This research explores the relation between environmental organization and cognitive organization... more This research explores the relation between environmental organization and cognitive organization. We hypothesize that selection pressure on learning ability indirectly causes selection pressure on alignment of neuro-cognitive and environmental structure, since such alignment implies that small changes in the environment can be handled with small changes in the implementation of behaviour. We indicate reinforcement-free types of learning ability as most strongly reliant on such alignment. We present a model in which a simple form of reinforcement-free learning is evolved in neural networks, and analyze the effect this has on the virtual species' neural organization. We find a higher degree of organization than in a control population without learning ability, and discuss the relation between the observed neural structure and the environmental structure. We discuss our findings in the context of the environmental complexity thesis and the Baldwin effect.
The environmental complexity thesis states that environmental complexity is the driving force beh... more The environmental complexity thesis states that environmental complexity is the driving force behind the evolution of cognition. Herbert Spencer held a particularly strong version of this view, and believed that life and mind can be understood as reflections of the environment they evolved in. However, Spencer's view does not account for the possibility of fit but diffusely implemented behaviour. As connectionist AI has amply demonstrated, fit behaviour does not by itself necessitate any isomorphism between a species' neuro-cognitive organization and the environment. We suggest supplementing Spencer's view with an account of the selection pressures that would cause evolution to organize cognition after the environment, and identify selection pressure on learning ability as a candidate. We argue that the more a species' neuro-cognitive organization resembles the organization of the environment, the easier it is to make appropriate updates in behaviour. We discuss various types of learning ability as it occurs in nature, and identify latent learning as the type most likely to constrain neuro-cognitive organization. We then build an Artificial Life model of the evolution of latent learning, and compare the structures of networks evolved under selection pressure for latent learning with networks evolved in absence of such selection pressure. Unlike the latter, the former repeatedly evolved the same compact behaviour system, which innately encodes some of the spatial relations of the environment. Our results indicate that selection pressure on learning ability can indeed guide evolution towards forms of neuro-cognitive organization that reflect environmental features
This research explores the relation between environmental organization and cognitive organization... more This research explores the relation between environmental organization and cognitive organization. We hypothesize that selection pressure on learning ability indirectly causes selection pressure on alignment of neuro-cognitive and environmental structure, since such alignment implies that small changes in the environment can be handled with small changes in the implementation of behaviour. We indicate reinforcement-free types of learning ability as most strongly reliant on such alignment. We present a model in which a simple form of reinforcement-free learning is evolved in neural networks, and analyze the effect this has on the virtual species' neural organization. We find a higher degree of organization than in a control population without learning ability, and discuss the relation between the observed neural structure and the environmental structure. We discuss our findings in the context of the environmental complexity thesis and the Baldwin effect.
The environmental complexity thesis states that environmental complexity is the driving force beh... more The environmental complexity thesis states that environmental complexity is the driving force behind the evolution of cognition. Herbert Spencer held a particularly strong version of this view, and believed that life and mind can be understood as reflections of the environment they evolved in. However, Spencer's view does not account for the possibility of fit but diffusely implemented behaviour. As connectionist AI has amply demonstrated, fit behaviour does not by itself necessitate any isomorphism between a species' neuro-cognitive organization and the environment. We suggest supplementing Spencer's view with an account of the selection pressures that would cause evolution to organize cognition after the environment, and identify selection pressure on learning ability as a candidate. We argue that the more a species' neuro-cognitive organization resembles the organization of the environment, the easier it is to make appropriate updates in behaviour. We discuss various types of learning ability as it occurs in nature, and identify latent learning as the type most likely to constrain neuro-cognitive organization. We then build an Artificial Life model of the evolution of latent learning, and compare the structures of networks evolved under selection pressure for latent learning with networks evolved in absence of such selection pressure. Unlike the latter, the former repeatedly evolved the same compact behaviour system, which innately encodes some of the spatial relations of the environment. Our results indicate that selection pressure on learning ability can indeed guide evolution towards forms of neuro-cognitive organization that reflect environmental features
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Papers by Solvi Arnold