Abstract

The concept of “good taste” in design is often mistaken for a subjective preference, yet cognitive science defines it as a refined ability to recognize pattern, harmony, and cultural relevance. While generative artificial intelligence can produce aesthetically pleasing images based on statistical probability, it lacks the biological and experiential foundation required to exercise aesthetic judgment. This article explores the neurological disconnect between algorithmic generation and human curation. It argues that aesthetic sensitivity, or the “design eye,” is developed through embodied cognition—the physical interaction with materials such as watercolor, film photography, and clay. Research indicates that the sensorimotor experiences gained from analog practice create the neural frameworks necessary for evaluating digital outputs. Algorithms tend to regress toward the mean, producing safe, derivative visuals. In contrast, human designers trained in physical media understand nuance, texture, and imperfection. This distinction emphasizes why university design curriculums continue to prioritize manual craft. The ability to discern why a design works remains a human function, ensuring that the designer remains the master of the tool rather than its operator.

Training the ‘design eye’: why algorithms can’t generate good taste

The rise of artificial intelligence in the creative sector has automated the production of pixels, but it has not automated the production of taste. High school students entering university programs often view software proficiency as the ultimate goal of design education. However, the software is merely a vehicle for execution. The core value of a professional designer lies in the “design eye,” a term describing the cultivated ability to judge proportion, color balance, and compositional tension. This ability stems from physical experience rather than digital processing.

The statistical trap of AI aesthetics

Artificial intelligence models function as statistical engines. They analyze billions of images to determine the most probable arrangement of pixels for a given prompt. This process inherently biases the output toward the “average” of the dataset. While the results are often polished, they frequently lack the distinctiveness that characterizes high-level design.

Manovich (2021) discussed this phenomenon in the context of cultural analytics, noting that while computational tools can analyze vast amounts of cultural data, the generated output tends to mimic existing patterns rather than disrupt them. Good taste often involves a deliberate deviation from the norm to create visual interest. An algorithm follows rules derived from the past. A designer with a trained eye knows when to break those rules to create something relevant for the future.

Embodied cognition and material practice

The development of aesthetic judgment is linked to embodied cognition. This psychological theory posits that cognitive processes are deeply rooted in the body’s interactions with the world. When a student mixes watercolor on paper, they learn about opacity and saturation through physical feedback. They watch how the pigment settles into the texture of the sheet. This tactile experience builds a mental library of visual textures.

Gallese and Di Dio (2012), researchers in neuroscience, argued that aesthetic experience is not just a visual process but a sensorimotor one. When we look at a texture in a digital image, our brain simulates the sensation of touching it. If a designer has never physically engaged with real materials, their ability to judge the authenticity and emotional impact of a digital texture is diminished. Analog practices like darkroom photography or clay modeling train the brain to recognize the nuance of light and form in ways that manipulating a slider on a screen cannot.

The role of imperfection

Digital tools are designed for precision. Vector lines are perfectly straight; hex codes render colors with absolute consistency. However, human visual preference often leans towards slight imperfection and organic variation. The “design eye” recognizes that a layout might need to be optically centered rather than mathematically centered to look correct.

Research in experimental aesthetics suggests that computer-generated symmetry can sometimes evoke a sense of coldness or artificiality compared to human-generated art. Chamberlain et al. (2018) found that while computer algorithms can successfully emulate artistic styles, human observers often perceive a lack of intentionality in the work. The human designer introduces intentional subtle flaws or irregularities that add warmth and approachability to a brand identity or illustration.

Curriculum and the cultivation of judgment

University programs in visual communication design and new media emphasize manual craft to build this critical judgment. At institutions like BINUS Bandung, the curriculum integrates analog experimentation to ensure students develop a sophisticated visual vocabulary. A student who understands the weight of lead type in letterpress printing makes better decisions when choosing digital fonts.

The future role of the designer is that of a curator. As AI tools generate hundreds of options in minutes, the designer must possess the taste to select the single option that aligns with the client’s strategic goals. This selection process requires a depth of cultural and material knowledge that algorithms do not possess. The machine generates the options. The human provides the value.

References

Chamberlain, R., Mullin, C., Scheerlinck, B., & Wagemans, J. (2018). Putting the art in artificial: Aesthetic responses to computer-generated art. Psychology of Aesthetics, Creativity, and the Arts, 12(2), 177–192. https://doi.org/10.1037/aca0000136

Gallese, V., & Di Dio, C. (2012). Neuroaesthetics: The body in aesthetic experience. The Encyclopedia of Human Behavior, 2, 687–693. https://doi.org/10.1016/B978-0-12-375000-6.00257-2

Manovich, L. (2021). Cultural analytics. MIT Press.