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.
Highlights
Human behaviors from toolmaking to language are thought to rely on a uniquely evolved capacity for hierarchical action sequencing
The optimal number of hidden states provides a measure of structural complexity
The fit was better for both models on the simpler Oldowan sequences
Summary
Human behaviors from toolmaking to language are thought to rely on a uniquely evolved capacity for hierarchical action sequencing. We adopt a data-driven computational approach to this challenge by using grammatical pattern recognition algorithms to measure the structural complexity of behavioral sequences from modern tool-making replication experiments—effectively extracting action grammars for critical survival skills from the human evolutionary past. This allows us to isolate and compare the structural complexity of “noisy” natural behaviors that simultaneously vary across a wide range of other perceptual, motor, and kinematic dimensions, including identification of specific brain responses to this complexity. This sample includes 5 sequences for which upper limb movements and manual joint angles were recorded as part of a previous study[27], and 6 for which the tools and waste produced were analyzed and compared imperial.ac.uk
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