Intelligence without representation
Artificial Intelligence
Exploring adaptive agency II: simulating the evolution of associative learning
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Learning and evolution in neural networks
Adaptive Behavior
Evolution, learning, and instinct: 100 years of the baldwin effect
Evolutionary Computation
| Hi-index | 0.00 |
This research explores the relation between environmental structure and neurocognitive structure. We hypothesize that selection pressure on abilities for efficient learning (especially in settings with limited or no reward information) translates into selection pressure on correspondence relations between neurocognitive and environmental structure, since such correspondence allows for simple changes in the environment to be handled with simple learning updates in neurocognitive structure. We present a model in which a simple formof reinforcement-free learning is evolved in neural networks using neuromodulation and analyze the effect this selection for learning ability has on the virtual species' neural organization. We find a higher degree of organization than in a control population evolved 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, the Baldwin effect, and other interactions between adaptation processes.