Modeling second-order boundary perception: A machine learning approach.

Christopher DiMattina,Curtis L. Baker,Wolfgang Einhäuser

doi:10.1371/journal.pcbi.1006829

Christopher DiMattina, Curtis L. Baker + Show 1 more

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https://doi.org/10.1371/journal.pcbi.1006829

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Abstract

Visual pattern detection and discrimination are essential first steps for scene analysis. Numerous human psychophysical studies have modeled visual pattern detection and discrimination by estimating linear templates for classifying noisy stimuli defined by spatial variations in pixel intensities. However, such methods are poorly suited to understanding sensory processing mechanisms for complex visual stimuli such as second-order boundaries defined by spatial differences in contrast or texture. We introduce a novel machine learning framework for modeling human perception of second-order visual stimuli, using image-computable hierarchical neural network models fit directly to psychophysical trial data. This framework is applied to modeling visual processing of boundaries defined by differences in the contrast of a carrier texture pattern, in two different psychophysical tasks: (1) boundary orientation identification, and (2) fine orientation discrimination. Cross-validation analysis is employed to optimize model hyper-parameters, and demonstrate that these models are able to accurately predict human performance on novel stimulus sets not used for fitting model parameters. We find that, like the ideal observer, human observers take a region-based approach to the orientation identification task, while taking an edge-based approach to the fine orientation discrimination task. How observers integrate contrast modulation across orientation channels is investigated by fitting psychophysical data with two models representing competing hypotheses, revealing a preference for a model which combines multiple orientations at the earliest possible stage. Our results suggest that this machine learning approach has much potential to advance the study of second-order visual processing, and we outline future steps towards generalizing the method to modeling visual segmentation of natural texture boundaries. This study demonstrates how machine learning methodology can be fruitfully applied to psychophysical studies of second-order visual processing.

Highlights

Many of the most common functions of sensory systems involve detection, identification or discrimination of particular stimuli
We demonstrate that our method can reveal different spatial summation strategies that human observers utilize for different kinds of second-order boundary perception tasks, and can be used to compare competing hypotheses of how contrast modulation is integrated across orientation channels
We focus on contrast-defined second-order boundaries (Fig 1a and 1b) since they are well studied psychophysically [30,31,32,33,34], and there are neurons in the early mammalian visual cortex that give selective responses to contrast modulation stimuli [35,36,37,38]

Summary

Introduction

Many of the most common functions of sensory systems involve detection, identification or discrimination of particular stimuli. A widely used approach to modeling human psychophysics has been to assume that performance can be understood in terms of how well a visual stimulus matches an internal “template”, typically modeled as a linear spatial filter This approach uses high-dimensional regression models fit to data from tasks in which a subject detects or classifies a sensory stimulus presented with superimposed white noise [2,3,4,5], to recover an estimate of the linear filter that best accounts for the data. Psychophysical SI methods have been applied in varied problem domains and yielded valuable insight into perceptual mechanisms [4, 8,9,10,11,12,13]

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