Sans Forgetica - Testing

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Description: Presenting information in a perceptually disfluent format sometimes enhances memory. Recent work examining one type of perceptual disfluency manipulation, Sans Forgetica typeface, have yielded discrepant findings; some research finds support for the idea that the novel, disfluent typeface improves memory while other research does not. To explore this discrepancy, the current study examined a boundary condition that determines when disfluency is and is not beneficial to learning. Specifically, we examined whether knowledge about an upcoming test (high test expectancy) versus not (low test expectancy) helps clarify when mnemonic benefits arise and when they do not for disfluent stimuli. In Experiment 1 (preregistered, N = 231), we found that Sans Forgetica is a desirable difficulty, but only under the right circumstances. That is, Sans Forgetica was in fact perceptually disfluent as evinced by lower JOLs and increased study times. However, we observed improved memory on an old/new recognition test only when there was no expectation of a final test. When participants expected a test, the effect disappeared. In Experiment 2 (preregistered N = 232), we conceptually replicated the results of Experiment 1 using a cued recall test. In Experiment 3 (preregistered, N = 232), we ruled out an explanation based on time-on-task and replicated the results from Experiment 2. While we did observe a positive effect of disfluency on memory, caution should be taken in interpreting these results. Not only were the effect sizes small, but low testing expectancy may not be realistically achievable in actual educational contexts. While more research is needed, we echo our prior arguments that students wanting to remember more and forget less should stick to other, more empirically supported desirable difficulties shown to enhance memory.

Wiki

This wiki contains R code, raw data, and summary data, for the experiments contained in "Is this going to be on the test? Testing Expectancy Moderates the Disfluency Effect." The wiki is divided into the following sections: Github Code: R code, raw data, summary data for Experiments 1, 2, and 3 expt_recog_data: analysis scripts, raw data, and summary data for Experiment 1 in manuscript. expt2...

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