Visual explanations prioritize functional properties at the expense of visual fidelity

Cognition. 2023 Jul:236:105414. doi: 10.1016/j.cognition.2023.105414. Epub 2023 Mar 2.

Abstract

Visual explanations play an integral role in communicating mechanistic knowledge about how things work. What do people think distinguishes such pictures from those that are intended to convey how things look? To explore this question, we used a drawing paradigm to elicit both visual explanations and depictions of novel machine-like objects, then conducted a detailed analysis of the semantic information conveyed in each drawing. We found that visual explanations placed greater emphasis on parts of the machines that move or interact to produce an effect, while visual depictions emphasized parts that were visually salient, even if they were static. Moreover, we found that these differences in visual emphasis impacted what information naive viewers could extract from these drawings: explanations made it easier to infer which action was needed to operate the machine, but more difficult to identify which machine it represented. Taken together, our findings suggest that people spontaneously prioritize functional information when producing visual explanations but that this strategy may be double-edged, facilitating inferences about physical mechanism at the expense of preserving visual fidelity.

Keywords: Causal learning; Explanation; Natural pedagogy; Visual production.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Humans
  • Knowledge*
  • Semantics*