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Research Article
Dietrich Stout
Emory University
Corresponding Author
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Human behaviors from tool-making 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 tool-making. 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 tool-making 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.
Keywords
Human behaviors, data-driven approach, capacity for hierarchical action sequencing
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This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Dietrich Stout
Emory University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 15 Mar, 2021
Community comments: 1
Review #1 received
Received 16 Mar, 2021
Review #7 received
Received 16 Mar, 2021
Review #6 received
Received 16 Mar, 2021
Review #4 received
Received 16 Mar, 2021
Review #5 received
Received 16 Mar, 2021
Reviewer #3 agreed
On 12 Mar, 2021
Reviewer #8 agreed
On 12 Mar, 2021
Reviewer #14 agreed
On 12 Mar, 2021
Reviewer #2 agreed
On 12 Mar, 2021
Reviewer #5 agreed
On 12 Mar, 2021
Reviewer #4 agreed
On 12 Mar, 2021
Reviewer #1 agreed
On 12 Mar, 2021
Reviewer #7 agreed
On 12 Mar, 2021
Reviewer #13 agreed
On 12 Mar, 2021
Reviewer #11 agreed
On 12 Mar, 2021
Reviewer #12 agreed
On 12 Mar, 2021
Reviewer #6 agreed
On 12 Mar, 2021
Reviewers invited
Invitations sent on 12 Mar, 2021
Editor assigned
On 12 Mar, 2021
Editor invited
On 12 Mar, 2021
Submission checks complete
On 12 Mar, 2021
First submitted
On 01 Mar, 2021
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Human behaviors from tool-making 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 tool-making. 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 tool-making 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.
Figure 1
Figure 2
Figure 3
Figure 4
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