The measurement, evolution, and neural representation of action grammars of human behavior

• Authors: Dietrich Stout, Emory University; Thierry Chaminade, Aix Marseille Université; Jan Apel, Stockholm University; Ali Shafti, Imperial College London; A. Aldo Faisal, Imperial College London
• Journal Title: Scientific Reports
• Volume: Volume 11
• Publisher: Nature Publishing Group | 2021-07-02
• Type of Work: Article | Final Publisher PDF
• Subjects: Sociology, Social Structure and Development
• Keywords: human behavior; toolmaking; language; evolution; hierarchical action sequencing; replication

Abstract

Human behaviors from toolmaking to language are thought to rely on a uniquely evolved capacity for hierarchical action sequencing. Testing this idea will require objective, generalizable methods for measuring the structural complexity of real-world behavior. Here we present a data-driven approach for extracting action grammars from basic ethograms, exemplified with respect to the evolutionarily relevant behavior of stone toolmaking. We analyzed sequences from the experimental replication of ~ 2.5 Mya Oldowan vs. ~ 0.5 Mya Acheulean tools, finding that, while using the same “alphabet” of elementary actions, Acheulean sequences are quantifiably more complex and Oldowan grammars are a subset of Acheulean grammars. We illustrate the utility of our complexity measures by re-analyzing data from an fMRI study of stone toolmaking to identify brain responses to structural complexity. Beyond specific implications regarding the co-evolution of language and technology, this exercise illustrates the general applicability of our method to investigate naturalistic human behavior and cognition.