The SPOT Grid: Naturalistic Images with Predefined Settings, Patterns, Objects, and Textures
Abstract
In the natural world, higher-level visual features (e.g., scenes and objects) systematically covarywith specific lower-level constituent features (e.g., textures and patterns). This natural covariationmakes it difficult to isolate aspects of behavior or brain activity that are specific to a single level ofthe feature hierarchy. For example, a particular neural or behavioral response may reflectrecognition of a “jungle” scene, or it may reflect sensitivity to the lower-level features — leaftextures, dense vertical lines, etc.— that constitute jungle scenes. To address this challenge, wedeveloped a novel approach for partially decoupling higher- and lower-level features innaturalistic images. We employed a Stable Diffusion image generation framework to create anaturalistic image set in which setting–object pairings and texture–pattern pairings are factoriallycombined, enabling systematic manipulation of higher-level and lower-level image content. Tovalidate our approach, we used state-of-the-art image processing models to confirm thathigher-level features are represented similarly regardless of their lower-level constituent featuresand, conversely, that lower-level features are represented similarly regardless of the higher-levelobjects/settings they constitute. We then confirmed that human participants can readily andrapidly categorize these features in our generated images. We present the image generationframework and introduce the SPOT (Settings, Patterns, Objects, Textures)…
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