1 Department of Psychology, Harvard University.
2 Center for Brain Science, Harvard University.
Psychol Sci. 2019 Apr;30(4):516-525. doi: 10.1177/0956797619828724. Epub 2019 Feb 13.
People learn differently from good and bad outcomes. We argue that valence-dependent learning asymmetries are partly driven by beliefs about the causal structure of the environment. If hidden causes can intervene to generate bad (or good) outcomes, then a rational observer will assign blame (or credit) to these hidden causes, rather than to the stable outcome distribution. Thus, a rational observer should learn less from bad outcomes when they are likely to have been generated by a hidden cause, and this pattern should reverse when hidden causes are likely to generate good outcomes. To test this hypothesis, we conducted two experiments ( N = 80, N = 255) in which we explicitly manipulated the behavior of hidden agents. This gave rise to both kinds of learning asymmetries in the same paradigm, as predicted by a novel Bayesian model. These results provide a mechanistic framework for understanding how causal attributions contribute to biased learning.
人们从好的和坏的结果中学习。我们认为，与效价相关的学习不对称部分是由对环境因果结构的信念驱动的。如果隐藏的原因可以干预产生坏（或好）的结果，那么理性的观察者就会将责任（或功劳）归咎于这些隐藏的原因，而不是稳定的结果分布。因此，当理性的观察者认为坏的结果可能是由隐藏的原因产生时，他们应该从坏的结果中学到的东西就会减少，而当隐藏的原因可能产生好的结果时，这种模式就会反转。为了检验这一假设，我们进行了两项实验（N=80，N=255），在这些实验中，我们明确地操纵了隐藏代理的行为。正如一个新的贝叶斯模型所预测的那样，这在同一个范式中产生了两种学习不对称。这些结果为理解因果归因如何导致有偏差的学习提供了一个机械框架。
