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In tasks related to image analysis, searching for pixel-level regularities has reached a power of information extraction still far from what humans capture with image-based observations. This leads to poor generalization when even small shifts occur at the level of the observations. We explore a perspective on this problem that is directed to learning the generative process with causality-related foundations, using models capable of combining symbolic manipulation, probabilistic reasoning, and pattern recognition abilities. We briefly review and explore connections of research from machine learning, cognitive science, and related fields of human behavior to support our perspective for the direction to more robust and human-like artificial learning systems.<\/jats:p>","DOI":"10.1145\/3696412","type":"journal-article","created":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T10:58:26Z","timestamp":1726743506000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Causal representation learning through higher-level information extraction"],"prefix":"10.1145","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3069-2282","authenticated-orcid":false,"given":"Francisco","family":"Silva","sequence":"first","affiliation":[{"name":"INESC TEC, Porto, Portugal and Faculty of Sciences, University of Porto, Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6193-8540","authenticated-orcid":false,"given":"H\u00e9lder","family":"P. 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