Grounding Synthetic Knowledge: An Epistemological Framework and Criteria of Relevance for the Scientific Exploration of Life, Affect and Social Cognition

TitleGrounding Synthetic Knowledge: An Epistemological Framework and Criteria of Relevance for the Scientific Exploration of Life, Affect and Social Cognition
Publication TypeConference Paper
Year of Publication2011
AuthorsDamiano, L, Hiolle, A, Cañamero, L
EditorLenaerts, T, Giacobini, M, Bersini, H, Bourgine, P, Dorigo, M, Doursat, R
Name of ProceedingsAdvances In Artificial Life, ECAL 2011 (Proc. 11th European Conference on Artificial Life)
Pagination200–207
PublisherMIT Press
Conference LocationParis, France
ISBN Number978-0-262-29714-1
Abstract

In what ways can artificial life contribute to the scientific exploration of cognitive, affective and social processes? In what sense can synthetic models be relevant for the advancement of behavioral and cognitive sciences? This article addresses these questions by way of a case study — an interdisciplinary cooperation between developmental robotics and developmental psychology in the exploration of attachment bonds. Its main aim is to show how the synthetic study of cognition, as well as the synthetic study of life, can find in autopoietic cognitive biology more than a theory useful to inspire the synthetic modelling of the processes under inquiry. We argue that autopoiesis offers, not only to artificial life, but also to the behavioural and social sciences, an epistemological framework able to generate general criteria of relevance for synthetic models of living and cognitive processes. By “criteria of relevance” we mean criteria (a) valuable for the three main branches of artificial life (soft, hard, and wet) and (b) useful for determining the significance of the models each branch produces for the scientific exploration of life and cognition. On the basis of these criteria and their application to the case study presented, this article defines a range of different ways that synthetic, and particularly autopoiesis-based models, can be relevant to the inquiries of biological, behavioural and cognitive sciences.

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