The origin and function of external representations

被引:1
|
作者
Fernando, Chrisantha [1 ]
Osindero, Simon [1 ]
Banarse, Dylan [1 ]
机构
[1] Google DeepMind, 8 Handyside St, London N1C 4AG, England
关键词
Language; science; art; cognitive archaeology; machine learning; large language models; representation; MIDDLE STONE-AGE; HUMAN BRAIN; LANGUAGE-ACQUISITION; NONHUMAN-PRIMATES; EVOLUTION; EMERGENCE; THINKING; FUTURE; SELF; CONSCIOUSNESS;
D O I
10.1177/10597123241262534
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
External representations (ERs) are objects or performances in the world whose proper function is to communicate about other things in the world. Why and how did we make them, and what do they give us? We outline a simple framework for thinking about ERs grounded in modern machine learning. We propose a minimal set of neural mechanisms needed for open-ended ER production. Our constructivist enactive view contrasts with nativist views which propose specialist neural modules requiring symbolic internal representations. We propose a plausible set of (biological and cultural) evolutionary steps to full Gricean symbolic communication via a set of increasingly complex enactive algorithms for ER production. A pragmatic space of games is defined, which includes not only fully cooperative language games but also science, art, and evolved signal manipulation games. This space is defined by the complexity of learning needed by sender and receiver. We propose that one important step towards open-ended ER use was selection for bush reading, which like mind-reading is an inferential process requiring complex contextual and syntactic understanding of cues about events displaced in space and time. Bush reading pre-adapted receivers to be receptive, competent, and perspicacious interpreters of later intentionally produced signals about hidden topics such as felt mental states. This paved the way for minimally Gricean communication, which subsequently could be bootstrapped into explicit theories of mind, folk psychology narratives, and symbolic language in general. Recent findings in cognitive archaeology are integrated within the framework, and new experiments in machine learning suggested.
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页码:515 / 549
页数:35
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