Shanks D R
Department of Psychology, University College London.
Q J Exp Psychol A. 1995 May;48(2):257-79. doi: 10.1080/14640749508401390.
We can predict and control events in the world via associative learning. Such learning is rational if we come to believe that an associative relationship exists between a pair of events only when it truly does. The statistical metric delta P, the difference between the probability of an outcome event in the presence of the predictor and its probability in the absence of the predictor tells us when and to what extent events are indeed related. Contrary to what is often claimed, humans' associative judgements compare very favourably with the delta P metric, even in situations where multiple predictive cues are in competition for association with the outcome. How do humans achieve this judgmental accuracy? I argue that it is not via the application of an explicit mental version of the delta P rule. Instead, accurate judgements are an emergent property of an associationist learning process of the sort that has become common in adaptive network models of cognition. Such an associationist mechanism is the "means" to a normative or statistical "end".
我们可以通过联想学习来预测和控制世界上的事件。如果我们只有在一对事件之间真正存在关联关系时才开始相信它们之间存在关联，那么这种学习就是合理的。统计指标δP，即预测因素存在时结果事件的概率与预测因素不存在时结果事件的概率之差，告诉我们事件何时以及在多大程度上确实相关。与通常的说法相反，人类的联想判断与δP指标相比非常有利，即使在多个预测线索竞争与结果建立关联的情况下也是如此。人类是如何实现这种判断准确性的呢？我认为这不是通过应用δP规则的明确心理版本来实现的。相反，准确的判断是一种联想主义学习过程的涌现属性，这种过程在认知的自适应网络模型中已经很常见。这样一种联想主义机制是达到规范或统计“目的”的“手段”。
