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Behrendt, M. B., Cheng, L. P., Halvorsen, A. T., Kuntzelman, K., & Johnson, M. R (April 2020). EEG reinvestigations of visual statistical learning for faces, scenes, and objects. Poster presentation for Annual UCARE Research Program, University of Nebraska – Lincoln.


Copyright 2020 by the authors.


The objective of this ongoing, replication study is to understand temporal and spatial patterns in our environment by using the technique of electroencephalography (EEG). Visual statistical learning (VSL) helps us to understand conditional probabilities from our environments. This concept is why we know that chairs are located under tables, not above. The goal of this study is to understand whether participants can unconsciously associate pairs of items (faces, scenes, and objects) from their short-term memory. Strong pairs become more similar to each other, as compared to weak pairs, which become less similar. In the main task, participants saw items appear on the screen, on at a time, for 100ms each. Items directly followed each other without transitions. In the post-task, participants were asked to rate how familiar pairs of items were, using a sliding scale. There were three types of pairs presented: strong pairs where item B followed item A 100% of the time; weak pairs where item B followed item A 11% of the time; and foil pairs where item B followed item A 0% of the time. In conclusion, results are similar to the current study (n = 10) in that there are behavioral differences between strong vs. foil and strong vs. weak pairs.

behrendt_2020.ppt (1881 kB)

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