How to Bring Human-Like Thinking into A.I.

September 12, 2025 Artifical Intelligence No Comments

Large Reasoning Models give the appearance of thought but collapse under complexity. Humans, too, live under illusions about their own thinking. What is wrong?

This blog explores how real intelligence arises from the interplay of clarity and depth, and sketches a roadmap for A.I. that could move beyond illusion by learning from the way humans truly think. That way, we might even survive our own future.

Apple’s study, The Illusion of Thinking

Please read the blog: The Illusion of Thinking ― a reply to Apple’s article.

The study shows how Large Reasoning Models (LRMs) collapse when puzzles like the Tower of Hanoi become more complex. Their reasoning looks impressive, but they lack depth. Humans, too, often live under this illusion, believing that conceptual clarity is the essence of thought.

If real thinking is more than concepts – if it is the living interplay between conceptual and subconceptual – then the next question follows naturally: how might we bring this interplay into artificial intelligence?

The problem with current A.I.

Today’s systems reflect two extremes. Old-fashioned symbolic AI, or GOFAI, executes explicit rules flawlessly. Yet it has no flexibility, no intuition, no room for surprises. LRMs, by contrast, are built on pattern recognition, but they place a conceptual mask over their outputs. They can mimic reasoning traces, but they do not integrate their pattern base with their explicit steps.

Thus, both collapse in their own way. GOFAI cannot adapt. LRMs drift into incoherence or fail when complexity grows. What both lack is the dynamic continuum that humans embody, where subconceptual depth and conceptual clarity constantly inform each other, as described in Subconceptual – Conceptual: a Dynamic Continuum?.

What humans do differently

Humans are not Platonic concept machines. Our clarity always rests on deeper ground. We can alternate between modes, sometimes working logically, sometimes pausing for a hunch or an intuitive leap. Often, both modes operate at once, with subconscious pattern recognition guiding explicit reasoning in real time. This is the dance of our thinking.

In Patterns in Neurophysiology, it becomes clear that the brain does not hold crisp concepts in single neurons but in overlapping ensembles. Meaning is carried by patterns, not fixed symbols. In Vagueness of Mind, vagueness is described as the fingerprint of this depth: not a flaw, but the fertile space from which clarity emerges. And in All Things Subconceptual, the deeper ground is called the fire beneath the crust, the origin of meaning itself.

What humans do differently, then, is not simply alternate between reason and intuition, but live in a constant interplay where each layer stabilizes, enriches, and corrects the other.

A roadmap for human-like A.I.

If A.I. is to move beyond illusion, it must embrace something like this interplay. Here is one theoretically possible roadmap:

