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  • 标题:仮説検証サイクルと記号接地
  • 本地全文:下载
  • 作者:橋田 浩一 ; 嶋田 総太郎 ; 今井 むつみ
  • 期刊名称:認知科学
  • 印刷版ISSN:1341-7924
  • 电子版ISSN:1881-5995
  • 出版年度:2016
  • 卷号:23
  • 期号:1
  • 页码:65-73
  • DOI:10.11225/jcss.23.65
  • 出版社:Japanese Cognitive Science Society
  • 摘要:Language acquisition is a process of symbol grounding, which is construction of sym- bol systems adapted to environment. Environmental adaptation defines the values which cognitive agents pursue primarily by means of hypothesis-test cycles encompass- ing both the inside and outside of their bodies. In addition to these directly grounded cycles, there are also hypothesis-test cycles within cognitive agents. Cognitive processes are combinations of these cycles, where cycles embody typical cognitive phenomena such as navigation and language use. Cycles are essentially countable, so that systems comprising cycles necessarily have discrete structures. A cognitive agent is hence formulated as a discrete system consist- ing of cycles including both directly grounded cycles and symbols (indirectly grounded cycles), where each cycle embodies some value or meaning directly or indirectly associ- ated with environmental adaptation. Computational models of cognition as combina- tion of such cycles (values = meanings) are far more efficient (simpler and less prone to overdesign) than traditional models stipulating possibly non-cyclic information flows. Environmental-adaptation cycles operate at multiple spatiotemporal scales, including real-time adaptive behavior, middle-term learning, and evolution across generations. It is vitally important to address real-time adaptation behavior in terms of cycles, which will raise the efficiency of the computational model not only at the level of real-time adaptation but also accordingly at higher levels. Cycle-based (meaning-based) com- putational models are necessary also because cycles derive meta-level constraints such as symmetry bias and naming insight, which are indispensable for abductive reasoning and language acquisition. Existing technologies including deep learning fail to reflect such a value-based (meaning-based) architecture of cognition. For the sake of thorough symbol grounding, novel approaches are necessary which should integrate environmental-adaptation cycles in the entire computational model at multiple levels of meaning and value.
  • 关键词:記号接地;言語獲得;仮説検証サイクル;意味の重層性;メタレベル;symbol grounding;language acquisition;hypothesistest cycle;multiplicity of meaning;meta-level
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