Moutoussis Michael, Dolan Raymond J, Dayan Peter
Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom.
Max Planck-UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom.
PLoS Comput Biol. 2016 Jul 22;12(7):e1004965. doi: 10.1371/journal.pcbi.1004965. eCollection 2016 Jul.
The weight with which a specific outcome feature contributes to preference quantifies a person's 'taste' for that feature. However, far from being fixed personality characteristics, tastes are plastic. They tend to align, for example, with those of others even if such conformity is not rewarded. We hypothesised that people can be uncertain about their tastes. Personal tastes are therefore uncertain beliefs. People can thus learn about them by considering evidence, such as the preferences of relevant others, and then performing Bayesian updating. If a person's choice variability reflects uncertainty, as in random-preference models, then a signature of Bayesian updating is that the degree of taste change should correlate with that person's choice variability. Temporal discounting coefficients are an important example of taste-for patience. These coefficients quantify impulsivity, have good psychometric properties and can change upon observing others' choices. We examined discounting preferences in a novel, large community study of 14-24 year olds. We assessed discounting behaviour, including decision variability, before and after participants observed another person's choices. We found good evidence for taste uncertainty and for Bayesian taste updating. First, participants displayed decision variability which was better accounted for by a random-taste than by a response-noise model. Second, apparent taste shifts were well described by a Bayesian model taking into account taste uncertainty and the relevance of social information. Our findings have important neuroscientific, clinical and developmental significance.
特定结果特征对偏好的影响权重量化了一个人对该特征的“喜好”。然而,喜好远非固定的人格特征,而是具有可塑性。例如,即使这种从众行为没有得到回报,它们也往往会与他人的喜好保持一致。我们假设人们可能对自己的喜好不确定。因此,个人喜好是不确定的信念。人们可以通过考虑相关他人的偏好等证据,然后进行贝叶斯更新来了解自己的喜好。如果一个人的选择变异性反映了不确定性,就像在随机偏好模型中那样,那么贝叶斯更新的一个特征是,喜好变化的程度应该与这个人的选择变异性相关。时间折扣系数是耐心偏好的一个重要例子。这些系数量化了冲动性,具有良好的心理测量特性,并且可以在观察他人的选择后发生变化。我们在一项针对14至24岁人群的新颖的大型社区研究中考察了折扣偏好。我们在参与者观察另一个人的选择之前和之后评估了他们的折扣行为,包括决策变异性。我们发现了喜好不确定性和贝叶斯喜好更新的有力证据。首先,参与者表现出的决策变异性,用随机喜好模型比用反应噪声模型能更好地解释。其次,考虑到喜好不确定性和社会信息的相关性,贝叶斯模型很好地描述了明显的喜好转变。我们的发现具有重要的神经科学意义、临床意义和发展意义。