Face pareidolia is sensitive to spectral power and orientation energy.

The human visual system is sensitive to statistical regularities in natural images. This includes general properties like the characteristic 1/f power-spectrum fall-off coefficient observed across diverse natural scenes and category-specific properties like the bias favoring horizontal contrast energy for face recognition. Here, we examined the sensitivity of face pareidolia in adult observers to these image properties using fractal noise images and an unconstrained pareidolic face detection task. We presented participants in separate experiments with (Experiment 1) noise patterns with varying spectral fall-off coefficients and (Experiment 2) noise patterns with bandpass orientation filtering such that either horizontal or vertical contrast energy was limited. In both experiments, we found that face pareidolia rates were sensitive to these manipulations. In Experiment 1, we found that fractal noise patterns with steeper fall-off coefficients (favoring coarser appearance) led to lower rates of pareidolic face detection. In Experiment 2, we found that despite the clear bias favoring horizontal contrast energy in a wide range of face recognition tasks, both horizontal and vertical orientation bandpass filtering reduced rates of face pareidolia relative to isotropic images. We suggest that these results indicate that detecting pareidolic faces depends on the availability of face-like information across many low-level channels rather than a favored scale or orientation that is face-specific.