Institute of Psychology, Otto-von-Guericke University, D-39106 Magdeburg, Germany.
Department of Education and Psychology, Freie Universität Berlin, D-14195 Berlin, Germany.
Neuroimage. 2022 Oct 1;259:119437. doi: 10.1016/j.neuroimage.2022.119437. Epub 2022 Jul 3.
Optimal decision making in complex environments requires dynamic learning from unexpected events. To speed up learning, we should heavily weight information that indicates state-action-outcome contingency changes and ignore uninformative fluctuations in the environment. Often, however, unrelated information is hard to ignore and can potentially bias our learning. Here we used computational modelling and EEG to investigate learning behaviour in a modified probabilistic choice task that introduced two task-irrelevant factors that were uninformative for optimal task performance, but nevertheless could potentially bias learning: pay-out magnitudes were varied randomly and, occasionally, feedback presentation was enhanced by visual surprise. We found that participants' overall good learning performance was biased by distinct effects of these non-normative factors. On the neural level, these parameters are represented in a dynamic and spatiotemporally dissociable sequence of EEG activity. Later in feedback processing the different streams converged on a central to centroparietal positivity reflecting a signal that is interpreted by downstream learning processes that adjust future behaviour.
在复杂环境中进行最优决策需要根据意外事件进行动态学习。为了加快学习速度，我们应该高度重视表明状态-动作-结果关联变化的信息，而忽略环境中无信息的波动。然而，通常情况下，无关信息很难被忽略，并且可能会对我们的学习产生偏差。在这里，我们使用计算建模和 EEG 研究了在修改后的概率选择任务中学习行为，该任务引入了两个与任务无关的因素，这些因素对最佳任务表现没有信息作用，但仍然可能会对学习产生偏差：收益幅度随机变化，并且偶尔会通过视觉惊喜增强反馈呈现。我们发现，参与者整体良好的学习表现受到这些非规范因素的不同影响的影响。在神经水平上，这些参数以 EEG 活动的动态和时空可分离序列表示。在反馈处理的后期，不同的流汇聚在一个中央到中央顶正性上，反映了一个信号，该信号由下游学习过程解释，从而调整未来的行为。
