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.

自然语言通常被视为解释人类认知奇点的单一因素。相反，我们建议人类拥有多种内部思维语言，类似于计算机语言，它们编码和压缩各个领域（数学、音乐、形状……）的结构。这些语言依赖于不同于经典语言区域的皮层回路。每个都具有以下特点：（i）使用一小组符号对域进行离散化，以及（ii）将它们递归组合成对嵌套重复进行编码的心理程序。在基本形状或序列感知的各种任务中，所提出的语言中的最小描述长度捕获人类行为和大脑活动，而非人类灵长类动物数据由更简单的非符号模型捕获。