{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/applying-probabilistic-programming-to","title":"Applying Probabilistic Programming to Affective Computing","arxiv_id":"1903.06445","date":"2019-03-15","proceeding":null,"authors":["Desmond C. Ong","Harold Soh","Jamil Zaki","Noah D. Goodman"],"abstract":"Affective Computing is a rapidly growing field spurred by advancements in\nartificial intelligence, but often, held back by the inability to translate\npsychological theories of emotion into tractable computational models. To\naddress this, we propose a probabilistic programming approach to affective\ncomputing, which models psychological-grounded theories as generative models of\nemotion, and implements them as stochastic, executable computer programs. We\nfirst review probabilistic approaches that integrate reasoning about emotions\nwith reasoning about other latent mental states (e.g., beliefs, desires) in\ncontext. Recently-developed probabilistic programming languages offer several\nkey desidarata over previous approaches, such as: (i) flexibility in\nrepresenting emotions and emotional processes; (ii) modularity and\ncompositionality; (iii) integration with deep learning libraries that\nfacilitate efficient inference and learning from large, naturalistic data; and\n(iv) ease of adoption. Furthermore, using a probabilistic programming framework\nallows a standardized platform for theory-building and experimentation:\nCompeting theories (e.g., of appraisal or other emotional processes) can be\neasily compared via modular substitution of code followed by model comparison.\nTo jumpstart adoption, we illustrate our points with executable code that\nresearchers can easily modify for their own models. We end with a discussion of\napplications and future directions of the probabilistic programming approach.","url_abs":"http://arxiv.org/abs/1903.06445v1","url_pdf":"http://arxiv.org/pdf/1903.06445v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"applying-probabilistic-programming-to","repo_url":"https://github.com/desmond-ong/pplAffComp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"probabilistic-programming","task_name":"Probabilistic Programming"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}