Armin Zundel

Introduction: The Paradox

Aphantasia — the inability to voluntarily generate mental images — affects roughly 2–5% of the population . Ask someone with aphantasia to picture a sunset, and they will report knowing what a sunset is, remembering that they have seen one, perhaps even describing it in detail — but seeing nothing. The mind’s eye stays dark.

And yet, a striking paradox sits at the heart of the condition: the majority of people with aphantasia report rich, visual dreams. In Adam Zeman’s original 2015 study of 21 congenital aphantasics, 17 described visual imagery in their dreams, and the dissociation between absent voluntary imagery and preserved involuntary dream imagery was statistically significant . A later study with around 2,000 aphantasic participants confirmed the pattern at scale: 63.4% reported visual imagery in their dreams . As Zeman puts it, most aphantasics are confident they dream visually — they simply experience imagery in a brain state that is involuntary .

This article is about that subgroup: aphantasics whose sleeping brain can do what their waking brain cannot.

Why Dreams Escape the Blockade

The leading explanation is that voluntary and involuntary imagery rely on partially distinct neural networks. Zeman’s team concluded from their data that the two forms of imagery involve „partially but incompletely overlapping neural networks“ — a finding supported by the famous case of patient MX, who lost voluntary imagery after a cardiac procedure but later recovered involuntary flashbacks and visual dreams while voluntary imagery remained impaired .

When you deliberately try to imagine something while awake, the process is driven top-down: prefrontal control regions issue the „command“ to generate an image, and visual cortex areas construct it. In aphantasia, this top-down control pathway appears to be the weak link. During REM sleep, however, imagery generation works differently — it is driven bottom-up, spontaneously, without executive direction. The dreaming brain doesn’t need to ask for images; they simply arrive. The machinery of perception-like experience is intact; only the switchboard that normally operates it on demand is disconnected.

This has even created waves in the philosophy of mind: aphantasics‘ dream reports pose an empirical challenge to theories claiming that dreams are essentially imaginative experiences in the same sense as waking imagery. The resolution proposed by philosophers is that dreams involve non-voluntary forms of imagination — which is exactly what aphantasics retain .

Important Nuances: Dreaming Aphantasics Are Still Not „Normal“ Dreamers

Even within the dream-visualizing subgroup, dreaming differs from that of typical imagers. Large-scale questionnaire studies show that aphantasics overall report:

• Fewer remembered dreams and less vivid sensory content across modalities
• Lower lucidity — less awareness and control during dreams
• More „thinking“ in dreams — dream content skews toward semantic, narrative, and conceptual content rather than sensory scenes
• Preserved spatial cognition — knowing where things are in the dream, navigating dream space, works fine; what is reduced is the visual „surface detail“

So the picture is graded, not binary. Some aphantasics have cinematically vivid dreams; many have visual but somewhat muted ones; about a third report no visual dreams at all. Researchers now speak of meaningful subtypes: those impaired only in voluntary imagery, versus those impaired in both voluntary and involuntary imagery .

Consequences for Daily Life

For the dream-visualizing aphantasic, waking life is shaped by the voluntary deficit, not the dream capacity. Typical consequences include:

Memory. Autobiographical memory tends to be reduced — past events are known as facts rather than re-lived as scenes. Many aphantasics describe their past as a kind of database rather than a film .

Face recognition and visualization-dependent tasks can be harder, though aphantasics develop strong compensatory strategies: verbal, logical, spatial, and schematic representations. Notably, participants in Zeman’s study solved classic imagery tasks (like counting the windows in their home) using „knowledge,“ „memory,“ and „subvisual models“ — and performed successfully .

Emotional processing. Fewer involuntary visual intrusions after stressful events — a potential protective side. Aphantasics are also less shaken by scary stories, since they cannot visualize them .

The strange social position. Dream-visualizing aphantasics occupy a confusing middle ground: they know what visual experience feels like because they have it at night — yet cannot access it at will. Many describe this as standing outside a locked room whose interior they visit every evening.

How Sleep and Dream Work Can Help — Strategies

Here the honest disclaimer first: there are no established clinical protocols for „dream training in aphantasia.“ What follows synthesizes established dream-research methods with the specific cognitive profile of this subgroup.

1. Dream journaling — bridging the two states

Writing down dreams immediately upon waking (before the imagery fades) creates a verbal-semantic trace of a visual experience. For aphantasics, this does two things: it improves dream recall generally, and it lets them study their own visual experiences in the only format their waking mind handles well — language and structure. Over weeks, a journal becomes a dataset of one’s own imagery life.

