别总盯着模型分数，人怎么用AI才是关键。这篇论文发现，能跟AI互补的人靠的是好奇心而非高智商。
该论文以Polymarket真实金钱市场作基准，分析个体预测者与AI协作表现呈三模态：多数人要么完全依赖模型（成绩持平模型），要么用模型背书原有猜测（成绩比单独模型更差），仅少数人进行真正互补推理，准确率匹配甚至超过市场本身。协作特质（观点采择、知识谦逊、好奇心）比原始认知能力或模型基准更能区分高绩效人群。结果初步但统计显著，预注册复制研究正在进行。
Human Capital, Not Model Benchmarks, Predicts Hybrid Intelligence in Forecasting
Whether pairing people with AI helps or hurts is usually reported as a single average effect. Using a real-money prediction market (Polymarket) as an objective, externally resolved benchmark, this pilot shows that the value of human-AI collaboration depends on a specific, measurable form of human capital. Analyzed at the level of the individual forecaster, hybrid performance is trimodal: most people either deferred to the model (matching it) or used it to rubber-stamp a prior guess (performing worse than the model alone), while a minority engaged in genuine complementary reasoning and reached accuracy matching or even exceeding (i.e., lower error than) the market itself. Collaborative traits (perspective-taking, intellectual humility, and curiosity) rather than raw cognitive ability or model benchmarks, distinguished who reached that mode. The results are preliminary but statistically robust, and motivate a pre-registered replication now in preparation.