An Integrative Evolutionary Model of Human Floral Aesthetics: Dynamic Resource Prioritization and Predictive Coding Constraints

Engineering notes

Key topics: autonomous driving, prediction. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

The evolutionary mechanisms driving human aesthetic preference and autonomous nervous stabilization toward floral blooming patches have remained mathematically underspecified in evolutionary biology. This paper presents an integrative theoretical framework bridging optimal foraging theory under space-dispersed resource constraints and visual error minimization under the Free Energy Principle (FEP). We formalize the 137.5-degree golden angle divergence in phyllotaxis as a low-entropy geometric topology that minimizes sensory prediction errors within the primary visual cortex (V1). By employing replicator dynamics, we prove that an adaptive proactive foraging strategy, rooted in floral memory routing for resource preemption, converges deterministically toward a corner solution attractor (x = 1). The resultant surplus of neural computational resources is allocated to high-sensitivity motion detection for environmental noise (predator detection). Human floral aesthetics is thus formalized not as an epiphenomenon of cognitive leisure, but as a thermodynamically optimized navigation and security system engineered for survival.

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