Working memory affects anticipatory behavior during implicit pattern learning

Abstract

We investigated the relation between implicit sequence learning and individual differences in working memory (WM) capacity. Participants performed an oculomotor version of the serial reaction time (SRT) task and three computerized WM tasks. Implicit learning was measured using anticipation measures only, as they represent strong indicators of learning. Our results demonstrate that anticipatory behavior in the SRT task changes as a function of WM capacity, such that it increases with decreased WM capacity. On the other hand, WM capacity did not affect the overall number of correct anticipations in the task. In addition, we report a positive relation between WM capacity and the number of consecutive correct anticipations (or chunks), and a negative relation between WM capacity and the overall number of errors, indicating different learning strategies during implicit sequence learning. The results of the current study are theoretically important, because they demonstrate that individual differences in WM capacity could account for differences in learning processes, and ultimately change individuals’ anticipatory behavior, even when learning is implicit, without intention and awareness.

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Availability of data

The data set supporting the conclusions of this article is available as an electronic supplementary material. The data set is also available at https://doi.org/10.25500/00000379. The R code necessary to reproduce our models is available at https://github.com/ooominds/Working-memory-affects-anticipatory-behavior.

Notes

  1. 1.

    More complex probabilistic sequences could likewise increase task reliability (Stark-Inbar, Raza, Taylor, & Ivry, 2016; West, Vadillo, Shanks, & Hulme, 2018).

  2. 2.

    Note that including a set of additional predictors (i.e., age, gender, and education) did not improve the models (for more details and an additional control model containing a measure of executive function using a subset of participants, see Supplementary Material, Tables S3 and S4).

  3. 3.

    Because our sample size was relatively small and the size of the effects of interest could not be inferred from previous research, we addressed the potential power and parametrization bias issues by conducting a corresponding set of analyses using Bayesian estimation. Note that the estimates from the frequentist and Bayesian models were largely comparable. Where there are differences in statistical significance of the estimates, we signal them in text. Further details about Bayesian analyses are provided in the Supplementary Material. For the full model frequentist and Bayesian estimate comparisons, see Tables S5–S11.

  4. 4.

    In the no-interaction model, there was a marginally significant effect of Block, χ2 (1) = 3.80, p = 0.051, such that correct anticipations increased in Block 6. Using Bayesian approach, the effect of Block was statistically significant (as indicated by the 95% credible interval), estimate = 0.23, credible interval  = [0.001, 0.45].

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Acknowledgements

We would like to thank Maciej Borowski and Dagmar Hanzlikova for their help with the data collection, and Adnane Ez-Zizi for statistical consultation.

Funding

This research was supported by a Leverhulme Trust Research Leadership Award (RL-2016-001) to D. Divjak, which funded all authors.

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Correspondence to Srdan Medimorec.

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Medimorec, S., Milin, P. & Divjak, D. Working memory affects anticipatory behavior during implicit pattern learning. Psychological Research 85, 291–301 (2021). https://doi.org/10.1007/s00426-019-01251-w

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