Zhang Felicia, Emberson Lauren L
Department of Psychology, Princeton University, Princeton, NJ, United States.
Front Psychol. 2019 Aug 6;10:1792. doi: 10.3389/fpsyg.2019.01792. eCollection 2019.
Majority of visual statistical learning (VSL) research uses only offline measures, collected after the familiarization phase (i.e., learning) has occurred. Offline measures have revealed a lot about the extent of statistical learning (SL) but less is known about the learning mechanisms that support VSL. Studies have shown that prediction can be a potential learning mechanism for VSL, but it is difficult to examine the role of prediction in VSL using offline measures alone. Pupil diameter is a promising online measure to index prediction in VSL because it can be collected during learning, requires no overt action or task and can be used in a wide-range of populations (e.g., infants and adults). Furthermore, pupil diameter has already been used to investigate processes that are part of prediction such as prediction error and updating. While the properties of pupil diameter have the potentially to powerfully expand studies in VSL, through a series of three experiments, we find that the two are not compatible with each other. Our results revealed that pupil diameter, used to index prediction, is not related to offline measures of learning. We also found that pupil differences that appear to be a result of prediction, are actually a result of where we chose to baseline instead. Ultimately, we conclude that the fast-paced nature of VSL paradigms make it incompatible with the slow nature of pupil change. Therefore, our findings suggest pupillometry should not be used to investigate learning mechanisms in fast-paced VSL tasks.
大多数视觉统计学习（VSL）研究仅使用离线测量方法，这些方法是在熟悉阶段（即学习阶段）结束后收集的。离线测量方法揭示了大量关于统计学习（SL）程度的信息，但对于支持VSL的学习机制却知之甚少。研究表明，预测可能是VSL的一种潜在学习机制，但仅使用离线测量方法很难检验预测在VSL中的作用。瞳孔直径是一种很有前景的在线测量指标，可用于衡量VSL中的预测，因为它可以在学习过程中收集，无需明显的动作或任务，并且可用于广泛的人群（例如婴儿和成年人）。此外，瞳孔直径已被用于研究预测过程的一部分，如预测误差和更新。虽然瞳孔直径的特性有可能有力地扩展VSL研究，但通过一系列三个实验，我们发现两者并不兼容。我们的结果表明，用于衡量预测的瞳孔直径与学习的离线测量方法无关。我们还发现，看似由预测导致的瞳孔差异，实际上是我们选择作为基线的位置所导致的结果。最终，我们得出结论，VSL范式的快节奏性质使其与瞳孔变化的缓慢性质不兼容。因此，我们的研究结果表明，不应使用瞳孔测量法来研究快节奏VSL任务中的学习机制。
