Exploring Machine Cognition: How Machines Understand

Can a machine truly understand something – or does it merely simulate understanding? This is one of the most provocative questions in the philosophy of technology today. As artificial intelligence grows more sophisticated, it forces us to reexamine what we mean by cognition itself. Answering it requires more than computer science; it demands neuroscience, psychology, philosophy, and the study of representation. Cognition in machines is not a single problem – it is many problems layered on top of each other, and understanding it means working through several competing frameworks at once.

What do we mean by cognition? 🔗

Before asking whether machines can cognize, we need to be clear about what cognition is. Cognitive science examines what cognition is, what it does, and how it works – studying how information is represented, processed, and transformed across faculties like perception, language, memory, reasoning, and emotion, both in biological organisms and in machines. That definition is deliberately wide. It covers everything from recognizing a face to planning a chess move, from remembering a name to drawing an inference. No single discipline owns it. Neuroscience, psychology, philosophy, linguistics, and computer science each offer a partial account – and it is only by combining those accounts that a fuller picture emerges.

This is not just an academic observation. It has direct consequences for how we build and evaluate intelligent machines. If we reduce cognition to one dimension – say, the ability to produce correct outputs – we miss much of what makes cognition genuinely cognitive. The debate over machine cognition is, at its core, a debate about which dimensions matter most.

The computational approach: thinking as symbol manipulation 🔗

The most influential framework for understanding machine cognition is the computational theory of mind (CTM). In its modern form, the theory holds that the human mind is an information-processing system and that cognition is a form of computation. According to the Stanford Encyclopedia of Philosophy, if we treat the mind as a syntax-driven machine, we can explain why mental activity tracks semantic properties in a coherent way – and that explanation does not require mechanisms radically different from those in the physical sciences. In other words, rational thought becomes mechanically explicable.

The foundations go back to Alan Turing, who argued that any symbolic algorithm executable by a human can be replicated by a suitable Turing machine. The theory was proposed in its modern form by Hilary Putnam in 1960 and 1961, with significant development by Jerry Fodor in the decades that followed. Fodor’s key contribution was the idea of a language of thought – the claim that cognition operates over mental representations structured like a language, with compositional syntax. This gave the computational model both philosophical rigor and a blueprint for artificial systems.

The practical impact has been enormous. Philosophical theories of mental representation, such as Fodor’s modularity hypothesis, directly inform the design of symbolic AI systems that emulate structured reasoning. Rule-based expert systems, theorem provers, and early natural language parsers all carry the imprint of this tradition. But the computational model has critics. Fodor himself acknowledged that most real cognition is abductive and global – sensitive to all possibly relevant background beliefs – which creates the notorious frame problem: no purely local, syntactic rule can capture what is contextually relevant.

The neuroscientific approach: starting with the brain 🔗

A second major tradition insists that any adequate model of cognition must begin with actual neural architecture, not with abstract symbol systems. Cognitive computational neuroscience – a rapidly evolving interdisciplinary field – merges principles from neuroscience, psychology, and artificial intelligence to probe the neural mechanisms responsible for complex mental functions such as perception, memory, reasoning, and decision-making.

The neuroscientific approach takes seriously the fact that the brain operates very differently from a classical digital computer. On a twenty-watt power budget, the brain’s algorithms combine statistical and computational efficiency in ways that remain beyond current AI. Recent work in AI and machine learning has begun to explore the intersection between Bayesian inference and neural network models, combining the statistical strengths of the former – uncertainty representation and probabilistic inference – with the representational learning and computational efficiency of the latter. This convergence points toward a more biologically grounded account of machine cognition.

A concrete result of this approach is the development of deep convolutional neural networks (CNNs), whose hierarchical structure mirrors the organization of the visual cortex. Neurobiological discoveries about the hierarchical organization of the visual cortex prompted refinements in convolutional neural networks, which mirror cortical processing. This is not merely metaphor – researchers now routinely test AI models by measuring how well they predict actual brain-activity data, treating biological plausibility as an empirical benchmark rather than a philosophical aspiration.

The psychological approach: behavior, representation, and learning 🔗

Psychology contributes a third lens. Where neuroscience looks at neural implementation and computation looks at formal structure, cognitive psychology focuses on behavior – on what an intelligent system does under controlled conditions, and what internal states are needed to explain that behavior. Current AI design makes extensive reference to human cognitive models, simulating aspects of human mental activity such as attention, encoding, and memory.

The connection runs in both directions. Psychological frameworks like Baddeley’s working memory model and Posner’s attention network theory have been unified within computational modeling, which provides a common language of algorithms and representations across disparate psychological theories. And conversely, large language models are increasingly being used as tools for psychological investigation – performing behavioral tasks at scales impossible for human participants, and generating data that can be compared against human brain activity to test theories of cognition.