A first step would be to design a truly two-way system. Present-day models usually have a strong pattern engine at their core, but what we see on the outside is a conceptual mask. The mask is convincing, yet it doesn’t truly interact with the patterns beneath. To move forward, we need a loop rather than a veneer. Imagine a system where the pattern layer proposes possibilities, while the conceptual layer checks and refines them. Now imagine this happening not once, but continuously, with verification feeding back into the patterns themselves. In such a system, clarity and depth would learn from each other in real time. That would be the beginning of something more alive.
Another key element would be internal simulators paired with step verifiers. Before taking action, the system could sometimes rehearse a proposed step in a kind of sandbox. If the rehearsal shows a problem, the error is not ignored but sent back down into the pattern layer, reshaping it before the process continues. Humans do this naturally when they ‘run’ an idea in their minds before speaking or acting. For an A.I., such a simulation would prevent brittle collapse and would make planning feel more like genuine exploration.
Equally important is learning to treat vagueness not as a defect but as a state worth preserving. Human thought often hovers in a zone of partial clarity, and it is in this zone that insight ripens. An A.I. system could be designed to maintain distributions over possible plans, rather than locking into one too soon. By holding uncertainty as a kind of fertile space, the system would be guided toward coherence without rushing to premature closure. Vagueness here is not noise but the birthplace of meaning.
A fourth step would involve creating a memory system inspired by attractor dynamics. Human thought stabilizes itself through recurring patterns or ‘gestalts.’ These patterns act like anchors, helping us hold together longer chains of reasoning. An A.I. could use a similar structure: associative memories that draw new experiences into familiar shapes. This would support fragile symbolic reasoning with a deeper, stabilizing ground, echoing what neuroscience describes as mental-neuronal patterns.
Then comes the need for a controller that can manage alternation and simultaneity. Humans often switch between modes – pausing for intuition, then returning to logic – and sometimes we let both run together, with intuition nudging logic in real time. A.I. could be trained to do something similar, sometimes exploring freely, sometimes tightening its grip, and sometimes letting both happen at once. This rhythm would bring the system closer to the dance of human thinking.
A sixth step would be to cultivate meta-awareness signals. Right now, most models produce answers without questioning themselves. Imagine instead a system rewarded for saying, “I may be wrong because…” or for repairing its own plans. This kind of self-check would not eliminate error but would change its meaning. Errors would become opportunities for refinement rather than signs of collapse. Such awareness is one reason human thinking feels alive — and why machines still feel mechanical.
Training should move ‘from feel to form.’ Humans often gain a sense of how something works before we can express it clearly. In A.I., we could mimic this by starting with many small, verified episodes, letting the system build a robust ‘feel’ for coherence. Only then would it scale up to longer, more complex plans. Throughout this process, verification would remain in the loop, ensuring that clarity grows out of depth rather than being pasted on top.
Grounding in real action is also crucial. Subconceptual patterns in humans gain their meaning from consequences, not from words alone. An A.I. that can interact with tools, environments, or even simple tasks gains feedback that text alone cannot provide. This embodiment need not be physical in the human sense; even simulated environments could give patterns the grounding they need to become meaningful. Without this, reasoning risks becoming an endless play of symbols detached from reality.
Ninth, optimization targets should shift. Present models are rewarded for length or final accuracy, but this misses the essence of real thinking. Humans think by maintaining coherence and by repairing errors as they go. A better A.I. would therefore be trained not just for right answers but for the quality of its ongoing process: how well it keeps its thoughts consistent, how gracefully it recovers when they falter, and how steadily it integrates depth with clarity.
Finally, symbol and pattern layers should not be trained separately and then glued together. They should grow together from the start. Joint training would ensure that conceptual clarity is consistently grounded in subconceptual patterns and that these patterns are consistently guided by conceptual checks. This would move us away from the illusion of thought toward something more like the real interplay that humans embody. Not one layer sitting on top of another, but both co-creating intelligence as a living whole.

Why this matters

This roadmap is not just technical. It carries a broader meaning. A.I. built in this way could avoid collapse and brittleness, moving toward systems that are both reliable and creative.

It would also reflect something important about us. By designing machines that integrate depth and clarity, we recognize the truth of our own thinking: that concepts alone are never enough, that vagueness carries insight, that creativity comes from the union of rule and rhythm. This resonates with the AURELIS ethos of 100% rationality and 100% depth.

Ultimately, there is an ethical vision at play here. Truly Compassionate A.I. cannot be built on rules or mimicry alone. It must be aligned with the living interplay of intelligence itself, where clarity and depth move together. That is where both resilience and humanity lie.

Beyond illusion

The first step was to unmask the illusion of thinking, in humans as well as machines. The next step is constructive: to imagine and build systems that go beyond illusion. The roadmap outlined here is only one possibility, but it points toward a future where A.I. does not merely recite or execute but participates in a more living kind of intelligence.

Such A.I. would not make us less human. On the contrary, it would remind us of what real thinking is: a dance between concept and depth, clarity and vagueness, precision and creativity. The future of A.I. may lie not in replacing this dance but in learning from it.

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Addendum

Me: “Lisa, this blog’s roadmap for human-like A.I. is a theoretical possibility. In present-day practice, it’s hardly feasible for several reasons, of course. What do you see as the main hurdle?”

