How the Human Brain Solves the Symbol-Grounding Problem

Simone Viganò, V. Borghesani, M. Piazza
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Abstract

A fundamental issue in cognitive science is the so-called “symbol-grounding problem” (Harnad 1980), related to the question of how symbols acquire meaning. One simple view posits that, for concrete words, our brain solves the problem by creating associations between the neural representations of the surface forms of symbols (spoken or written words) to the one(s) evoked by the object, action, or event classes the symbols refer to (e.g., see Pulvermuller 2013; 2018). Evidence supporting this view comes from the observation that words related to well known concepts such as numerical quantities (Piazza et al. 2007; Eger et al. 2009), colors (e.g. Simmons et al. 2007), manipulable objects (Chao et al. 1999), places (Kumar et al. 2017), or actions (Hauk 2004; 2011), automatically re-activate the same brain regions that are active during the perception/execution of those specific object features/actions. These data, however, are informative on the neural bases of symbol grounded representations, but not on those underlying symbol grounding: i) they fall short in assessing the role of memory systems implicated in this kind of symbol-toconcept associative learning, and ii) they do not provide a full picture of the effects that symbol grounding has on the brain. Here, to investigate the neural changes generated by this process, we adopted an artificial learning paradigm where 21 adult subjects learned to categorize novel multisensory objects by giving them specific symbolic labels.
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人类大脑如何解决符号基础问题
认知科学中的一个基本问题是所谓的“符号基础问题”(Harnad 1980),与符号如何获得意义的问题有关。一种简单的观点认为,对于具体的单词,我们的大脑通过在符号的表面形式(口语或书面文字)的神经表征与符号所指的对象、动作或事件类所唤起的表征之间建立联系来解决问题(例如,参见粉状穆勒2013;2018)。支持这一观点的证据来自于对与众所周知的概念相关的词汇的观察,如数值量(Piazza et al. 2007;Eger等人,2009),颜色(例如Simmons等人,2007),可操作对象(Chao等人,1999),地点(Kumar等人,2017)或动作(Hauk 2004;2011),自动重新激活在感知/执行这些特定对象特征/动作期间活跃的相同大脑区域。然而,这些数据在符号基础表征的神经基础上提供了信息,但在那些潜在的符号基础上却没有:1)它们在评估涉及这种从符号到概念的联想学习的记忆系统的作用方面存在不足,2)它们没有提供符号基础对大脑的影响的全貌。为了研究这一过程所产生的神经变化,我们采用了人工学习范式,让21名成年受试者通过给予特定的符号标签来学习对新的多感官物体进行分类。
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