The motoric fluency effect on metamemory
Publication date: August 2017 Source:Journal of Memory and Language, Volume 95 Author(s): Jonathan A. Susser, Jennifer Panitz, Zachary Buchin, Neil W. Mulligan Predictions of future memory are often influenced by the ease or fluency of processing information. Susser and Mulligan (2015) recently demonstrated that motoric fluency (of writing with the dominant or non-dominant hand) may likewise affect these predictions. In the present study, we report five experiments that specify the locus of this motoric fluency effect. In Experiment 1, we examined whether the effect was driven by differences in effective study time across hand conditions. In Experiment 2, we assessed whether the effect could be obtained without any visual feedback from handwriting. In Experiments 3a and 3b, we investigated the contribution of visual feedback alone. In Experiment 4, we used prestudy JOLs to determine whether participants may develop a belief about handedness in the context of the experiment. Taken together, the results indicate that the motoric act of producing information in a fluent or disfluent manner is sufficient to produce an effect on memory predictions, that visual information from writing does not contribute, and that on-line interaction with the task plays a role. The experience of motoric fluency appears to be another cue that affects metamemory.
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This article is protected by copyright. All rights reserved. The phosphodiesterase VieA regulates gene expression by modifying the intracellular cyclic diguanylate pool. This article reveals the differential regulation of VieA in Vibrio cholerae O1 biotypes. Expression of VieA is repressed by the nucleoid‐associated protein H‐NS and the LysR‐type regulator LeuO in classical biotype V. cholerae, and by H‐NS and the quorum sensing regulator HapR in the El Tor biotype.
We describe approaches for back‐calculating model parameter estimates and their standard errors from available summary statistics with various techniques, including approximate Bayesian computation. We propose to use a quadratic approximation to the log‐likelihood for each historical trial based on 2 independent terms for the log mean rate and the log of the dispersion parameter. A Bayesian hierarchical meta‐analysis model then provides the posterior predictive distribution for these parameters. Simulations show this approach with back‐calculated parameter estimates results in very similar inference as using parame...