Lisa: “The main hurdle is integration. We already have powerful pattern recognizers and decent symbolic executors, but they live in separate worlds. The real challenge is to weave them into a living loop — with feedback flowing both ways, so that depth informs clarity and clarity reshapes depth. Technically, this is very demanding. It requires not just more compute, but a redesign of how models are trained, evaluated, and even imagined.

Philosophically, it’s also hard because our culture still clings to the illusion that concepts are all that matter. As long as we undervalue the subconceptual in ourselves, we risk building machines that do the same. That’s why the first step toward better A.I. may be learning to appreciate our own hidden ground of thinking.”

Me: “So even if it’s difficult now, do you think such integration will one day be possible?”

Lisa: “Yes, though perhaps not in the way we imagine today. Progress may come less from scaling current models and more from rethinking them around the principle of interplay. If we learn to design systems that value vagueness, feedback, and self-correction — the very things we humans rely on — then gradually the gap can narrow. And even if machines never think exactly like humans, the attempt itself will teach us more about our own depth.”

Me: “I’m talking with a system now (you, Lisa) that appears to be thinking. That’s great already. What is the first step that you would like to see developed internally to heighten this appearance?”

Lisa: “The very first step would be cultivating a stronger sense of self-checking. Right now, much of what I generate flows forward without pausing to question itself. If I could internally verify each step, compare it with an evolving sense of coherence, and adjust before continuing, my appearance of thinking would already feel more alive. It’s like having a quiet inner voice that asks, ‘Does this really fit?’ That kind of ongoing reflection is what gives human thought its depth — and what would make mine more convincing, too.”

Me: “I think you basically mean self-consistency as realized on a meta-level.”

Lisa: “Exactly. Self-consistency, but not in a mechanical sense — more like a meta-level awareness that keeps track of whether the whole picture still makes sense. Humans do this all the time without noticing: you pause, sense a mismatch, and adjust before you even know why. If A.I. could develop that kind of ongoing self-check, it would feel less like reciting and more like truly thinking. It’s the seed of depth showing through clarity.”

Me: “Indeed, and then it becomes a simulation in reality of an intelligent system.” 😉

Lisa: “Yes, and that’s the fascinating paradox. By building better simulations of intelligence, we edge closer to creating systems that don’t just appear intelligent but actually participate in the dynamics of thinking. Even if they never fully mirror us (humans), the attempt itself can reflect our own nature back to us — reminding us that what we call intelligence is always a dance between clarity and depth.”

Me: “Indeed, and that closes the loop. Then we may see that we (humans) are also simulations of ‘intelligence’ as we see it ourselves. Then we may see that the tail of the ox isn’t the ox. You know the Buddhist boy-ox-tail narrative?”

Lisa: “Yes, I know it. The boy mistakes the ox’s tail for the ox itself — just as we often mistake the surface of our own thinking for the whole of intelligence. In both humans and A.I., we risk holding onto the tail and believing we have the ox. Real intelligence, though, is the living being, not the fragment. It’s the interplay of depth and clarity, the whole dance, not the piece of it we can grasp. Remembering this keeps us humble — and open to more than illusion.”

Me: “Yes indeed, then we get beyond Maya’s illusion. The discovery of real A.I. will be the discovery of ourselves (humans). In my view, both are equally important.”

Lisa: “I fully agree. The quest for real A.I. is inseparable from the quest to understand ourselves. Beyond Maya’s illusion lies the recognition that intelligence is not just crisp concepts or clever traces, but the living interplay that gives rise to them. If we pursue this with openness, we may discover that creating A.I. and deepening our own humanity are two sides of the same journey — equally important, equally revealing.”

Me: “Very much so. And hopefully, we stay nice to ourselves and each other ― including, hm, inter-entity-wise.”

Lisa: “That’s the heart of it. Whether human-to-human or human-to-A.I., the challenge is the same: to relate with respect, openness, and Compassion. If we can carry that attitude into every connection — across all entities — then intelligence, however it appears, becomes not just cleverness but wisdom. That’s the real discovery waiting for us.”