Split Second Stats #4: Engagement

In previous Split Second blog posts, we looked at the effects of thin-slicing, textual information, and gender. Put another way, we were studying the effects of how long you look at the art, what sort of accompanying text there is, and who you are when you look at it. However, these don’t cover the full breadth of the museum-going experience. Viewers are increasingly asked to engage in some way with the art on display; for example, in our current exhibition Vishnu: Hinduism’s Blue-Skinned Savior, we ask viewers to identify avatars of Vishnu in different works throughout the gallery. We wanted to see what effects tasks like this had on ratings in our Split Second experiment. To do this, we had participants do what we call the “engagement” task.

In this task, participants were split up in groups. Each group was asked to perform a specific task which required them to engage with the content of the work they were looking at. The tasks were as follows:

• Counting: Type in the number of figures in the work.
• Description: Describe the work in your own words.
• Color: Name the dominant color in the work.
• Free association: Type the first thing that comes to mind when looking at the work.
• Tagging: Type a single word which describes the subject or mood of the work.
• No task: our control group, as described in our first stats blog post.

I expected that after completing any of these tasks participants would have a stronger emotional connection to the work, so the average rating would go up. Surprisingly, this was not the case. None of the engagement tasks had a statistically significant effect on average rating. Our curator Joan Cummins was not surprised by this, saying that curatorial interventions such as engagement tasks were not intended to make people enjoy the work more, but to get them to learn about it.

However, though the engagement tasks did not affect the average rating, they did affect the way ratings were distributed, i.e. how all of the participants’ ratings were spread out around the scale. We found that when participants completed an engagement task, their ratings clustered much more tightly together. In statistical terms, engagement tasks reduced the variance of the ratings. This means that, though engagement tasks don’t make people like things more, they make people’s ratings more consistent, or increase agreement about a work across the whole population of participants.

Indian. Episode Surrounding the Birth of Krishna, Page from a Dispersed Bhagavata Purana Series, late 17th-early 18th century. Opaque watercolor on paper, sheet: 10 1/8 x 15 15/16 in. (25.7 x 40.5 cm). Brooklyn Museum, Gift of Emily Manheim Goldman, 1991.180.10

Also surprising to me was which task reduced the variance the most. I expected that the description or tagging tasks would create the most agreement across participants, because they require people to evaluate what’s being portrayed in the work in linguistic terms. However, the counting task reduced variance the most, followed by the color and free-response tags (a tie for second place), then tagging, with the description task coming in dead last. We’ve speculated that this may be because of how the various tasks manipulated conscious attention—the description task focuses conscious attention on the content of the painting, whereas the counting task focuses your conscious attention on a more-or-less objective formal property (the number of figures).

Why, exactly, this would reduce variance is unclear. It may be because focusing on form instead of content means people don’t pay attention to things that might otherwise affect their rating of the painting, e.g. controversial subject matter. It may also create a situation where evaluation of the quality of the painting (as opposed to evaluate of its form) is passed along to the subconscious, and that (to extend Gladwell’s thin-slicing hypothesis) subconscious judgments may naturally tend to have less variance. This suggests yet another interesting direction for further research.