[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-174948-105":53,"doc-detail-174948-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","human-sense-of-humor","Human Sense of Humor","","Explores whether computers can develop a sense of humor and why humor understanding is difficult for language-based systems. Summarizes major formal humor theories, especially incongruity and the incongruity-resolution framework, explaining how setup–punchline reinterpretation can trigger reward-related responses in humans. Connects these cognitive requirements to “AI-complete” challenges and outlines practical implications of computational humor for tasks like detecting and generating jokes.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/human-sense-of-humor/174948/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/human-sense-of-humor/174948.png","ImageObject",442,249,{"name":88,"@type":89},"Oliver","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/vnd.openxmlformats-officedocument.wordprocessingml.document","2026-10-16","2026-09-02",true,{"@type":98,"interactionType":99,"userInteractionCount":73},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"Why is it difficult for machines to detect or generate humor?","Question",{"text":108,"@type":109},"Humor depends on frame-shifting insight and semantic integration, so systems must resolve incongruity in a way that humans find meaningful. This often requires human-level cognition or error-prone heuristics.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"What is the incongruity-resolution (IR) theory of humor?",{"text":113,"@type":109},"Humor arises when an incongruity created by a joke’s setup is resolved after the punchline, revealing an overarching compatible interpretation. The response is linked to reward-related processing.",{"name":115,"@type":106,"acceptedAnswer":116},"How do computational humor systems enable practical applications?",{"text":117,"@type":109},"By training computers to perform humor-related tasks such as detecting or generating humorous content. These capabilities can support applications that help users navigate and select humor.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},174948,1792162288,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":73,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":40,"language":135,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":136,"faqs":137,"seo_title":138,"seo_description":61,"update_tm":139,"read_time":140},8796095461610,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Computers Learning Humor Is No Joke\nThomas Winters\nColumn Editor’s Note: Machine generation and comprehension of language is a difficult enough task, but it's not even funny how difficult it is for a machine to produce and detect humor. Data scientist Thomas Winters explains the reasons for this challenge, and addresses how much humor a computer can learn. (Spoiler: A bit).\nKeywords: computational humor, humor detection, humor generation\nCan a computer have a sense of humor? On reflection, this question may seem paradoxical given that humor is such an intrinsically human trait. While some primates produce laughter (\u0013 HYPERLINK \\l \"ref-Gervais2005evolution\" \\h \u0014Gervais & Wilson, 2005\u0015); (\u0013 HYPERLINK \\l \"ref-Preuschoft1997social\" \\h \u0014Preuschoft & Hooff, 1997\u0015), humans are the only known species that use humor for making others laugh. Furthermore, every known human civilization also has had at least some form of humor for making others laugh (\u0013 HYPERLINK \\l \"ref-Caron2002From\" \\h \u0014Caron, 2002\u0015); (\u0013 HYPERLINK \\l \"ref-Gervais2005evolution\" \\h \u0014Gervais & Wilson, 2005\u0015). Given this innately and intrinsically human skill, it might seem implausible for a machine to have any form of a sense of humor. In this article, we explore several computational techniques that have been used to detect and generate humor, and how these systems can help us improve our humor abilities and gain more insights into this uniquely human trait.\nHuman Sense of Humor\nOver the whole of human history, many scholars investigated the inner workings of humor, resulting in an abundance of different theories for explaining verbally expressed humor. While no generally accepted all-encompassing theory of humor exists, most modern formal humor theories point to ‘incongruity’ as one of the important basic elements of humor (\u0013 HYPERLINK \\l \"ref-Gervais2005evolution\" \\h \u0014Gervais & Wilson, 2005\u0015); (\u0013 HYPERLINK \\l \"ref-Hurley2011Inside\" \\h \u0014Hurley et al., 2011\u0015); (\u0013 HYPERLINK \\l \"ref-Morreall1986philosophy\" \\h \u0014Morreall, 1986\u0015). Incongruity denotes the incoherence between the interpretation of two parts of a joke, namely, its setup and punchline. One such popular incongruity theory is the incongruity-resolution (IR) theory, which is especially useful for computational models due to its mechanical (rather than descriptive) nature. The IR theory states that humor arises when an incongruity is resolved, meaning that one overarching, compatible interpretation for the whole text is discovered after hearing the punchline (\u0013 HYPERLINK \\l \"ref-Ritchie1999Developing\" \\h \u0014Ritchie, 1999\u0015); (\u0013 HYPERLINK \\l \"ref-Shultz1974Development\" \\h \u0014Shultz, 1974\u0015); (\u0013 HYPERLINK \\l \"ref-Suls1972two\" \\h \u0014Suls, 1972\u0015). Others argued that incongruity and resolution are insufficient conditions for humor, stating that the interpretations must not replace one another and that the second interpretation must be of lower value than the first (\u0013 HYPERLINK \\l \"ref-Apter1982experience\" \\h \u0014Apter, 1982\u0015); (\u0013 HYPERLINK \\l \"ref-Wyer1992theory\" \\h \u0014Wyer & Collins, 1992\u0015). Another theory suggests that the two interpretations must overlap while also oppose one another to create humor (\u0013 HYPERLINK \\l \"ref-Raskin1984Semantic\" \\h \u0014Raskin, 1984\u0015). As an example of IR, consider the following joke:\nTwo fish are in a tank. Says one to the other:\u000b“You man the guns, I’ll drive.”\nAccording to the IR theory, the setup creates an initial interpretation of two fish being in an aquarium. The punchline breaks this mental image, as aquaria generally lack weaponry and controllable mobility. Our brain thereupon searches for a reasonable explanation to fix this incongruity by considering previously heard text, and likely discovers the hidden interpretation that the fish were in a military vehicle all along. Resolving this incongruity triggers our brain to release reward-related responses in its subcortical structures, causing the feeling of mirth and evoking laughter (\u0013 HYPERLINK \\l \"ref-Chan2012Segregating\" \\h \u0014Chan et al., 2012\u0015). T","cbCaiuJKx1gHmJch","https://ap.wps.com/l/cbCaiuJKx1gHmJch","docx",35860,"English","# Can a computer have a sense of humor?\n# Human Sense of Humor\n## Incongruity and incongruity-resolution\n## Cognitive requirements for humor\n# Computational Humor Applications","[{\"question\":\"Why is it difficult for machines to detect or generate humor?\",\"answer\":\"Humor depends on frame-shifting insight and semantic integration, so systems must resolve incongruity in a way that humans find meaningful. This often requires human-level cognition or error-prone heuristics.\"},{\"question\":\"What is the incongruity-resolution (IR) theory of humor?\",\"answer\":\"Humor arises when an incongruity created by a joke’s setup is resolved after the punchline, revealing an overarching compatible interpretation. The response is linked to reward-related processing.\"},{\"question\":\"How do computational humor systems enable practical applications?\",\"answer\":\"By training computers to perform humor-related tasks such as detecting or generating humorous content. These capabilities can support applications that help users navigate and select humor.\"}]","Human Sense of Humor | DOCX",1788317524,6]