{"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/neural-models-of-the-psychosemantics-of-most","title":"Neural Models of the Psychosemantics of `Most'","arxiv_id":"1904.02734","date":"2019-04-04","proceeding":"WS 2019 6","authors":["Lewis O'Sullivan","Shane Steinert-Threlkeld"],"abstract":"How are the meanings of linguistic expressions related to their use in\nconcrete cognitive tasks? Visual identification tasks show human speakers can\nexhibit considerable variation in their understanding, representation and\nverification of certain quantifiers. This paper initiates an investigation into\nneural models of these psycho-semantic tasks. We trained two types of network\n-- a convolutional neural network (CNN) model and a recurrent model of visual\nattention (RAM) -- on the \"most\" verification task from \\citet{Pietroski2009},\nmanipulating the visual scene and novel notions of task duration. Our results\nqualitatively mirror certain features of human performance (such as sensitivity\nto the ratio of set sizes, indicating a reliance on approximate number) while\ndiffering in interesting ways (such as exhibiting a subtly different pattern\nfor the effect of image type). We conclude by discussing the prospects for\nusing neural models as cognitive models of this and other psychosemantic tasks.","url_abs":"http://arxiv.org/abs/1904.02734v1","url_pdf":"http://arxiv.org/pdf/1904.02734v1.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":"neural-models-of-the-psychosemantics-of-most","repo_url":"https://github.com/shanest/neural-vision-most","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}