Adaptive behavior involves rapid error processing and action evaluation. However, it has not been clarified how errors contribute to automatic behaviors that can be retrieved to successfully adapt to our complex environment. Automatic behaviors strongly rely on the process of probabilistic sequence learning and memory. Therefore, the present study investigated error processing during the online retrieval of probabilistic sequence knowledge. Twenty-four healthy young adults acquired and continuously retrieved a repeating stimulus sequence reflected by reaction time (RT) changes on a rapid forced-choice RT task. Performance was compared with a baseline that denoted the processing of random stimuli embedded in the probabilistic sequence. At the neurophysiological level, event-related brain potentials synchronized to responses were measured. Error processing was tracked by the error negativity (Ne) and the error positivity (Pe). The mean amplitude of the Ne gradually decreased as the task progressed, similarly for the sequence retrieval and the embedded baseline process. The mean amplitude of the Pe increased over time, likewise, irrespective of the type of the stimuli. Accordingly, we propose that automatic error detection (Ne) and conscious error evaluation (Pe) are not sensitive to sequence learning and retrieval. Overall, the present study provides insight into how error processing takes place for the retrieval of sequence knowledge in a probabilistic environment.

中文翻译：

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在线检索概率序列知识过程中的错误处理

自适应行为涉及快速错误处理和动作评估。但是，尚未弄清错误如何导致自动行为发生，这些行为可以被检索以成功适应我们的复杂环境。自动行为强烈依赖于概率序列学习和记忆的过程。因此，本研究调查了概率序列知识在线检索过程中的错误处理。24名健康的年轻成年人获取并连续检索到重复的刺激序列，该序列由快速选择强迫性RT任务的反应时间（RT）变化反映出来。将性能与表示处理嵌入概率序列中的随机刺激的基线进行比较。在神经生理学水平上，测量与反应同步的事件相关脑电势。通过错误负性（Ne）和错误正性（Pe）跟踪错误处理。随着任务的进行，Ne的平均振幅逐渐减小，这与序列检索和嵌入式基线过程类似。同样，无论刺激的类型如何，Pe的平均幅度都随时间增加。因此，我们提出自动错误检测（Ne）和有意识错误评估（Pe）对序列学习和检索不敏感。总的来说，本研究提供了关于在概率环境中如何进行错误处理以检索序列知识的见解。类似于序列检索和嵌入式基线过程。同样，无论刺激的类型如何，Pe的平均幅度都随时间增加。因此，我们提出自动错误检测（Ne）和有意识错误评估（Pe）对序列学习和检索不敏感。总的来说，本研究提供了关于在概率环境中如何进行错误处理以检索序列知识的见解。类似于序列检索和嵌入式基线过程。同样，无论刺激的类型如何，Pe的平均幅度都随时间增加。因此，我们提出自动错误检测（Ne）和有意识错误评估（Pe）对序列学习和检索不敏感。总的来说，本研究提供了关于在概率环境中如何进行错误处理以检索序列知识的见解。