Abstract
The efficient coding hypothesis posits that sensory systems are tuned to the regularities of their natural input. The statistics of natural image databases have been the topic of many studies, which have revealed biases in the distribution of orientations that are related to neural representations as well as behavior in psychophysical tasks. However, commonly used natural image databases contain images taken with a camera with a planar image sensor and limited field of view. Thus, these images do not incorporate the physical properties of the visual system and its active use reflecting body and eye movements. Here, we investigate quantitatively, whether the active use of the visual system influences image statistics across the visual field by simulating visual behaviors in an avatar in a naturalistic virtual environment. Images with a field of view of 120° were generated during exploration of a virtual forest environment both for a human and cat avatar. The physical properties of the visual system were taken into account by projecting the images onto idealized retinas according to models of the eyes' geometrical optics. Crucially, different active gaze behaviors were simulated to obtain image ensembles that allow investigating the consequences of active visual behaviors on the statistics of the input to the visual system. In the central visual field, the statistics of the virtual images matched photographic images regarding their power spectra and a bias in edge orientations toward cardinal directions. At larger eccentricities, the cardinal bias was superimposed with a gradually increasing radial bias. The strength of this effect depends on the active visual behavior and the physical properties of the eye. There were also significant differences between the upper and lower visual field, which became stronger depending on how the environment was actively sampled. Taken together, the results show that quantitatively relating natural image statistics to neural representations and psychophysical behavior requires not only to take the structure of the environment into account, but also the physical properties of the visual system, and its active use in behavior.
Highlights
One of the most successful and long-standing computational approaches to perception has posited that perceptual systems are adapted to the sensory stimuli they encounter
In order to analyze the properties of the natural input of the visual system, we created a dataset of image patches in a naturalistic virtual environment
To establish that the virtual forest scenes do not show idiosyncratic differences to natural images but exhibit similar statistics to real images, the planar virtual images in the central area (φ = 0◦, χ = 0◦) of the visual field were compared with the van Hateren and van der Schaaf (1998) database
Summary
One of the most successful and long-standing computational approaches to perception has posited that perceptual systems are adapted to the sensory stimuli they encounter. Since the computational resources available to a sensory system are limited by biological constraints, more resources should be allocated to process the inputs that are more likely to be encountered This formalization is closely related to Bayesian approaches to perception (Knill and Richards, 1996), according to which the visual system infers the most likely causes of ambiguous, uncertain, and noisy sensory signals it obtains by probabilistically combining them with prior knowledge. In this Bayesian setting, computing a posterior probability over image causes only leads to the correct inferences, if the prior distribution over these image causes is adapted to the empirical distribution of the variables in the environment. It is of crucial importance both in the framework of information theory and the Bayesian framework for a sensory system to be well-calibrated to the statistics of its input (Fiser et al, 2010; Ganguli and Simoncelli, 2014; Wei and Stocker, 2017)
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