Interpretation and its Discontents

I was struck in this reading by how the nature of our interpretive capabilities as human beings can often lead us astray. This was most notable in visual illusions, such as Edward Adelson's checkershadow. I have looked at this illusion multiple times, and even seen spent hours looking at different demonstrations of it, but it still amazes me how robust this human capability of meaning creation/interpretation is. Even knowing that our brain is interpreting values different than those hitting our retina doesn't help "fix" the perceptual experience of how it appears.

This also came to the forefront in a different way with Healy's treatment of 'randomness.' It amazes me how we as humans generally perceive the Poisson-based distribution to have more structure than the Matérn distribution, despite the fact that algorithmically the first is more "random" than the second.

In these readings, these lapses in rationality are posed as a constraint on our ability to directly perceive "objective" values in our world, but I also see this as the feature of human beings that gives power and salience to data visualization. This unconscious/subconscious/nonconscious (whatever it may be) "pop-out"-edness is less about the physical ink on paper and more about our embodied humanness.

I believe that these interpretative discrepancies with "reality" also come in the cognitive framing of a given subject matter. I would be interested in learning more about how the conceptual metaphor at play (regardless of whether it is instantiated in visual or textual form) affects the viewers' interpretation of a visualization. These readings focuses primarily on the visual side of these things, but I am also interested in the priming effect a large headline can have on the reading of a visualization, even if it entirely bypasses consciousness. For example a line graph showing a slowing trend of GDP could have a title of "Due for a Rebound?" or "Economy in a Sinkhole" and may create significantly different takeaways from the same perceptual stimuli.

In short, I think these cognitive biases should always remain at the forefront of the design process -- not only because of the potential to mislead, but also because these biases are what make human interpretation of data visualization possible at all.