A fundamental component of interacting with our environment is gathering and interpretation of sensory information. When investigating how perceptual information shapes the mechanisms of decision-making, most researchers have relied on the use of manipulated or unnatural information as perceptual input, resulting in findings that may not generalize to real-world scenes. Unlike simplified, artificial stimuli, real-world scenes contain low-level regularities (natural scene statistics) that are informative about the structural complexity of a scene, which the brain could exploit during perceptual decision-making. In this study, participants performed an animal detection task on low, medium or high complexity scenes as determined by two biologically plausible natural scene statistics, contrast energy (CE) or spatial coherence (SC). In experiment 1, stimuli were sampled such that CE and SC both influenced scene complexity. Diffusion modeling showed that both the speed of information processing and the required evidence were affected by the low-level scene complexity. Experiment 2a/b refined these observations by showing how the isolated manipulation of SC alone resulted in weaker but comparable effects on decision-making, whereas the manipulation of only CE had no effect. Overall, performance was best for scenes with intermediate complexity. Our systematic definition of natural scene statistics quantifies how complexity of natural scenes interacts with decision-making in an animal detection task. We speculate that the computation of CE and SC could serve as an indication to adjust perceptual decision-making based on the complexity of the input.
Low-level image statistics in natural scenes influence perceptual decision-making
