aiqbench analysis
This study uses neuro-evolutionary agents to challenge the Cognitive Buffer Hypothesis, demonstrating that large neural networks capable of handling dynamic environments often emerge from prior evolution in stable conditions rather than direct adaptation to change. The findings provide computational evidence that evolutionary history is a critical factor in developing robust architectures for unpredictable scenarios, offering insights relevant to training resilient AI systems.
Big Brains and Changing Environments: Cause or Consequence?
arXiv:2609.15569v1 Announce Type: cross Abstract: Large brains are metabolically costly, and associations with changing environments do not imply they evolved there, as the Cognitive Buffer Hypothesis (CBH) would suggest. They may instead evolve in stable conditions and later facilitate colonization of changing environments. Using neuro-evolution in an artificial seasonal foraging task, we compared agents evolving exclusively in changing environments to agents first evolved in static environments before transitioning. Results show that larger neural networks in dynamic environments arise mainly from prior static evolution, achieving superior performance under unpredictable changes. Our results challenge strict CBH predictions, provide agent-based (computational) support for a colonization-based account and highlight the role of evolutionary history in brain size evolution.
Title:Big Brains and Changing Environments: Cause or Consequence?
Abstract:Large brains are metabolically costly, and associations with changing environments do not imply they evolved there, as the Cognitive Buffer Hypothesis (CBH) would suggest. They may instead evolve in stable conditions and later facilitate colonization of changing environments. Using neuro-evolution in an artificial seasonal foraging task, we compared agents evolving exclusively in changing environments to agents first evolved in static environments before transitioning. Results show that larger neural networks in dynamic environments arise mainly from prior static evolution, achieving superior performance under unpredictable changes. Our results challenge strict CBH predictions, provide agent-based (computational) support for a colonization-based account and highlight the role of evolutionary history in brain size evolution.
Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Populations and Evolution (q-bio.PE)
arXiv:2609.15569 [cs.NE]
(or arXiv:2609.15569v1 [cs.NE] for this version)
https://doi.org/10.48550/arXiv.2609.15569 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1145/3795101.3805275 Focus to learn more DOI(s) linking to related resources
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Full text under the publisher's open licence · 230 words
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