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Article dans une revue Scientific Reports Année : 2021

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

Résumé

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.
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Dates et versions

hal-03278117, version 1 (05-07-2021)

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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⟩
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