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Despite extensive research, it remains unclear why the visual system employs such broadly tuned face detection capabilities. We hypothesized that face pareidolia results from the visual system\u2019s optimization for recognizing both faces and objects. To test this hypothesis, we used task-optimized deep convolutional neural networks (CNNs) and evaluated their alignment with human behavioral signatures and neural responses, measured via magnetoencephalography (MEG), related to pareidolia processing. Specifically, we trained CNNs on tasks involving combinations of face identification, face detection, object categorization, and object detection. Using representational similarity analysis, we found that CNNs that included object categorization in their training tasks represented pareidolia faces, real faces, and matched objects more similarly to neural responses than those that did not. Although these CNNs showed similar overall alignment with neural data, a closer examination of their internal representations revealed that specific training tasks had distinct effects on how pareidolia faces were represented across layers. Finally, interpretability methods revealed that only a CNN trained for both face identification and object categorization relied on face-like features\u2014such as \u2018eyes\u2019\u2014to classify pareidolia stimuli as faces, mirroring findings in human perception. Our results suggest that human-like face pareidolia may emerge from the visual system\u2019s optimization for face identification within the context of generalized object categorization.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1012751","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T18:48:17Z","timestamp":1738003697000},"page":"e1012751","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":7,"title":["Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1458-3759","authenticated-orcid":true,"given":"Pranjul","family":"Gupta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6619-5590","authenticated-orcid":true,"given":"Katharina","family":"Dobs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,1,27]]},"reference":[{"issue":"11","key":"pcbi.1012751.ref001","doi-asserted-by":"crossref","first-page":"4302","DOI":"10.1523\/JNEUROSCI.17-11-04302.1997","article-title":"The fusiform face area: a module in human extrastriate cortex specialized for face perception","volume":"17","author":"N Kanwisher","year":"1997","journal-title":"J Neurosci"},{"issue":"6","key":"pcbi.1012751.ref002","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/S1364-6613(00)01482-0","article-title":"The distributed human neural system for face perception","volume":"4","author":"J Haxby","year":"2000","journal-title":"Trends Cogn Sci"},{"key":"pcbi.1012751.ref003","doi-asserted-by":"crossref","first-page":"342","DOI":"10.3389\/fpsyg.2011.00342","article-title":"Low-level cues and ultra-fast face detection","volume":"2","author":"SM Crouzet","year":"2011","journal-title":"Front Psychol"},{"key":"pcbi.1012751.ref004"},{"issue":"24","key":"pcbi.1012751.ref005","doi-asserted-by":"crossref","first-page":"11687","DOI":"10.1073\/pnas.1820553116","article-title":"Individual differences in visual salience vary along semantic dimensions","volume":"116","author":"B de Haas","year":"2019","journal-title":"Proc Natl Acad Sci U S A"},{"key":"pcbi.1012751.ref006","doi-asserted-by":"crossref","unstructured":"van Dyck LE, Denzler SJ, Gruber WR. 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