Dehaene Stanislas, Al Roumi Fosca, Lakretz Yair, Planton Samuel, Sablé-Meyer Mathias
Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France; Collège de France, Université Paris-Sciences-Lettres (PSL), 11 Place Marcelin Berthelot, 75005 Paris, France.
Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France.
Trends Cogn Sci. 2022 Sep;26(9):751-766. doi: 10.1016/j.tics.2022.06.010. Epub 2022 Aug 3.
Natural language is often seen as the single factor that explains the cognitive singularity of the human species. Instead, we propose that humans possess multiple internal languages of thought, akin to computer languages, which encode and compress structures in various domains (mathematics, music, shape…). These languages rely on cortical circuits distinct from classical language areas. Each is characterized by: (i) the discretization of a domain using a small set of symbols, and (ii) their recursive composition into mental programs that encode nested repetitions with variations. In various tasks of elementary shape or sequence perception, minimum description length in the proposed languages captures human behavior and brain activity, whereas non-human primate data are captured by simpler nonsymbolic models. Our research argues in favor of discrete symbolic models of human thought.
自然语言常常被视为解释人类物种认知独特性的唯一因素。相反，我们提出人类拥有多种内在思维语言，类似于计算机语言，它们对各个领域（数学、音乐、形状……）的结构进行编码和压缩。这些语言依赖于与经典语言区域不同的皮质回路。每种语言的特点是：（i）使用一小套符号对一个领域进行离散化，以及（ii）将它们递归组合成心理程序，这些程序对带有变化的嵌套重复进行编码。在各种基本形状或序列感知任务中，所提出语言中的最小描述长度能够捕捉人类行为和大脑活动，而非人类灵长类动物的数据则由更简单的非符号模型捕捉。我们的研究支持人类思维的离散符号模型。
