Abstract
People’s knowledge about objects has traditionally been probed using a combination of feature-listing and rating tasks. However, feature listing fails to capture nuances in what people know about how objects look — their visual knowledge — which cannot easily be described in words. Moreover, rating tasks are limited by the set of attributes that researchers even think to consider. By contrast, freehand sketching provides a way for people to externalize their visual knowledge about objects in an open-ended fashion. As such, sketch behavior provides a versatile substrate for asking a wide range of questions about visual object knowledge that go beyond the scope of a typical study. Here we introduce THINGS-drawings, a new crowdsourced dataset containing multiple freehand sketches of the 1,854 object concepts in the THINGS database (Hebart et al., 2019). THINGS-drawings contains fine-grained information about the stroke-by-stroke dynamics by which participants produced each sketch, as well as a rich set of other metadata, including ratings on various attributes, feature lists, and demographic characteristics of the participants contributing each sketch. As such, THINGS-drawings provides more comprehensive coverage of real-world visual concepts than previous sketch datasets (Eitz et al., 2012; Sangkloy et al., 2016; Jongejan, et al., 2016), which contain less richly annotated sketches of a smaller number of concepts (i.e., ~100-300). This broader scope enables stronger tests of the capabilities of current artificial intelligence systems to understand abstract visual inputs, and thus a benchmark for driving the development of systems that display more human-like image understanding across visual modalities. Moreover, we envision THINGS-drawings as a resource to the vision science community for investigating the richer aspects of many perceptual and cognitive phenomena in a unified manner, including visual imagery, memorability, semantic cognition, and visual communication.