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

Dietrich Stout (1) , Chaminade Thierry (2) , Jan Apel (3) , Ali Shafti (4) , A. Aldo Faisal (4)

1 Emory University [Atlanta, GA]
2 INT - Institut de Neurosciences de la Timone
3 Stockholm University
4 Imperial College London

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.

Domaines: Neurosciences; Anthropologie biologique

Dietrich Stout, Chaminade Thierry, Jan Apel, Ali Shafti, A. Aldo Faisal. The measurement, evolution, and neural representation of action grammars of human behavior. Scientific Reports, 2021, 11, pp.13720. ⟨10.1038/s41598-021-92992-5⟩. ⟨hal-03278117⟩