Julian Zubek, Tomasz Korbak, Joanna Rączaszek-Leonardi
{"title":"Models of symbol emergence in communication: a conceptual review and a guide for avoiding local minima","authors":"Julian Zubek, Tomasz Korbak, Joanna Rączaszek-Leonardi","doi":"10.1007/s10462-024-11048-y","DOIUrl":null,"url":null,"abstract":"<div><p>\n Computational simulations are a popular method for testing hypotheses about the emergence of symbolic communication. This kind of research is performed in a variety of traditions including language evolution, developmental psychology, cognitive science, artificial intelligence, and robotics. The motivations for the models are different, but the operationalisations and methods used are often similar. We identify the assumptions and explanatory targets of the most representative models and summarise the known results. We claim that some of the assumptions—such as portraying meaning in terms of mapping, focusing on the descriptive function of communication, and modelling signals with amodal tokens—may hinder the success of modelling. Relaxing these assumptions and foregrounding the interactions of embodied and situated agents allows one to systematise the multiplicity of pressures under which symbolic systems evolve. In line with this perspective, we sketch the road towards modelling the emergence of meaningful symbolic communication, where symbols are simultaneously grounded in action and perception and form an abstract system.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 2","pages":""},"PeriodicalIF":10.7000,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11048-y.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence Review","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10462-024-11048-y","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Models of symbol emergence in communication: a conceptual review and a guide for avoiding local minima
Computational simulations are a popular method for testing hypotheses about the emergence of symbolic communication. This kind of research is performed in a variety of traditions including language evolution, developmental psychology, cognitive science, artificial intelligence, and robotics. The motivations for the models are different, but the operationalisations and methods used are often similar. We identify the assumptions and explanatory targets of the most representative models and summarise the known results. We claim that some of the assumptions—such as portraying meaning in terms of mapping, focusing on the descriptive function of communication, and modelling signals with amodal tokens—may hinder the success of modelling. Relaxing these assumptions and foregrounding the interactions of embodied and situated agents allows one to systematise the multiplicity of pressures under which symbolic systems evolve. In line with this perspective, we sketch the road towards modelling the emergence of meaningful symbolic communication, where symbols are simultaneously grounded in action and perception and form an abstract system.
期刊介绍:
Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.