Curious Objects: How Visual Complexity Guides Attention and Engagement

Abstract

Some things look more complex than others. For example, a crenulate and richly organized leaf may seem more complex than a plain stone. What is the nature of this experience—and why do we have it in the first place? Here, we explore how object complexity serves as an efficiently extracted visual signal that the object merits further exploration. We algorithmically generated a library of geometric shapes and determined their complexity by computing the cumulative surprisal of their internal skeletons—essentially quantifying the “amount of information” within each shape—and then used this approach to ask new questions about the perception of complexity. Experiments 1–3 asked what kind of mental process extracts visual complexity: a slow, deliberate, reflective process (as when we decide that an object is expensive or popular) or a fast, effortless, and automatic process (as when we see that an object is big or blue)? We placed simple and complex objects in visual search arrays and discovered that complex objects were easier to find among simple distractors than simple objects are among complex distractors—a classic search asymmetry indicating that complexity is prioritized in visual processing. Next, we explored the function of complexity: Why do we represent object complexity in the first place? Experiments 4–5 asked subjects to study serially presented objects in a self‐paced manner (for a later memory test); subjects dwelled longer on complex objects than simple objects—even when object shape was completely task‐irrelevant—suggesting a connection between visual complexity and exploratory engagement. Finally, Experiment 6 connected these implicit measures of complexity to explicit judgments. Collectively, these findings suggest that visual complexity is extracted efficiently and automatically, and even arouses a kind of “perceptual curiosity” about objects that encourages subsequent attentional engagement.

Seeing and speaking: How verbal “description length” encodes visual complexity.

“…Shape skeletons capture many aspects of a shape’s large- and small-scale organization, including not only the number of “parts” a shape has (Siddiqi et al, 1996) but also how these parts are configured with respect to one another and even, to some extent, the complexity of each part itself (since the shape of a skeletal branch captures the shape of its corresponding part). Shape skeletons are also psychologically plausible, with growing empirical evidence that they are computed and represented by human vision (Ayzenberg et al, 2019; Ayzenberg & Lourenco, 2019; Firestone & Scholl, 2014; Lowet et al, 2018; Sun & Firestone, 2021, Wilder et al, 2011). To turn this representation into a measure of complexity, we followed previous work (including Sun & Firestone, 2021) in computing the integral of the turning angle along each skeletal branch, summed over the total number of branches (using ShapeToolBox1.0; Feldman & Singh, 2006).…”

“…Shape skeletons are also psychologically plausible, with growing empirical evidence that they are computed and represented by human vision (Ayzenberg et al, 2019; Ayzenberg & Lourenco, 2019; Firestone & Scholl, 2014; Lowet et al, 2018; Sun & Firestone, 2021, Wilder et al, 2011). To turn this representation into a measure of complexity, we followed previous work (including Sun & Firestone, 2021) in computing the integral of the turning angle along each skeletal branch, summed over the total number of branches (using ShapeToolBox1.0; Feldman & Singh, 2006). An intuitive way to capture this measure might be to imagine a person walking along the skeleton of a shape; the more often this person changes direction (such that their next step was not easily predictable from their previous step), the greater the complexity of their walk and so the greater the complexity of the shape itself.…”

The Role of Stimulus‐Specific Perceptual Fluency in Statistical Learning

“…Our goal in Experiment 2 was to design stimuli that were not as visually complex as the complex stimuli in Experiment 1. Defining visual complexity is a difficult task and to our knowledge, there is no fully agreed‐upon approach (see, for example, Donderi, 2006 ; Miton & Morin, 2021 ; Pelli et al., 2006 ; and Sun & Firestone, 2021 , for discussions of the issues involved). However, most measures capture similar general principles (e.g., that simpler items have fewer and more distinguishable features) and are highly correlated with each other.…”

“…More complex stimuli take longer to process and require more working memory capacity (Alvarez & Cavanagh, 2004 ; Eng, Chen, & Jiang, 2005 ; Kemps, 1999 ; Liu, Chen, Liu, & Fua, 2012 ; Luria, Sessa, Gotler, Jolicoeur, & Dell'Acqua, 2010 ). They are also more appealing (Madan, Bayer, Gamer, Lonsdorf, Sommer, 2018 ) and attentionally engaging (Sun Firestone, 2021 ). Complexity affects learning as well: new symbols are easier to acquire when they are visually simpler (Pelli, Burns, Farell, & Moore‐Page, 2006 ).…”

The Order & Complexity Toolbox for Aesthetics (OCTA): A systematic approach to study the relations between order, complexity, and aesthetic appreciation

“…Other studies used different types of fractal images (e.g., Bies, Blanc-Goldhammer, Boydston, Taylor, & Sereno, 2016;Spehar, Walker, & Taylor, 2016). Sun and Firestone (2021) used a measure of structural surprisal to categorize abstract shapes as simple and complex while controlling for the number of sides. The Matlab code to generate the shapes for that study was shared publically on the Open Science Framework.…”