Modeling a Cognitive Transition at the Origin of Cultural Evolution using Autocatalytic Networks
Liane Gabora, Mike Steel

TL;DR
This paper models the emergence of complex cognitive structures and cultural evolution using autocatalytic networks, illustrating the transition from simple stone tools to sophisticated, self-organizing semantic networks that underpin cultural development.
Contribution
It introduces a formal autocatalytic network model for cognitive and cultural transitions, specifically modeling the shift from Oldowan to Acheulean tools and the emergence of hierarchical thought.
Findings
Model demonstrates how cognitive structures become self-sustaining and autocatalytic.
Shows the development of hierarchical mental representations like the Acheulean hand axe.
Explains the prolonged cultural stasis through self-organizing semantic networks.
Abstract
Autocatalytic networks have been used to model the emergence of self-organizing structure capable of sustaining life and undergoing biological evolution. Here, we model the emergence of cognitive structure capable of undergoing cultural evolution. Mental representations of knowledge and experiences play the role of catalytic molecules, and interactions amongst them (e.g., the forging of new associations) play the role of reactions, and result in representational redescription. The approach tags mental representations with their source, i.e., whether they were acquired through social learning, individual learning (of pre-existing information), or creative thought (resulting in the generation of new information). This makes it possible to model how cognitive structure emerges, and to trace lineages of cumulative culture step by step. We develop a formal representation of the cultural…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
