{"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/where-is-my-forearm-clustering-of-body-parts","title":"Where is my forearm? Clustering of body parts from simultaneous tactile and linguistic input using sequential mapping","arxiv_id":"1706.02490","date":"2017-06-08","proceeding":null,"authors":["Karla Stepanova","Matej Hoffmann","Zdenek Straka","Frederico B. Klein","Angelo Cangelosi","Michal Vavrecka"],"abstract":"Humans and animals are constantly exposed to a continuous stream of sensory\ninformation from different modalities. At the same time, they form more\ncompressed representations like concepts or symbols. In species that use\nlanguage, this process is further structured by this interaction, where a\nmapping between the sensorimotor concepts and linguistic elements needs to be\nestablished. There is evidence that children might be learning language by\nsimply disambiguating potential meanings based on multiple exposures to\nutterances in different contexts (cross-situational learning). In existing\nmodels, the mapping between modalities is usually found in a single step by\ndirectly using frequencies of referent and meaning co-occurrences. In this\npaper, we present an extension of this one-step mapping and introduce a newly\nproposed sequential mapping algorithm together with a publicly available Matlab\nimplementation. For demonstration, we have chosen a less typical scenario:\ninstead of learning to associate objects with their names, we focus on body\nrepresentations. A humanoid robot is receiving tactile stimulations on its\nbody, while at the same time listening to utterances of the body part names\n(e.g., hand, forearm and torso). With the goal at arriving at the correct \"body\ncategories\", we demonstrate how a sequential mapping algorithm outperforms\none-step mapping. In addition, the effect of data set size and noise in the\nlinguistic input are studied.","url_abs":"http://arxiv.org/abs/1706.02490v1","url_pdf":"http://arxiv.org/pdf/1706.02490v1.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":"where-is-my-forearm-clustering-of-body-parts","repo_url":"https://github.com/stepakar/sequential-mapping","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}