From Machine Learning to Machine Understanding: Neuroscientists Map the Path to True Artificial General Intelligence

What separates a chess engine that plays brilliant moves from a human who actually understands why they are brilliant? A provocative editorial published in Frontiers in Systems Neuroscience argues that this question is not a philosophical curiosity but the central obstacle standing between today’s powerful artificial intelligence and genuine artificial general intelligence. The editorial, written by Yan Yufik of Virtual Structures Research, Robert Kozma of the University of Memphis, and James Kroger of New Mexico State University, concludes a trilogy of research collections spanning a decade and stakes out a bold claim: the future of AI lies not in better learning but in machine understanding, a capacity rooted in the self-organizing physics of living neural tissue.

The trilogy began in 2016 with a collection on self-organization in the nervous system, continued in 2021 with an examination of understanding capacity in humans and machines, and now closes with a volume on the neurobiological foundations of cognition. Across these installments, the editors have pressed a set of deceptively simple questions. What is intelligence? How does human intelligence differ from that of other species? How could intelligence emerge in a physical universe at all? And what factors explain the startling breakthroughs of the last decade in machine learning, a field still built on ideas about digital neurons and perceptrons that date to the mid-twentieth century?

The editors’ answers begin with a distinction that most of the AI industry has blurred. All species can learn, they argue, but the capacity for understanding is uniquely human. They borrow a definition attributed to the developmental psychologist Jean Piaget: intelligence is what you use when you do not know what to do, when neither instinct nor prior training has prepared you for the situation. Novel situations, in this view, demand a dual feat, constructing an adequate response while simultaneously overcoming the inertia of previous learning, the legacy mindset that interferes with the construction. Learning drills in responses from the outside; understanding decides internally, and it is precisely this internal decision-making that current AI systems lack.

To ground this claim, the editorial assembles a century of psychological theory into a single technical picture. From Gestalt psychology it takes the insight that sensory units exist as segregated wholes before they acquire names or uses, and that this original segregation is what allows the world to appear imbued with meaning. From Piaget it takes the characterization of understanding as a system of operational coordinations that transform objects of thought the way actions transform material objects, treating any observed series as one sector of an unlimited range of possibilities. From William James it takes the image of attention hovering near a desired object until accumulated neural tension breaks through a barrier and the nervous wave pours into a tract that has long been awaiting its advent. And from Donald Hebb it takes the cornerstone idea of neuronal assemblies, groups of neurons acting as the basic functional units of the brain, whose interactions produce percepts that are genuinely new structures rather than mere sums of their parts.

The hard problem, the editors argue, is combinatorial. Meaning production is coextensive with choice, and choices explode combinatorially. An array of just ten objects affords 1,024 possible selections; accounting for permutations, in which A following B differs from B following A, raises the count to 3,628,800. A chess position with 32 pieces yields an astronomically large space of possibilities. Yet the human brain navigates these steeplechase marathons in real time while running on roughly the power of a dim light bulb. The proposed resolution is the Virtual Associative Structures proposal, or VaST, first outlined by Yufik in 1998, which holds that understanding compresses vast combinatorial spaces into minute volumes imbued with meaning.

VaST rests on a thermodynamic intuition. The brain, like any living system, seeks to secure steady energy inflows from energy-carrying stimuli while minimizing caloric costs and dissipative losses. Three mechanisms carry out this minimization. Neuronal packets self-organize in associative networks when tightly associated subnets fold into functional units, separated from their surroundings by energy barriers much as surface tension holds a raindrop or a snowflake together. Relational neurons link these packets across indefinite stretches of the stimulus stream. And complexes of coordinated packets form models, hierarchical structures in which upper-level neurons facilitate coordination among lower-level packets. When packets and models match incoming streams, the combinatorial space collapses, reducing intractable multitudes of possible combinations to a handful of meaningful candidates. Crucially, the editors note, spurious alternatives are not rejected; they simply remain beyond the boundaries of awareness.

The editorial grounds these abstractions in experimental findings. Studies of motor control show attentive rotation of population vectors in the motor cortex, while imaging work on the frontopolar cortex points to neurons specialized for apprehending relations among structured elements. Research on packet-based communication in the cortex, in which neurons emit structured bursts of activity, lends further support to the idea that packets serve as core functional components of cognition. The editors also cite classic perceptual learning experiments in monkeys: when animals actively attended to a touched finger, the cortical territory representing that finger expanded to more than twice its usual size, but no such expansion occurred in monkeys touched identically while attending elsewhere. Experience, in other words, reshapes the energy landscape only when attention is engaged.

From these pieces the editors build a synthesis that connects several major theoretical frameworks. VaST aligns closely with Karl Friston’s Active Inference Theory, in which brains minimize variational free energy, the discrepancy between predicted and received inputs. It also complements Yann LeCun’s Energy-Based Models, which minimize an energy function measuring compatibility between inputs and outputs while inferring latent variables, such as the correct segmentation of a sentence into words, that belong to neither. The editors propose that the latent variables in energy-based models correspond to the latent relational variables in VaST, and that the two minimization criteria are mutually reinforcing: improved compatibility entails prediction error reduction and vice versa. Both, they argue, are subsumed by a single deeper principle, maximizing energy inflows while minimizing costs and dissipative losses.

To illustrate the difference between perception, learning, and understanding, the editors turn to chess. A chimpanzee reaching for fruit must coordinate perceptual packets but does not understand the task; it would never envision that shorter sticks demand higher boxes. A chess player solving a checkmate problem, by contrast, manipulates a virtual structure, a superposition of all possible king-and-rook-against-king games that guides the execution of any particular game. In such a model, thinking of moving one piece is inseparable from awareness of how that move constrains the others. Once formed, the model becomes a unit in a larger repertoire, and each new model eliminates degrees of freedom in its constituents while acquiring its own identity. The editors argue that submitting machine outputs to Turing-test-style evaluations amounts to playing peekaboo with oneself, since the underlying process is either knowable from a chess book or readable in the code; the real question is what biological mechanisms produce understanding and how they can be approximated in tractable procedures.

The editorial closes with conversation starters for the next volume, ranging from the Libet phenomenon, which the editors reinterpret as the energy cost of bringing packet contents into awareness rather than as evidence against free will, to a speculative alignment between Piaget’s co-instantaneous coordination and Roger Penrose’s quantum-entangled cytoskeletal states, and to the observation that the Latin cogitare, to think, literally means to shake together. On their account, propagating waves of cortical activity shake neuronal packets together the way vibration sorts mixed nuts, driving the system toward Ockham-optimal packings. Whatever the fate of these individual hypotheses, the editorial’s central challenge to the AI community is unambiguous: deep learning compresses automatisms, but only self-organizing structures that create meaning, operate on minuscule energy budgets, and can explain themselves will cross the threshold from systems that perform to systems that understand.

Subject of Research: Neurobiological mechanisms of human understanding and their implications for achieving artificial general intelligence

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

Article References: Editorial: Neurobiological foundations of cognition and progress toward artificial general intelligence. (n.d.). https://doi.org/10.3389/fnsys.2026.1967487

Image Credits: AI Generated

DOI: 10.3389/fnsys.2026.1967487

Keywords: artificial general intelligence, machine understanding, neuronal packets, self-organization, VaST, active inference, energy-based models, Hebbian assemblies, cognitive neuroscience, combinatorial search, Piaget, Frontiers in Systems Neuroscience