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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\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.1177\/10597123251364746","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T09:54:26Z","timestamp":1754906066000},"page":"307-319","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Evolutionary Factors Contributing to the Emergence of Prediction"],"prefix":"10.1177","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1222-9156","authenticated-orcid":false,"given":"William","family":"Kang","sequence":"first","affiliation":[{"name":"Texas A&M University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher","family":"Anand","sequence":"additional","affiliation":[{"name":"Texas A&M University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin Hyun","family":"Park","sequence":"additional","affiliation":[{"name":"Texas A&M University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1454-4610","authenticated-orcid":false,"given":"Yoonsuck","family":"Choe","sequence":"additional","affiliation":[{"name":"Texas A&M University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,8,11]]},"reference":[{"key":"e_1_3_4_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00429-012-0475-5"},{"key":"e_1_3_4_3_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2012.00147"},{"key":"e_1_3_4_4_1","first-page":"1094","article-title":"Prediction, cognition and the brain","volume":"4","author":"Bubic A.","year":"2010","unstructured":"Bubic A., Von Cramon D. 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ANJI: Another NEAT java implementation."},{"key":"e_1_3_4_9_1","volume-title":"Emergence of prediction in delayed reaching task through neuroevolution","author":"Kang W.","year":"2023","unstructured":"Kang W., Anand C. (2023). Emergence of prediction in delayed reaching task through neuroevolution. Engineering honors in computer science and engineering thesis. Texas A&M University."},{"key":"e_1_3_4_10_1","first-page":"211","volume-title":"From animals to animats 17. SAB 2025. Lecture notes in computer science","author":"Kang W.","year":"2024","unstructured":"Kang W., Anand C., Choe Y. (2024). The role of energy constraints on the evolution of predictive behavior. In From animals to animats 17. SAB 2025. 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