2. Dream incubation — guided dreaming

Before sleep, deliberately choose a topic, problem, or scene: write it down, describe it verbally, tell yourself „tonight I will dream about this.“ This works with the aphantasic cognitive style — it uses semantic priming, not visualization — and lets the involuntary dream machinery do the visual work the waking mind can’t. People use incubation for creative problems, emotional processing, and skill rehearsal. For an aphantasic who cannot visualize a difficult conversation or a design problem while awake, the dreaming brain offers a working visual simulator at night.

3. Lucid dreaming techniques

Lucid dreaming is a learnable skill in the general population (reality checks, dream journaling, the MILD technique — rehearsing the intention to recognize you’re dreaming), and researchers have explicitly proposed testing whether aphantasics can learn dream control at comparable rates . Since aphantasics report lower baseline dream lucidity , training may be somewhat harder — but for the dream-visualizing subgroup, the imagery substrate is present, so there is no known reason the skill should be unreachable. The payoff would be unusual: lucid dreaming could give aphantasics their only form of voluntary-ish visual experience.

4. Using dreams as emotional and creative input

Many aphantasics report compensatory strengths in abstract, verbal, and logical domains . A practical strategy: treat the morning dream residue as raw material. Capture the structure of the dream (what happened, where, in what sequence) rather than trying to hold the images, then work with it analytically — for creative writing, design, or processing emotionally charged material that the waking mind can only handle abstractly.

5. Sleep hygiene as imagery hygiene

Since the visual faculty operates mainly during REM-rich sleep, anything that fragments sleep (alcohol, irregular schedules, late-night screens) disproportionately costs this subgroup their one window into imagery. Consistent sleep timing and enough total sleep — REM concentrates in the last third of the night — directly protect dream access.

6. Managing expectations with guided imagery

A caution: standard guided-imagery relaxation techniques („picture yourself on a beach“) often don’t work for aphantasics when awake and can cause frustration. Sleep- and dream-based approaches are the better fit for this subgroup precisely because they bypass the broken voluntary pathway instead of demanding it.

Conclusion

For aphantasics who visualize in dreams, the condition is best understood not as an absence of imagery but as an absence of access — the cinema runs every night; only the projectionist’s daytime shift is unmanned. Research increasingly treats this dissociation not as a curiosity but as a window into how imagery works in all of us . And practically, the dreaming brain is a resource: through journaling, incubation, and lucid-dream training, dream-visualizing aphantasics can route creative, emotional, and problem-solving work through the one channel where their visual mind is fully alive — and bring the results back, in words and structures, into the waking world where they live.

At its simplest, knowledge is knowing that something is the case. Understanding is knowing why and how it fits together. If knowledge is owning a pile of bricks, understanding is having a blueprint of the building and the skill to modify it.

Philosophically and practically, real understanding has several key dimensions:

1. Understanding as a Coherent Mental Model
Understanding isn’t a list of facts; it’s a dynamic, interconnected model in your mind. You understand something when you see how its parts relate to one another and to the whole. Isolated facts are fragile; a mental model is robust. If you forget one piece, you can reconstruct it from the surrounding structure. A student who has memorized the steps of mitosis has knowledge. A student who sees it as a dance of molecular machines ensuring faithful genetic replication has a model—and understanding.

2. The Ability to Explain and “Teach”
A litmus test: if you truly understand, you can explain it clearly, simply, and in multiple ways. You can unpack complexity for a novice without hiding behind jargon. Richard Feynman believed that if you can’t explain something in simple language, you don’t understand it. The act of explaining isn’t just the result of understanding; it’s a constituent of it.

3. Grasping Causality and “Why”
Understanding goes beyond correlation to causation. It’s not just knowing that a higher money supply correlates with inflation; it’s seeing the causal mechanism: more money chasing the same goods. This grasp of why allows you to reason about novel situations. If I change this one variable, what will happen? That’s a sign of deep understanding.

4. The Power of Transfer and Application
True understanding is flexible. You can take a principle learned in one domain and apply it to a completely new one. Understanding supply and demand in economics helps you understand dating dynamics or traffic congestion. Understanding natural selection helps you understand the spread of ideas (memes). This analogical transfer is a hallmark of understanding; it shows you’ve extracted the deep, abstract pattern.

5. Seeing Context and Limits
No understanding is complete without seeing its boundaries. You understand a scientific theory not just by knowing what it explains, but by knowing what it doesn’t explain—where it breaks down. Newtonian physics works perfectly until you deal with very high gravity or speed; then you need Einstein. Understanding the limits of a concept is a higher-order form of wisdom.

6. The Hermeneutic Circle (The Dance of Part and Whole)
This idea from philosophy of interpretation says that you understand the whole through its parts, but you also understand the parts through the whole. When you read a complex book, you understand a sentence by the chapter, but you understand the chapter by its sentences. It’s a constant back-and-forth. Understanding isn’t a single “aha!” moment; it’s an iterative, spiraling process of refinement.

