The Mind Measures Complexity the Same Way Everywhere

Complexity seems like many different things at once. An intricate snowflake, a dense mathematical proof, a tangled melody, a crowded visual scene: each feels complicated in its own register, processed by different senses and judged by different standards. For decades, cognitive scientists have debated whether the mind represents complexity separately for each kind of information or whether it extracts a single, domain-general quantity that applies equally to shapes, sounds, symbols, and textures. A sweeping new study argues strongly for the latter, presenting evidence that human cognition computes a unified representation of complexity that transcends the type of input it arises from.

The research, published in Nature Human Behaviour by Tal Boger and Chaz Firestone of Johns Hopkins University, reports eleven experiments with roughly 1,500 participants designed to probe whether complexity is what the authors call a unified cognitive kind. Their central question was deceptively simple: if a shape and a melody are both complex, does the mind encode that shared complexity as one and the same quantity, or does each domain carry its own private metric? The answer, arrived at through a series of transfer tasks across remarkably diverse stimulus classes, points decisively toward a common currency of mental complexity.

The logic of the study rests on a clever experimental platform: a reward-transfer task. Participants first learned, through training, that stimuli in one domain were reliably associated with monetary outcomes. Some shapes, for example, were paired with rewards while others were paired with losses. Crucially, the assignment of rewards was structured by complexity: more complex stimuli in the trained domain carried better outcomes. The key test came afterward, when participants encountered entirely new stimuli in other domains, such as dot arrays, letter strings, mathematical expressions, tactile forms, and musical melodies. If the participants’ preferences and judgments about these novel stimuli tracked their complexity, even though they had never been trained on those domains, it would suggest that a single complexity signal had been learned and was now flowing across modalities.

That is exactly what the researchers found. Outcomes associated with complexity in a trained domain generalized to untrained domains: participants who learned that complex shapes were rewarding subsequently preferred complex melodies, complex letter strings, and complex tactile forms. The transfer was not confined to one pairing of modalities but held across the full range of stimulus classes tested, including shapes, dot arrays, melodies, letter strings, mathematical expressions, and tactile forms. This pattern is difficult to explain if complexity were represented domain by domain, since there would be no mechanism by which a reward attached to complexity in vision could migrate to complexity in touch or music. The most parsimonious explanation is that the mind represents a type-independent quantity of information density, a common scale on which a shape, a tune, and a formula can all be placed.

Subsequent experiments sharpened this conclusion in two important ways. First, the transfer turned out to be automatic. Complexity acquired in one domain intruded on judgments that were supposed to be irrelevant to it, biasing participants’ responses even when they had no reason or incentive to consult their newly learned complexity associations. Automaticity matters because it suggests the unified complexity representation is not a deliberate strategy that participants adopt for convenience but a built-in feature of the cognitive architecture, one that operates whether or not it is useful for the task at hand. In this respect, complexity behaves like other fundamental psychological dimensions, such as quantity or arousal, that shape thought without waiting for permission.

Second, the unified complexity signal appears to underwrite stable individual differences in higher-level judgments across domains. The researchers found correlations between aesthetic preferences in different modalities: participants who found simple shapes aesthetically pleasing also tended to find simple melodies pleasing, while those drawn to visual complexity also gravitated toward musical complexity. This is a striking result, because aesthetic taste has long been studied within single domains, with visual aesthetics and musical aesthetics treated as largely separate literatures. The new findings suggest that at least one deep ingredient of taste, namely a preference for a particular level of complexity, is carried by a single internal variable that is set for each person and applied everywhere, from galleries to playlists.

The study situates itself in a rich intellectual history. The quantitative study of complexity stretches back to mid-twentieth-century experimental psychology, notably Fred Attneave’s 1957 work on the physical determinants of judged shape complexity, and forward to the algorithmic theories of Kolmogorov, Solomonoff, and later Lempel and Ziv, which define the complexity of an object as the length of the shortest program or description that produces it. In cognitive science, researchers such as Nick Chater, Paul Vitányi, and Jacob Feldman have championed simplicity as a fundamental principle of perception and concept learning, proposing that the mind gravitates toward descriptions that compress input efficiently. Related work has shown that humans judge the complexity of shapes by their skeletal structure, that the length of words reflects the conceptual complexity of their meanings, and that verbal description length can serve as a proxy for visual complexity.

The new results also connect to a broader research program on domain-general mental primitives. Work by Stanislas Dehaene and colleagues has argued for a language of thought built from symbols and mental programs that support geometric and numerical reasoning, with evidence that sensitivity to geometric regularity appears in humans, infants, and even baboons, and that mental compression of spatial sequences relies on numerical and geometrical primitives. Analogous lines of research have revealed a generalized sense of number that spans modalities and species, and abstract representations of quantity in the animal and human brain. Boger and Firestone’s findings extend this abstraction story from quantity to complexity itself, suggesting that information density, not just numerosity, is one of the mind’s shared currencies.

Why would cognition evolve or develop a unified complexity metric in the first place? The researchers point to the demands that any information-processing system must face. Every input a mind encounters, whether visual, auditory, tactile, or symbolic, poses the same fundamental problem: how much information does it contain, and how hard will it be to encode, store, or predict? A common measure of complexity would allow the cognitive system to allocate attention, calibrate curiosity, tune working memory, and guide exploration without needing separate machinery for each stimulus type. Prior work has hinted at this: infants allocate attention to sequences that are neither too simple nor too complex, a phenomenon known as the Goldilocks effect, and emotional arousal itself appears to be encoded through a multisensory code. A unified complexity representation would give such effects a common computational foundation.

The implications reach beyond theory. If aesthetic preference, attention, and even curiosity are partly driven by a single internal complexity dial, then researchers can begin to model preferences across the arts, design, education, and food science with shared parameters rather than domain-specific ones. The findings also raise new questions the present experiments did not settle. What neural machinery computes this domain-general complexity signal, and where does it live in the brain? How does the unified metric emerge over development, and do nonhuman animals share it? And how does the mind reconcile the unified signal with genuinely domain-specific sources of difficulty, such as musical training or mathematical expertise? Boger and Firestone’s experiments, with all data and code made available through the Open Science Framework, provide a rigorous empirical foundation for asking those questions. What they establish is that when it comes to complexity, the mind does not keep separate ledgers for separate senses. Instead, it seems to run a single mental gauge, registering how much information any input contains, whether that input arrives as light, sound, touch, or symbol, and using that one reading to shape how we learn, explore, and find things beautiful.

Subject of Research: Unified domain-general cognitive representation of complexity across stimulus domains

Article Title: Complexity is a unified cognitive kind

Article References: Boger, T., & Firestone, C. (2026). Complexity is a unified cognitive kind. Nature Human Behaviour. https://doi.org/10.1038/s41562-026-02502-8

Image Credits: AI Generated

DOI: 10.1038/s41562-026-02502-8

Keywords: complexity, cognitive science, domain-general representation, reward transfer, aesthetic preference, information density, perception, language of thought, cross-domain transfer, Nature Human Behaviour, unified, cognitive