Learning about the statistics of one’s environment is a fundamental requirement of adaptive behaviour. In this experiment we probe whether pupil dilation in response to brief auditory stimuli reflects automatic statistical learning about the underlying stimulus distributions. Specifically, we consider whether pupil dilation reflects automatic (task-irrelevant) learning about the precision of Gaussian distributions of pitch in a sequence of tones. We provide clear evidence, both by comparing responses to perceptually identical probe tones in low and high precision blocks, and using a novel model-based analysis, that subjects did indeed track the precision of the stimulus distribution. This extends previous work looking at electrophysiological effects of precision (or, equivalently, variance) learning, and provides new evidence that the putatively noradrenergic processes underlying pupil dilation reflect rapidly updated information about distributions of sensory stimuli. In addition, our study represents a validation of our model-based approach to analysing pupillometry data, which we believe has considerable promise for future studies.

了解一个人的环境统计信息是适应行为的基本要求。在这个实验中，我们探究瞳孔扩张对短暂听觉刺激的反应是否反映了有关潜在刺激分布的自动统计学习。具体来说，我们考虑瞳孔膨胀是否反映了有关音调序列中高斯音高分布精度的自动（与任务无关）学习。我们提供了明确的证据，既可以通过比较对低和高精度区块中感知相同的探测音的响应，也可以使用基于模型的新颖分析，来证明受试者确实跟踪了刺激分布的准确性。这扩展了以前的工作，研究了精确（或等效地，方差）学习的电生理效应，并提供了新的证据，证明瞳孔扩张背后的假定的去甲肾上腺素能过程反映了有关感觉刺激分布的快速更新信息。此外，我们的研究代表了对基于模型的瞳孔测量数据分析方法的验证，我们认为这对未来的研究具有很大的希望。