Abstract
We explored perceived material properties (roughness, texturedness, and hardness) with a novel approach that compares perception, image statistics and brain activation, as measured with fMRI. We initially asked participants to rate 84 material images with respect to the above mentioned properties, and then scanned 15 of the participants with fMRI while they viewed the material images. The images were analyzed with a set of image statistics capturing their spatial frequency and texture properties. Linear classifiers were then applied to the image statistics as well as the voxel patterns of visually responsive voxels and early visual areas to discriminate between images with high and low perceptual ratings. Roughness and texturedness could be classified above chance level based on image statistics. Roughness and texturedness could also be classified based on the brain activation patterns in visual cortex, whereas hardness could not. Importantly, the agreement in classification based on image statistics and brain activation was also above chance level. Our results show that information about visual material properties is to a large degree contained in low-level image statistics, and that these image statistics are also partially reflected in brain activity patterns induced by the perception of material images.
Highlights
The perception of material and surface properties is crucial for many aspects of our interaction with the environment, yet until now we have only a limited understanding of how this is achieved
It has been pointed out that the visual system probably relies on sets of invariant image statistics, or cues, in order to estimate object and material properties, instead of carrying out costly computations to work out the physics of a visual scene
In order to estimate the amount of information about the material property groups contained in our participants’ brain activity patterns while they observed our material stimuli, we ran the classifier on the β-weights extracted from areas V1, V2, and V3 in the subset of participants who had completed the retinotopy (Figure 3)
Summary
The perception of material and surface properties is crucial for many aspects of our interaction with the environment, yet until now we have only a limited understanding of how this is achieved. It is not yet well understood how the brain can quickly and successfully differentiate between a smooth, slippery object and a rough one that will provide a good grip. We examined what image features the brain might rely on during the processing of material properties and where in the brain information about material properties can be decoded. It has been pointed out that the visual system probably relies on sets of invariant image statistics, or cues, in order to estimate object and material properties, instead of carrying out costly computations to work out the physics of a visual scene
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