Processes of artistic expression and what we learned

When the Brain Meets the Canvas: The Cognitive Turn

Over the past ten years, art has stopped being a mysterious, purely subjective experience and has started to look a lot like a laboratory experiment. Researchers have built cognitive models that map how we notice a brushstroke, decode symbolism, and feel that surge of emotion. The shift is well‑documented in the review Visualizing the Impact of Art (2020), which notes that “the last decade has witnessed a renaissance of empirical and psychological approaches to art study, especially regarding cognitive models of art processing experience.

What does that mean for us, day‑to‑day? First, it gives us a language for talking about why a particular colour palette feels “warm” or why a composition feels “balanced.” Second, it provides measurable checkpoints that can be used in education, therapy, and even product design.

A few core ideas have emerged from the cognitive turn:

• Perceptual scaffolding: The brain first parses low‑level features (edges, contrast, motion) before it builds higher‑order meaning.
• Emotional resonance: Neural circuits linked to reward and memory light up when an artwork triggers personal relevance.
• Predictive coding: Viewers generate expectations based on cultural conventions; surprise—whether a broken perspective or an unexpected medium—creates a “prediction error” that the brain finds rewarding.

These concepts are not just academic jargon; they show up in concrete practice. For example, museum educators now stage “first‑look” moments where visitors spend a minute observing a work without any label, letting the perceptual scaffolding kick in before they are fed contextual information. In therapeutic settings, clinicians use the predictive‑coding principle to help clients experiment with “breaking” expectations in a safe, artistic space, thereby training flexibility in real‑world problem solving.

Glitches, Machines, and the New Self

If the cognitive model gives us a map of the mind’s terrain, the postdigital turn draws new borders on that map. A 2023 article in Digital and Postdigital Media in Art Education explores how “creative self‑expression” is being re‑examined as the self becomes increasingly “machinic.” The piece highlights Maggie‑Rose Condit‑Summerson’s work on “glitch feminism,” where technical errors—those sudden, jarring visual or auditory artifacts—are repurposed as tools for subverting patriarchal and racist narratives.

The core insight is simple yet radical: imperfection can be intentional, and that intentionality is a site of power. When a digital collage deliberately includes a pixelated tear or a corrupted file, it draws attention to the fragility of the medium and, by extension, the fragility of the social structures that shape it.

Key takeaways for practitioners:

• Embrace the error: Instead of polishing every output, leave room for the unexpected.
• Make the machine visible: Show the process—code snippets, hardware quirks, data streams—so the audience sees the collaboration between human and technology.
• Link to context: Use glitch aesthetics to comment on broader social issues, turning a technical flaw into a political statement.

In my own studio, I’ve started to incorporate “controlled crashes” into video installations. By programming a loop that intentionally triggers a buffer overflow after a set time, the piece shifts from smooth narration to chaotic distortion, prompting viewers to ask: whose narrative has been interrupted, and why?

Collaboration at the Edge: Artists, Scientists, and Society

The convergence of artistic practice and scientific inquiry isn’t a novelty—think of Leonardo da Vinci—but the institutional push for cross‑disciplinary work has accelerated dramatically. A 2019 European Parliament panel report on the future of science and technology emphasizes that “artists and scientists can potentially harness the knowledge now emerging in ways which are both meaningful and… Music is amongst man’s first cultural expressions.” The report argues for shared labs, joint grants, and public programs that blend empirical rigor with aesthetic inquiry.

Why does this matter? Because complex societal challenges—climate change, bioethics, AI bias—are rarely solved by a single discipline. When artists bring narrative, empathy, and speculation into the mix, they translate abstract data into lived experience. Conversely, scientists benefit from fresh perspectives that question assumptions and surface hidden variables.

Practical models of collaboration that are already proving effective:

• Residency exchanges: Artists spend a semester in a research institute, while scientists attend studio workshops.
• Co‑created exhibitions: Data visualizations are paired with performance pieces that embody the statistics’ human impact.
• Community‑driven hackathons: Teams of coders, designers, and local activists prototype solutions to neighborhood issues, with artists curating the storytelling component.

A recent example is the “BioArt Lab” at a major European university, where microbiologists grow pigment‑producing bacteria and artists shape the colonies into living portraits. The project sparked a public debate on the ethics of using living organisms for aesthetic purposes, illustrating how the collaboration generated discourse far beyond the lab walls.

From Insight to Practice: What We’re Taking Away

All these strands—cognitive modeling, postdigital glitch aesthetics, and cross‑disciplinary collaboration—are converging into a clearer picture of what artistic expression actually does. It’s no longer enough to label art as “just feeling”; we now see it as a process that negotiates perception, emotion, and social meaning while leveraging both human and technological agency.

Three practical lessons have emerged for anyone looking to deepen their practice or teach it more effectively:

• Design for the brain’s workflow. Structure experiences that respect the stages of perception, emotional engagement, and reflection.
• Turn errors into statements. Use glitches and other “failures” deliberately to challenge dominant narratives.
• Build bridges, not silos. Embed scientific perspectives early in the creative process, and invite artistic critique throughout research cycles.

Implementing these ideas doesn’t require a full lab overhaul. Simple adjustments—like adding a “silent observation” period before a gallery talk, or showcasing a raw code excerpt alongside a digital artwork—can make the difference between a fleeting encounter and a lasting impact.

Looking Ahead: The Unfinished Sketch

The future of artistic expression is as open-ended as a sketch on a napkin. As technology continues to blur the line between creator and tool, and as cognitive science refines its map of the aesthetic brain, we’ll likely see new forms of expression that we can’t yet name. What’s certain is that the process—the iterative dance between perception, material, and meaning—will remain the core driver.

For those of us on the front lines, staying curious, embracing uncertainty, and fostering interdisciplinary dialogue will keep our work both relevant and resonant. The canvas may change—from oil on linen to code on a server—but the act of making sense of the world through visual, auditory, or kinetic language will always be a uniquely human adventure.

Sources

• Visualizing the Impact of Art: An Update and Comparison of Current Psychological Models of Art Experience
• Digital and Postdigital Media in Art Education (2023)
• IN-DEPTH ANALYSIS Panel for the Future of Science and Technology (EU Parliament, 2019)