An autopoietic approach to cultural transmission chains
Alex Papadopoulos-Korfiatis (Edinburgh)
Tuesday 28 February 2017, 11:00–12:30
1.17 Dugald Stewart Building
One of the problems of autopoiesis as a biological, bottom-up, non-representational theory of cognition is that it struggles with scaling up to high-level cognitive behaviour such as language. The Iterated Learning model, a theory of language evolution based on its transmission from agent to agent in cultural chains, is a promising candidate in providing the first step towards a non-representational account of language; our goal in this work is the combination of these two approaches. In order to do that, we introduce a minimal joint action “left/right dancing” task that can be solved in multiple ways. Through individual episodes of reinforcement learning between simulated robotic agents, we show that an initial expert agent’s behaviour persists in cultural transmission chains; we investigate the conditions under which these chains break down and re-emerge, drawing interesting parallels to existing Iterated Learning research.