Avoiding organismic asymmetries in ecological cognition: Analysis of agent-environment couplings with eco-physical variables

SEIFERT, L, ARAÚJO, D and DAVIDS, Keith (2022). Avoiding organismic asymmetries in ecological cognition: Analysis of agent-environment couplings with eco-physical variables. Adaptive Behavior.

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Abstract

The target article promotes an enactive approach to human behaviour, highlighting the phenomenology of agent-environment coupling, and is rooted in the course of experience from pre-reflective self-consciousness. In our comment we debate the idea that experience does equate with subjectivity. Such an equation reflects an organismic asymmetry locating behavioural organisation in the subjective mind, interacting with the objective world. In contrast, an ecological realist perspective considers that human behaviour and experience should be captured at the ecological level of analysis, requiring investigation of eco-physical variables. To achieve this aim, researchers need to avoid organismic asymmetries, and instead study performance variables that underpin the symmetry of the agent-environment system. We also debate the place of language and the fact that verbalisation does not equate with subjective experience. According to James Gibson, language focuses on ‘knowledge about the environment’ and not ‘knowledge of the environment’ needed by any autonomous, self-regulating organism, making their way in the world. Last, the target paper promotes the course of in-formation to complement the course of experience, without fully explaining how to deal with potential incongruence and divergence between findings emerging from verbalisation and behavioural aspects of realizing a given activity (the difference between ‘what we say, what we do’). We conclude by considering how our ecological perspective could offer pathways for the presented enactive approach to go beyond the course of in-formation.

Item Type:	Article
Uncontrolled Keywords:	Artificial Intelligence & Image Processing; 0801 Artificial Intelligence and Image Processing; 1701 Psychology; 1702 Cognitive Sciences
Identification Number:	https://doi.org/10.1177/10597123221119690
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	15 Sep 2022 10:29
Last Modified:	15 Sep 2022 14:08
URI:	https://shura.shu.ac.uk/id/eprint/30711

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