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

• Type of Material: Article
• 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
• Language: English
• Date: 2021-07-02
• Publisher: Nature Publishing Group
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2021
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): https://www.nature.com/articles/s41598-021-92992-5
• Title of Journal or Parent Work: Scientific Reports
• Volume: 11
• Grant/Funding Information: This work was supported by the Commission of the European Communities Research Directorate-General Specific Targeted Project Number 029065, “Hand to Mouth: A framework for understanding the archaeological and fossil records of human cognitive evolution” and National Science Foundation (USA) Grant SMA-1328567 and a UKRI Turing AI Fellowship Grant EP/V025449/1 (AAF).
• Keywords: hierarchical action sequencing; language; replication; toolmaking; evolution; human behavior
• Research Categories: Sociology, Social Structure and Development

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.