Perceptual Scales Predicted by Fisher Information Metrics
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Keywords: perceptual scale, perceptual distance, gaussian texture, naturalistic textures, texture interpolation
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TL;DR: The predictions of perceptual scales based on Fisher information metrics are tested in a series of experiments. This will allow us to go beyond perceptual distances and get closer to perceptual geometry.
Abstract: Perception is often viewed as a process that transforms physical variables, external to an observer, into internal psychological variables. Such a process can be modeled by a function coined *perceptual scale*. The *perceptual scale* can be deduced from psychophysical measurements that consist in comparing the relative differences between stimuli (i.e. difference scaling experiments). However, this approach is often overlooked by the modeling and experimentation communities. Here, we demonstrate the value of measuring the *perceptual scale* of classical (spatial frequency, orientation) and less classical physical variables (interpolation between textures) by embedding it in recent probabilistic modeling of perception. First, we show that the assumption that an observer has an internal representation of univariate parameters such as spatial frequency or orientation while stimuli are high-dimensional does not lead to contradictory predictions when following the theoretical framework. Second, we show that the measured *perceptual scale* corresponds to the transduction function hypothesized in this framework. In particular, we demonstrate that it is related to the Fisher information of the generative model that underlies perception and we test the predictions given by the generative model of different stimuli in a set a of difference scaling experiments. Our main conclusion is that the *perceptual scale* is mostly driven by the stimulus power spectrum. Finally, we propose that this measure of *perceptual scale* is a way to push further the notion of perceptual distances by estimating the perceptual geometry of images i.e. the path between images instead of simply the distance between those.
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Primary Area: applications to neuroscience & cognitive science
Submission Number: 2733
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