Exploring the Cognitive Correlates of Artistic Practice Using a Parameterized Non-Photorealistic Toolkit

Artists and scientists have different approaches to knowledge acquisition, usage and dissemination. This research is one attempt to bridge these different fields by the creation of a unique software toolkit for nonphotorealistic rendering (NPR). The researchers are interested in elucidating the perceptual mechanisms or “cognitive correlates” that correspond and relate to artists’ techniques and conceptions regarding fine art painting. First, they analyze an extensive corpus of historical art-theory literature to identify broadly accepted art practice understandings and techniques, which might be relevant to human perception and cognition. They further condense this artistic knowledge into a concise set of heuristics, which are suitable for parameterization and algorithmic implementation and examine findings from psychology and neuroscience, which correlate to each heuristic. The researchers present their system design for a painterly NPR toolkit that is informed by these heuristics within an object-oriented, cognitively inspired architecture. By interpreting artistic and cognitive science knowledge into a well-defined computational framework, they gain opportunities to formalize and test new hypotheses. The productive power of such an approach is demonstrated by examining in depth two particular techniques—lost-and-found edges and varying fine detail level—used by a particular artist (Rembrandt). Four experiments based on eye tracking of human viewers are formulated, using their NPR toolkit to generate example artworks with manipulated generation parameters. Significant findings are obtained that suggest artists such as Rembrandt use techniques that leverage perceptual and cognitive function to exert control over viewer’s gaze patterns, which in turn influences the experienced artistic merit of a painting.

Steve DiPaola, Computational Painterly Rendering 1, 2017.

(© Steve DiPaola) (See also Color Plate D.)

COLOR PLATE D: EXPLORING THE COGNITIVE CORRELATES OF ARTISTIC PRACTICE USING A PARAMETERIZED NON-PHOTOREALISTIC TOOLKIT

Steve DiPaola, Computational Painterly Rendering 2, 2017. One of four experiments based on eye tracking of human viewers, using a non-photorealistic rendering toolkit to generate example artworks with manipulated generation parameters.

(© Steve DiPaola) (See top-ranked abstract in this issue by Steve DiPaola in the LABS 2016 special section.)

Supplementary Material

Painterly Output Cope (.jpg 2 MB)
[Click to Download] Using DiPaola’s Computer Program “Painterly” which models cognitive / creative processes of fine art painters, along with DiPaola’s new Deep Learning AI technique which learns styles from a large corpus, this output portrait was generated of David Cope. (Image © Steve DiPaola 2016)

Painterly Output Greg (.jpg 1 MB)
[Click to Download] Using DiPaola’s Computer Program “Painterly” which models cognitive / creative processes of fine art painters, a portrait (left) was created from the sitter (lower right) in the style of Rembrandt (upper right). DiPaola then used these in an eye tracking study to show the cognitive genius of master artists. (Image © Steve DiPaola 2015)

Painterly Output Maryam (.jpg 1.6 MB)
[Click to Download] Using DiPaola’s Computer Program “Painterly” which models cognitive / creative processes of fine art painters, a portrait (left) was created from the sitter (lower right) in the style of Rembrandt (upper right). DiPaola then used these in an eye tracking study to show the cognitive genius of master artists. (Image © Steve DiPaola 2015)