Low-level image statistics in natural scenes influence perceptual decision-making

Noor Seijdel,H Steven Scholte,Sara Jahfari,Iris I A Groen

doi:10.1038/s41598-020-67661-8

Abstract

A fundamental component of interacting with our environment is gathering and interpretation of sensory information. When investigating how perceptual information influences decision-making, most researchers have relied on 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 that are informative about the structural complexity, which the brain could exploit. 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 modelling showed that the speed of information processing was affected by low-level scene complexity. Experiment 2a/b refined these observations by showing how isolated manipulation of SC resulted in weaker but comparable effects, with an additional change in response boundary, whereas manipulation of only CE had no effect. Overall, performance was best for scenes with intermediate complexity. Our systematic definition quantifies how natural scene complexity interacts with decision-making. We speculate that CE and SC serve as an indication to adjust perceptual decision-making based on the complexity of the input.

Highlights

A fundamental component of interacting with our environment is gathering and interpretation of sensory information
This study systematically investigated the interaction between low-level statistics in natural scenes and perceptual decision-making processes
We show that task performance was best on medium complex images

Summary

Introduction

A fundamental component of interacting with our environment is gathering and interpretation of sensory information. In recent computational accounts of perceptual decision-making, sensory evidence for a decision option is integrated and accumulates over time until it reaches a certain boundary[1,2] Across these computational accounts, the speed of evidence accumulation is thought to depend on the quality or strength of sensory information available (the drift rate, as formalized with the well-known drift diffusion m odel[3]). We aimed to investigate how decision-making processes are influenced by low-level image properties, diagnostic of scene complexity. While multiple studies have shown that specific image properties (such as spatial frequency, or stimulus strength) interact with decision-making, they manipulate visual information into “unnatural” stimuli. Natural scene statistics have been demonstrated to carry diagnostic information about the visual environment: for example, slopes of spatial frequency spectra estimated across different spatial scales and orientations (’spectral signatures’) are informative of scene category and spatial layout[6,7,8]. Scene complexity reflected in local contrast distributions can be estimated using an early visual receptive field model that outputs two parameters, contrast energy (CE) and spatial coherence (SC), approximating the scale and shape of a Weibull fit to the local contrast distribution, respectively

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