Department of Experimental Psychology, University of Oxford, Oxford OX2 6GG, UK.
Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin 14195, Germany.
Cereb Cortex. 2020 Jun 30;30(8):4454-4464. doi: 10.1093/cercor/bhaa039.
Decisions are typically made after integrating information about multiple attributes of alternatives in a choice set. Where observers are obliged to consider attributes in turn, a computational framework known as "selective integration" can capture salient biases in human choices. The model proposes that successive attributes compete for processing resources and integration is biased towards the alternative with the locally preferred attribute. Quantitative analysis shows that this model, although it discards choice-relevant information, is optimal when the observers' decisions are corrupted by noise that occurs beyond the sensory stage. Here, we used electroencephalography (EEG) to test a neural prediction of the model: that locally preferred attributes should be encoded with higher gain in neural signals over the posterior cortex. Over two sessions, human observers judged which of the two simultaneous streams of bars had the higher (or lower) average height. The selective integration model fits the data better than a rival model without bias. Single-trial analysis showed that neural signals contralateral to the preferred attribute covaried more steeply with the decision information conferred by locally preferred attributes. These findings provide neural evidence in support of selective integration, complementing existing behavioral work.
决策通常是在综合考虑选择集中多个备选方案属性的信息后做出的。当观察者被迫依次考虑属性时，一种称为“选择性整合”的计算框架可以捕捉到人类选择中的显著偏差。该模型提出，连续的属性相互竞争处理资源，并且整合偏向于具有局部首选属性的备选方案。定量分析表明，尽管该模型丢弃了与选择相关的信息，但当观察者的决策受到超出感觉阶段的噪声干扰时，该模型是最优的。在这里，我们使用脑电图 (EEG) 来测试该模型的一个神经预测：即局部首选属性在后部皮层的神经信号中应具有更高的增益进行编码。在两个会话中，人类观察者判断两个同时出现的条形图流中哪一个的平均高度更高（或更低）。选择性整合模型比没有偏差的竞争模型更能拟合数据。单次试验分析表明，与首选属性相对侧的神经信号与局部首选属性赋予的决策信息的变化更陡峭。这些发现为选择性整合提供了神经证据，补充了现有的行为研究。
