Department of Neurology, Albert Einstein College of Medicine, United States; Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, United States.
Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, United States.
Neuroimage. 2021 Jan 15;225:117472. doi: 10.1016/j.neuroimage.2020.117472. Epub 2020 Oct 21.
Learning to anticipate future states of the world based on statistical regularities in the environment is a key component of perception and is vital for the survival of many organisms. Such statistical learning and prediction are crucial for acquiring language and music appreciation. Importantly, learned expectations can be implicitly derived from exposure to sensory input, without requiring explicit information regarding contingencies in the environment. Whereas many previous studies of statistical learning have demonstrated larger neuronal responses to unexpected versus expected stimuli, the neuronal bases of the expectations themselves remain poorly understood. Here we examined behavioral and neuronal signatures of learned expectancy via human scalp-recorded event-related brain potentials (ERPs). Participants were instructed to listen to a series of sounds and press a response button as quickly as possible upon hearing a target noise burst, which was either reliably or unreliably preceded by one of three pure tones in low-, mid-, and high-frequency ranges. Participants were not informed about the statistical contingencies between the preceding tone 'cues' and the target. Over the course of a stimulus block, participants responded more rapidly to reliably cued targets. This behavioral index of learned expectancy was paralleled by a negative ERP deflection, designated as a neuronal contingency response (CR), which occurred immediately prior to the onset of the target. The amplitude and latency of the CR were systematically modulated by the strength of the predictive relationship between the cue and the target. Re-averaging ERPs with respect to the latency of behavioral responses revealed no consistent relationship between the CR and the motor response, suggesting that the CR represents a neuronal signature of learned expectancy or anticipatory attention. Our results demonstrate that statistical regularities in an auditory input stream can be implicitly learned and exploited to influence behavior. Furthermore, we uncover a potential 'prediction signal' that reflects this fundamental learning process.
基于环境中的统计规律来预测未来的世界状态是感知的关键组成部分，对许多生物的生存至关重要。这种统计学习和预测对于语言和音乐欣赏的习得至关重要。重要的是，无需有关环境中偶然性的明确信息，就可以从暴露于感官输入中隐含地得出习得的期望。尽管先前有许多关于统计学习的研究表明，对意外刺激的神经元反应大于对预期刺激的反应，但期望本身的神经元基础仍知之甚少。在这里，我们通过人类头皮记录的事件相关脑电位（ERPs）检查了习得期望的行为和神经元特征。参与者被指示听一系列声音，并在听到目标噪声爆发时尽快按下响应按钮，该噪声爆发要么可靠地，要么不可靠地以前面三个纯音中的一个为先导，该纯音在低频、中频和高频范围内。参与者没有被告知前面的音调“提示”与目标之间的统计关系。在刺激块的过程中，参与者对可靠提示的目标的反应更快。这种习得期望的行为指标与负 ERP 偏移（称为神经元偶然反应（CR））相平行，该偏移在目标出现之前立即发生。CR 的幅度和潜伏期系统地受到提示与目标之间预测关系的强度调制。针对行为反应的潜伏期重新平均 ERP 显示，CR 与运动反应之间没有一致的关系，这表明 CR 代表习得期望或预期注意的神经元特征。我们的结果表明，可以隐含地学习并利用听觉输入流中的统计规律来影响行为。此外，我们发现了一个潜在的“预测信号”，反映了这一基本学习过程。