7. Empathy and Perspective-Taking
Understanding isn’t only for things and systems; it’s for people. Understanding another person means reconstructing their model of the world—their fears, desires, and reasoning—so well that their actions make sense from the inside. You don’t have to agree, but you can simulate “why this makes sense to them.”

In essence, understanding is the cognitive state in which a subject has integrated a body of information into a coherent and flexible mental model, enabling explanation, prediction, and meaningful transfer. 

In 1985, Peter Naur — Turing Award winner, co-author of the Backus–Naur Form — published a short essay with an unassuming title: Programming as Theory Building. It is rarely cited in engineering handbooks, yet it quietly reframes what software development actually is. And, perhaps unintentionally, it offers one of the best arguments for why theoretical computer science is not a luxury for engineers, but a necessity.

Naur’s claim: the code is not the product

Naur’s provocation is simple: programming is not primarily the production of program texts. The executable artifact — the source code, the documentation, the tests — is secondary. The primary activity is the building of a theory in the minds of the programmers.

By „theory“ Naur means something close to Gilbert Ryle’s notion of knowing how rather than knowing that. A programmer who holds the theory of a system can answer questions that no document contains: why the architecture looks this way and not another, which modifications are cheap and which are catastrophic, which user requests fit the system’s grain and which violate its deepest assumptions. The theory is what lets a developer respond intelligently to a new situation — one that was never written down anywhere.

This explains phenomena every practitioner recognizes but classical engineering metaphors cannot:

• Programs „die“ when their team leaves. The code still compiles, the documentation is still on the shelf — yet the system becomes unmaintainable. Naur’s diagnosis: the program’s life is the theory held by its programmers. Lose the people, lose the theory, lose the program. The text is merely a fossil of it.
• Documentation can never be sufficient. Not because engineers are lazy writers, but because a theory in Ryle’s sense is in principle not fully expressible in text. You can record conclusions, but not the capacity to derive new conclusions in unforeseen circumstances.
• Rebuilding is cheaper than reviving. A new team often fares better rewriting a system than inheriting it — because modification requires the theory, and reconstructing a theory from artifacts alone is brutally expensive.

From Naur to theoretical computer science

Here is where the argument turns. If engineering competence consists in holding theories, then the question for engineering education and practice becomes: where do good theories come from?

One answer is experience — Naur’s own view. Theories of specific systems are built by living with those systems. But there is a second, complementary answer, and this is the role of theoretical computer science: TCS provides the general theories within which situation-specific theories can be built.

Consider what a theoretical education actually deposits in an engineer’s mind:

• Computability and complexity theory teach not facts but judgments: which problems resist efficient solution in principle, so that one stops searching for the algorithm that cannot exist and starts reformulating the problem. That is theory in Naur’s sense — a capacity for intelligent response — applied across all possible systems.
• Formal semantics and type theory provide the vocabulary in which claims about program behavior can be stated precisely at all. An engineer without this vocabulary can feel that a design is unsound; an engineer with it can say why, and convince others.
• Automata, logics, and models of concurrency are not academic decorations. They are the shapes of thought — the compressed experience of decades — that let an engineer build a local theory of their system faster, because the general theory is already in place.

Naur noted that a theory holder can do things a mere text-reader cannot. The same holds one level up: an engineer with theoretical foundations can approach a novel problem — a domain, a failure mode, a performance anomaly nobody has seen before — and generate understanding where the purely trained practitioner can only pattern-match against what they have already encountered.

Two kinds of theory, one practice

It helps to be precise about the relationship. Naur’s theory building is particular: the theory of this payroll system, this compiler, this embedded controller. Theoretical computer science is general: theories of computation, information, and structure as such. Neither substitutes for the other.

The general theory without the particular is empty formalism — proofs about nothing anyone runs. The particular theory without the general is fragile craft — intuition that shatters the moment the problem shifts shape. Engineering, at its best, is the continuous act of instantiating general theories into a living, shared, particular theory of a system — and feeding insights back.

This also reframes the eternal curriculum debate. „Should engineers study theory?“ is the wrong question, as if theory were a subject among others. On Naur’s account, all competent engineering is theory building. The only question is whether engineers build their theories with the best conceptual tools available — or rediscover, at great expense and one project at a time, what the theorists already knew.

A closing thought

There is something humbling in Naur’s view. If the real product lives in people’s heads, then engineering is irreducibly human: a matter of shared understanding, cultivated over time, impossible to fully hand over, automate, or file away. Theoretical computer science does not threaten this human core — it strengthens it. It gives minds better theories to build with.

The code is temporary. The theory is the program. And the theories behind the theory — that is what TCS is for.

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References: Naur, P. (1985). „Programming as Theory Building.“ Microprocessing and Microprogramming, 15(5). Ryle, G. (1949). The Concept of Mind.