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Testing statistical learning implicitly: A novel chunk-based measure of statistical learning

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Isbilen_etal_2017.pdf
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Citation

Isbilen, E. S., McCauley, S. M., Kidd, E., & Christiansen, M. H. (2017). Testing statistical learning implicitly: A novel chunk-based measure of statistical learning. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (CogSci 2017) (pp. 564-569). Austin, TX: Cognitive Science Society.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002E-3261-B
Abstract
Attempts to connect individual differences in statistical learning with broader aspects of cognition have received considerable attention, but have yielded mixed results. A possible explanation is that statistical learning is typically tested using the two-alternative forced choice (2AFC) task. As a meta-cognitive task relying on explicit familiarity judgments, 2AFC may not accurately capture implicitly formed statistical computations. In this paper, we adapt the classic serial-recall memory paradigm to implicitly test statistical learning in a statistically-induced chunking recall (SICR) task. We hypothesized that artificial language exposure would lead subjects to chunk recurring statistical patterns, facilitating recall of words from the input. Experiment 1 demonstrates that SICR offers more fine-grained insights into individual differences in statistical learning than 2AFC. Experiment 2 shows that SICR has higher test-retest reliability than that reported for 2AFC. Thus, SICR offers a more sensitive measure of individual differences, suggesting that basic chunking abilities may explain statistical learning.