsys.2025.1683133/full argue that Large Language Models (LLN) fail to address the symbol grounding problem and operate strictly on the basis of statistical regularities. They contend that closing conceptual gaps between symbolic tokens, neural representations, and embodied experiences will require a theoretical framework accounting for the integration of continuous sensory flows, goal-directed behavior and neural learning.Grossberg https://www.frontiersin.org/journals/systems-neuroscience/articles/10.3389/fnsys.2025.1630151/full reviews developments in Adaptive Resonance Theory (ART) since the time of its inception and presents new results, comparing performance in ART to that in LLM. The article argues that progress towards AGI will lead to Autonomous Adaptive Intelligence (AAI) systems implementing the core ART intuition: learning proceeds in an interplay between bottom-up and top-down processes. Comparing AAI to LLM highlights the limitations of the latter rooted in the limitations of deep learning.Kirkpatrick https://www.frontiersin.org/journals/systems-neuroscience/articles/10.3389/fnsys.2025.1695493/full argues insightfully that human intelligence is fundamentally grounded in motion and asserts that AGI will thoroughly integrate motion and cognition and thus escape from the circular logic inherent in the computational metaphor. In a tour de force analysis, the paper traces successes and shortcoming of the metaphor to McCullogh-Pitts models of neurons inspired by the metaphor. A critical limitation of such models is manifested in their ability to emulate proofs of mathematical propositions but not the processes that underlie the understanding of those proofs. The paper makes constructive suggestions for the development of biologically inspired AGI.Rachman and Pashea https://www.frontiersin.org/journals/sociology/articles/10.3389/fsoc.2026.1646503/full associate human intelligence with problem solving in diverse contexts under a general objective of minimizing uncertainty. The authors observe that the uncertainty minimization criteria apply to all species while the use of symbolic systems allows humans to expand the contexts beyond the biologically fixed constraints. Framing the challenge facing AGI as creating systems that are aligned with human values and match human cognitive flexibility, the article reverts Active Inference as a promising venue towards meeting that challenge. A novel idea in their approach is the placement of moral values at the apex of predictive hierarchies. A formalism is proposed ( "transcendental inference") that decomposes variational free energy into accuracy and complexity terms and expresses conditions for social agreement as maximizing accuracy (benefits) while minimizing complexity (costs).A theory of understanding requires switching from the "intelligence through learning" mindset to the "intelligence through understanding" mindset: the former one is expressed in the idea of perceptron (a fixed neuronal structure adapting through feedback to repetitive stimuli bundles) while the latter one envisions a self-organizing neuronal pool adapting to varying stimuli streams where stimuli compositions might be never twice the same. We will use the Virtual Associative Structures (VaST) proposal (Yufik, 1998a) to illustrate the idea, starting with the key assumptions.1. The pool -environment interactions seek to secure steady energy inflows from the energycarrying stimuli. The evolution of intelligence involves minimization of expenditures (caloric costs and dissipative losses) in securing at In the the is (Yufik, in that it can from the and energy into further minimization neuronal neurons and of in networks when fold into functional units separated by energy barriers from the Models in of networks when neurons facilitate coordination between low while the coordination in the and models are self-organizing stimuli streams by units and models in the combinatorial reducing multitudes of possible combinations to a of outside of such are not but beyond the of responses to stimuli deep learning compresses multiple such automatisms into one are of meaning" (or et al, integrate and thus meaning to indefinite in the stimuli streams can have