Better than Goodenough? Evaluating new computational techniques for finding diagnostic structure in children's drawings.

In her 1926 book Measurement of Intelligence by Drawings, Florence Goodenough pioneered the quantitative analysis of children's human-figure drawings as a tool for evaluating their cognitive development. This influential work launched a broad enterprise in cognitive evaluation that continues to the present day, with most clinicians and researchers deploying variants of the checklist-based scoring methods that Goodenough invented. Yet recent work leveraging computational innovations in cognitive science suggests that human-figure drawings possess much richer structure than checklist-based approaches can capture. The current study uses these contemporary tools to characterize structure in the images from Goodenough's original work, then assesses whether this structure carries information about demographic and cognitive characteristics of the participants in that early study. The results show that contemporary methods can reliably extract information about participant age, gender, and mental faculties from images produced over 100 years ago, with no expert training and with minimal human effort. Moreover, the new analyses suggest a different relationship between drawing and mental ability than that captured by Goodenough's highly influential approach, with important implications for the use of drawings in cognitive evaluation in the present day.

Human perception and machine vision reveal rich latent structure in human figure drawings.

For over a hundred years, children's drawings have been used to assess children's intellectual, emotional, and physical development, characterizing children on the basis of intuitively derived checklists to identify the presence or absence of features within children's drawings. The current study investigates whether contemporary data science tools, including deep neural network models of vision and crowd-based similarity ratings, can reveal latent structure in human figure drawings beyond that captured by checklists, and whether such structure can aid in understanding aspects of the child's cognitive, perceptual, and motor competencies. We introduce three new metrics derived from innovations in machine vision and crowd-sourcing of human judgments and show that they capture a wealth of information about the participant beyond that expressed by standard measures, including age, gender, motor abilities, personal/social behaviors, and communicative skills. Machine-and human-derived metrics captured somewhat different aspects of structure across drawings, and each were independently useful for predicting some participant characteristics. For example, machine embeddings seemed sensitive to the magnitude of the drawing on the page and stroke density, while human-derived embeddings appeared sensitive to the overall shape and parts of a drawing. Both metrics, however, independently explained variation on some outcome measures. Machine embeddings explained more variation than human embeddings on all subscales of the Ages and Stages Questionnaire (a parent report of developmental milestones) and on measures of grip and pinch strength, while each metric accounted for unique variance in models predicting the participant's gender. This research thus suggests that children's drawings may provide a richer basis for characterizing aspects of cognitive, behavioral, and motor development than previously thought.