Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition
Fig 5
Visualization of critical features used by the Dual-task CNN to classify face pareidolia stimuli.
A Visualization of the critical features in sample pareidolia stimuli (top row) that the Dual-task CNN uses to classify these stimuli as either ‘face’ (middle row) or ‘object’ (bottom row). Green areas indicate positive class attribution, essential for classification, while red areas signify negative class attribution, detrimental for classification. The pareidolia images were sourced from Wardle et al. [12] (https://www.nature.com/articles/s41467-020-18325-8) and are used under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). Parts of the object and pareidolia images containing logos or brands have been covered with a white box for legal compliance. Example masks are used to quantify the use of face-like features in classifying pareidolia stimuli as ‘face’ or ‘object’. For each pareidolia stimulus, we generated masks corresponding to the entire face (‘Face’), specific face features like eyes (‘Eyes’) and mouth (‘Mouth’), and the area outside the face (‘Outside Face’). We calculated the mean pixel ratio within each mask (ranging from 0, indicating the lowest, to 1, the highest) for classifying the stimulus as ‘face’ (red bars) or as ‘object’ (yellow bars). B, C, D, E, F This mask-based ratio is calculated for all the CNNs. The analysis revealed that the Dual-task CNN primarily relies on facial features, especially the eye region, to classify the pareidolia stimulus as a face. Areas outside the face were similarly used in both classifications. Such clarity is not observed in other CNNs. Error bars denote SEM across stimuli.