Opposing timing constraints severely limit the use of pupillometry to investigate visual statistical learning

Felicia Zhang, Lauren L. Emberson

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number1792
JournalFrontiers in Psychology
Volume10
Issue numberJULY
DOIs
StatePublished - 2019

All Science Journal Classification (ASJC) codes

  • Psychology(all)

Keywords

  • Learning
  • Prediction
  • Pupil dilation
  • Pupillometry
  • Visual statistical learning

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