Predicting the Partition of Behavioral Variability in Speed Perception with Naturalistic Stimuli

Abstract

A core goal of visual neuroscience is to predict human perceptual performance from natural signals. Performance in any natural task can be limited by at least three sources of uncertainty: stimulus variability, internal noise, and suboptimal computations. Determining the relative importance of these factors has been a focus of interest for decades but requires methods for predicting the fundamental limits imposed by stimulus variability on sensory-perceptual precision. Most successes have been limited to simple stimuli and simple tasks. But perception science ultimately aims to understand how vision works with natural stimuli. Successes in this domain have proven elusive. Here, we develop a model of humans based on an image-computable (images in, estimates out) Bayesian ideal observer. Given biological constraints, the ideal optimally uses the statistics relating local intensity patterns in moving images to speed, specifying the fundamental limits imposed by natural stimuli. Next, we propose a theoretical link between two key decision-theoretic quantities that suggests how to experimentally disentangle the impacts of internal noise and deterministic suboptimal computations. In several interlocking discrimination experiments with three male observers, we confirm this link and determine the quantitative impact of each candidate performance-limiting factor. Human performance is near-exclusively limited by natural stimulus variability and internal noise, and humans use near-optimal computations to estimate speed from naturalistic image movies. The findings indicate that the partition of behavioral variability can be predicted from a principled analysis of natural images and scenes. The approach should be extendable to studies of neural variability with natural signals.

Contact lenses, the reverse Pulfrich effect, and anti-Pulfrich monovision corrections

“…defocus blur, binocular disparity, and motion) provide reason for optimism 8,[56][57][58] . Models that compute cue values directly from images ('image-computable models') have found recent success in predicting human performance in a range of visual tasks [59][60][61][62][63][64][65][66] . However, to our knowledge, there exists no theoretical or empirical work that tightly links the properties of natural images to processing speed.…”

Continuous psychophysics shows millisecond-scale visual processing delays are faithfully preserved in movement dynamics

“…But they have rarely been applied to human psychophysical data, perhaps because human datasets collected with traditional button-press methods have insufficient data for the method's full statistical power to be realized (Knoblauch & Maloney, 2008;Macke & Wichmann, 2010;Murray, 2012). The continuous target-tracking paradigm, paired with recent developments linking normative models to methods for systems identification (Jaini & Burge, 2017) and human behavior (Burge & Geisler, 2015;Chin & Burge, 2020), provides an exciting direction for future work in the analysis of human perception and behavior.…”

Natural scene statistics predict how humans pool information across space in surface tilt estimation

“…In recent years, a series of papers have provided evidence linking certain statistical aspects of natural images and scenes [15,[28][29][30][36][37][38][39] to the design of the human visual system [40][41][42], and to the performance of ideal and human observers in perceptual tasks [14,16,38,[43][44][45][46][47][48][49][50][51][52]. This broad program of research has, with varying degrees of rigor, invoked natural scene statistics to account for a strikingly diverse set of topics: how the shape of pupils changes across species in different ecological niches [42], where corresponding points are located in the two PLOS COMPUTATIONAL BIOLOGY retinas [40,41], how biases in binocular eye movements manifest [49], how targets are detected in natural images [48], how image contours are perceptually grouped [38,43], how image orientation is estimated [46], how focus error is estimated [50,51], how binocular disparity is estimated [45,53,54], how image motion is estimated [47,52,55], how 3D tilt is estimated [16], and now, how cues to 3D tilt are pooled across space. Over...…”