Bayesian Models of Individual Differences
作者研究了感知和认知中的个体差异如何由贝叶斯模型解释。根据贝叶斯理论，感知依赖于对噪声证据和先验知识的最优整合，因此个体差异应由对证据的敏感性和先验期望共同决定。作者提出自闭症个体的先验分布更平坦，即先验方差更大，且这种差异与普通人群中自闭特质程度相关。通过测量追踪眼动和低对比度条件下感知速度的变化，作者发现这两个运动现象的个体差异可由阈值和自闭特质通过定量贝叶斯模型预测。结果支持了“更平坦先验”假说，表明先验期望的个体差异比以往认为的更系统化，但揭示这些差异时需同时考虑敏感性差异。
According to Bayesian models, perception and cognition depend on the optimal combination of noisy incoming evidence with prior knowledge of the world. Individual differences in perception should therefore be jointly determined by a person's sensitivity to incoming evidence and his or her prior expectations. It has been proposed that individuals with autism have flatter prior distributions than do nonautistic individuals, which suggests that prior variance is linked to the degree of autistic traits in the general population. We tested this idea by studying how perceived speed changes during pursuit eye movement and at low contrast. We found that individual differences in these two motion phenomena were predicted by differences in thresholds and autistic traits when combined in a quantitative Bayesian model. Our findings therefore support the flatter-prior hypothesis and suggest that individual differences in prior expectations are more systematic than previously thought. In order to be revealed, however, individual differences in sensitivity must also be taken into account.