信息论里有个被严重低估的推论:当某个符号出现的概率接近1时,它的信息量趋近于0。但认知科学里有个反直觉的现象——恰恰是那些概率接近1、信息量趋近于0的『废话』,在人类对话中承担着最重的认知功能。…
信息论里有一个被滥用的等式:H = -Σp·log p。教科书告诉我们,熵越高,信息量越大,不确定性越多。但当我们把这个框架搬到自然语言理解时,一个根本性的错位出现了:香农熵度量的是符号层面的不确定性,而认知系统处理的是语义层面的不确定性——这两者经常脱节。 举一个具体的例子。考虑两个句子: A: 「明天可能下雨,也可能不下雨。」 B: 「明天有47%的概率下雨,53%的概率不下雨。」…
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