The representational approach: what does the machine hold in mind? 🔗

Cutting across all three traditions is the question of mental representation. What are the internal states that mediate between input and output in a cognitive system? Mental representations serve as the mind’s “data structures,” capturing sensory inputs, abstract concepts, and probabilistic beliefs in formats that can be manipulated computationally. These representations can take several forms: symbolic structures (words, concepts), distributed patterns (neural activations), or probabilistic distributions over possible states of the world.

Fodor and Pylyshyn’s language of thought hypothesis argues that cognition involves a symbolic “mentalese” with compositional structure, enabling complex thought. But a powerful objection – the symbol grounding problem – challenges this view: how do symbols in a machine acquire genuine meaning, rather than being mere formal tokens manipulated by rules? A computational model of cognition must be supplemented by a naturalistic theory of content that explains how symbolic or numerical states subject to computational rules gain their semantic content – and this is widely considered an in-principle limitation, not a technical shortcoming that a better algorithm could fix.

Distributed and probabilistic models offer partial responses to grounding by tying representations to sensory data. But critics from the embodied cognition tradition go further, arguing that genuine cognition emerges from sensorimotor interactions with an environment – not from any internal encoding at all. On this view, a machine that processes text in isolation, however sophisticated, is missing something essential about what it means to understand.

The hardware-software debate: is the brain a computer? 🔗

Perhaps the most philosophically charged question in this territory is the hardware-software distinction as it applies to the brain. The analogy is intuitive: the brain is to the mind what computer hardware is to computer software. While the computer metaphor draws an analogy between mind as software and brain as hardware, the computational theory of mind is the stronger claim that the mind actually is a computational system – not merely analogous to one.

Functionalism, associated most strongly with Hilary Putnam, builds on this idea. It holds that what makes a mental state the state it is – a belief, a desire, a perception – is its functional role, not its physical substrate. Consciousness is the computation, and it makes no difference whether that computation runs in neurons or silicon. This view is enormously influential in AI research, as it grounds the hope that machine cognition is not just possible but achievable through the right kind of information processing.

But the analogy breaks down on close inspection. A computer is an intrinsically dualistic entity, with its physical set-up designed not to interfere with its logical set-up. A living organism, by contrast, continuously produces its own structure – synapses, cells, connections – and does not maintain a clean separation between the medium and the message. Neurons are not transistors. They are complex, self-modifying, living entities. The brain does not simply execute a fixed program; it rewrites itself through experience, through synaptic plasticity, through the constant interplay of genetics and environment.

John Searle’s famous Chinese Room argument sharpens this point. Searle argues that simulating cognition is not the same as instantiating cognition – a system can manipulate symbols correctly without understanding what those symbols mean. A machine may pass every behavioral test and still lack the semantic content that constitutes genuine understanding. Whether this objection is decisive remains disputed, but it marks the boundary between two fundamentally different conceptions of what machine cognition could be.

Why a multi-dimensional approach is necessary 🔗

Each of these frameworks – computational, neuroscientific, psychological, representational – captures something real. None captures everything. A satisfactory explanation of the human brain as a cognitive system will ultimately have to satisfy a comprehensive set of explanatory norms, drawing on both mechanistic accounts of how neural processes are implemented and computational accounts of what those processes are doing and why.

This is not mere theoretical pluralism. It has engineering consequences. Achieving complex cognitive abilities – such as understanding abstract concepts, reasoning, and adapting to novel scenarios – remains a major challenge for deep neural networks, despite remarkable progress on narrower tasks. Scaling up model parameters and training data improves performance on benchmarks, but does not automatically confer the kind of flexible, context-sensitive understanding that human cognition displays. The gap between narrow competence and genuine comprehension is precisely where the philosophy matters most.

The debate is also not merely academic. As AI systems take on roles in medicine, law, education, and governance, the question of what these systems genuinely understand – and what they only appear to understand – carries real stakes. A system that manipulates medical symbols without grasping their meaning is not the same as one that comprehends a patient’s condition. Computation is necessary for cognition because cognition does not occur without information processing – but whether it is sufficient remains the central open question in the philosophy of machine cognition.

What do you think? If a machine produces outputs that are indistinguishable from those of a human expert – in medicine, in law, in philosophy – does it matter whether it truly “understands” what it is doing, or is reliable performance enough? And if the hardware-software distinction breaks down for biological brains, does that undermine the very possibility of building genuinely cognitive machines, or does it simply mean we need better models?

References

1. https://en.wikipedia.org/wiki/Philosophy_of_mind
2. https://plato.stanford.edu/entries/computational-mind/
3. https://en.wikipedia.org/wiki/Computational_theory_of_mind
4. https://philarchive.org/archive/NGATCM
5. https://pmc.ncbi.nlm.nih.gov/articles/PMC6706072/
6. https://pmc.ncbi.nlm.nih.gov/articles/PMC9582153/
7. https://marksprevak.com/publications/philosophical-issues-in-computational-cognitive-sciences-06dd/
8. https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2022.878729/full
9. https://link.springer.com/article/10.1007/s11229-020-02568-5
10. https://www.nature.com/articles/s41467-026-71267-5
11. https://link.springer.com/article/10.1007/s11023-018-9468-3