Future Studies

Drawing evaluation – ​A key “measure” of drawing performance in our studies is the evidence of the finished drawing itself. However the traditional, perhaps most accepted, association with drawing in the fine art context is that quality assessment is purely subjective. Even when consideration is limited to mere technical accuracy, there remain multiple ways in which a drawing can be understood as more or less accurately “looking like” its real-world referent. The purpose of this study would be to compare three approaches: 1) expert judgments, 2) crowd-sourced judgments, and 3) machine-learning algorithmic judgments. The study would aim to determine the efficacy of the above three approaches for evaluating drawing accuracy; to develop a better understanding of how drawing accuracy is perceived; and to develop tools that aid the evaluative task in other future studies.

Foveal/peripheral vision by filter – ​The application of high- or low-pass image filters creates images with information selectively included/occluded, the result of which may be analogous to what is viewed through human foveal and peripheral vision. A study using such filters will comprise an alternative methodology and companion study to our current eyetracking-based foveal/peripheral study.

Digital drawing and erasure – ​Anecdotal observation suggests that drawers using a digital pen display in an observational drawing task tend to erase more than is typical of drawers using traditional (analog) media. Our hypothesis is that either i) erasing is easier or erasure is more complete in digital drawing, making it an appealing technique for drawing development, or ii) drawers experience a higher rate of error when drawing digitally.