The 37% Rule reveals the mathematics behind human choices, Tinder, choice overload, algorithmic anxiety and decision theory. Discover how the famous Optimal Stopping Problem explains relationships, artificial intelligence, dating apps and why we never seem able to stop searching.
A Regra dos 37% revela a matemática por trás de escolhas humanas, Tinder, excesso de opções, ansiedade algorítmica e teoria da decisão. Descubra como a famosa fórmula do Optimal Stopping Problem explica relacionamentos, inteligência artificial, apps de namoro e por que nunca conseguimos parar de procurar.
Hebbian learning is often reduced to a familiar phrase—“neurons that fire together wire together”—but this simplification obscures a deeper reality: correlation alone does not produce intelligence. This article revisits the foundations of Hebbian learning and expands the discussion into a more complete framework that integrates reinforcement learning and prediction error. Drawing from neuroscience, machine learning, and historical developments since Donald O. Hebb, we show how intelligence emerges not from correlation, but from the interaction between structure, value, and adaptive correction. By connecting local synaptic rules to global learning dynamics—spanning dopaminergic signaling, three-factor learning rules, and temporal difference methods—this article provides a technically grounded and didactic explanation of how modern AI systems and biological brains learn from experience. The central argument is both simple and unsettling: learning systems do not converge to truth, but to what is consistently reinforced. Understanding this distinction is essential for interpreting the limits, risks, and future trajectories of artificial intelligence.