Opposing Timing Constraints Severely Limit the Use of Pupillometry to Investigate Visual Statistical Learning.

Felicia Zhang,Lauren L Emberson

doi:10.3389/fpsyg.2019.01792

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.

Highlights

Statistical learning (SL) is the ability to exploit patterns in the environment after passive exposure
In Experiment 1, we found that pupil change during visual statistical learning (VSL) familiarization phase does not predict accuracy during VSL test phase
We found that pupil constriction might be related to prediction error

Summary

Introduction

Statistical learning (SL) is the ability to exploit patterns in the environment after passive exposure. The images are organized based on statistical information (e.g., transitional probability, co-occurrence frequency) such that some of the images are more likely to appear one Pupillometry and Visual Statistical Learning after another compared to others. A stream of images can be composed of triplets, such that the same three images always appeared in the same sequence (ABC-DEF-ABCGHI) and statistical information is the only cue that learners have to uncover the structure or organization of the visual stream. Following common nomenclature in the field, we will refer to these measures as offline measures because they are collected after learning (i.e., after the familiarization phase or during the test phase) has occurred. We will refer to measures collected during learning (i.e., during the familiarization phase) as online measures

Methods

Results

Conclusion