Evolutionary Factors Contributing to the Emergence of Prediction
Year: 2025
Type: article
Abstract: Prediction is an important foundation of cognitive and intelligent behavior. However, how such predictive capabilities emerged from simple organisms has not been investigated fully. Our prior works have shown the relationship between input delay and predictive function. Furthermore, we showed that environmental change can help predictive properties to evolve. In this paper, we investigate two other key factors contributing to the evolution of prediction. We set up a reaching task with a two-segment articulated arm with the goal of touching a moving target. In Task 1, the target’s location is received with a delay in the sensors. In Task 2, we introduced occlusion in the form of input blank-out. When the hand is too close to the moving target, the target disappears from the sensors, and reappears when it is farther than a threshold. In both tasks, prediction is needed to keep track of the target’s correct location. For the controller, we used the NeuroEvolution of Augmenting Topologies (NEAT) algorithm. Our results from Task one indicate that an important fitness criterion for the emergence of predictive behavior is energy conservation. The results from Task two show that more occlusion leads to network types with stronger predictive power become more successful. Through our prior and current experiments, we identified four seemingly unrelated and unlikely factors that may have led to the evolution of prediction: delay, environmental change, energy constraint, and occlusion. These are prevalent conditions in the natural environment, thus it seems inevitable that predictive capabilities will emerge in evolving agents.
Source: Adaptive Behavior
Authors: William Kang, Christopher Anand, Jin Hyun Park, Yoonsuck Choe
Institution: Texas A&M University
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $4,150
OpenAlex ID: W4413127585