Stationary photo

educational research

Beginning in January of 2017, Doherty began a collaboration with Denis Dumas (Ph.D, Educational Psychology) at the University of Denver. Over a series of five articles, printed across five peer-reviewed journals, the team studied the personality traits and thought processes of professional actors.

Measuring Divergent Thinking Originality with Human Raters and Text-Mining Models: A Psychometric Comparison of Methods

[Published February 2020 in Psychology of Aesthetics Creativity and the Arts]

by Denis Dumas, Peter Organisciak, and Michael Doherty

Genesis: The team distributed an Alternate Uses Task (e.g. "How many uses for a brick can you think of in two minutes?") to measure creativity in hundreds of actors, and now had to generate Originality scores for all of their responses.

Problem: Though human raters could perhaps generate the most accurate Originality score, that would require a lot of time and resources.

Solution:  There are a number of text-mining models, or language datasets, that can measure Originality.


Study: The team used most popular models to determine which one was the most effective.

Findings: Human raters were, indeed, the most reliable in rating Originality, but the language dataset GloVE 840B was a close second, making it the most reliable system to measure Originality in artists.

by Denis Dumas, Michael Doherty, and Peter Organisciak

Genesis: The team was curious about how actors' brains work, and therefore wanted to learn the psychological and neurological differences between them and other people.

Problem: After collecting data from professional actors, student actors, and non-actors, the data the team collected was difficult to compare in meaningful ways.

Solution: By using a classification model, in this case an actor-sorting artificial intelligence (AI) system, the team could determine which differences between actors and non-actors were significant enough to distinguish the two groups.

Study: Use said AI system to determine the key differences between actors and non-actors, and between professional actors and student actors.

Findings: The AI system was able to distinguish actors from non-actors with a 92% accuracy, and found that actors had higher levels of Extraversion, Openness, Assertiveness, Elaboration, and Creative Activities. It distinguished professional actors from student actors with a 96% accuracy (68% when the team didn't factor in age), and found that professional actors high higher levels of Originality, Volatility, and Literary Activities.

by Denis Dumas, Yixiao Dong, and Michael Doherty

Genesis: The team administered an Analogy Finding Task (i.e. Complete the Analogy) to their participants, and measured both their ability to make a successful analogy (Sensitivity) and their ability to rule out non-valid analogies (Selectivity).

Problem: The team wanted to ensure that all participants were able to use their imaginations in the AFT, regardless of creative expertise.

Solution: All participants were asked to do the task, and then asked to do it while "thinking creatively."

Study: Determine how the "think creatively" prompt affected actors versus non-actors.

Findings: While the "think creatively" prompt increased actors' Sensitivity much more than non-actors, it also decreased their Selectivity much more than non-actors. The team's hypothesis is that this is due to an actor's flexibility when given a creative prompt.