Preregistration: Freeing capacity in WM through the use of LTM representations
Creators: name: Bartsch, Lea
name: Shepherdson, Peter
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Cognition and Perception
subject: Social and Behavioral Sciences
subject: Psychology
subject: FOS: Psychology

description: Background and Research Question In the first two experiments of this preregistered study (see OSF) we aimed to investigate whether the presence and use of LTM representations frees capacity for maintaining additional information in WM. In the first phase of our first experiment, we presented participants with word pairs for them to encode into LTM (LTM learning phase). Subsequently, they completed trials of a WM task, also involving word pairs. Crucially, the pairs presented in each WM trial consisted of varying numbers of new pairs (LTM unavailable) and the previously learned LTM pairs. The results of this first experiment provided evidence that cued recall performance in the WM task was unaffected when memory set size increased through the addition of LTM-available pairs but deteriorated when set size increased through adding LTM-unavailable (new) pairs. In the second Experiment we not only varied WM loads across two levels (2, and 4 pairs), but also varied LTM loads across three levels of LTM load (0, 2, and 4 pairs). Recall performance deteriorated with LTM load for low WM load (2 pairs) but remained superior to performance with the same set size of only LTM-unavailable (new) pairs. In contrast, LTM load did not affect recall performance at higher WM load (4 pairs). This added to the evidence of the first experiment suggesting that individuals can outsource workload to LTM to optimize performance, but also speaks towards a WM system with a flexible gate to LTM that can be opened or closed depending on the current cognitive need. So far, our experiments have focused on the effects of having a LTM representation available when having to maintain information over a short period of time. However, WM is typically described as a system for both storing and processing information on a short-term basis. Further, these two functions are often assumed to trade off, with memory load reducing processing efficiency and processing load reducing memory accuracy (e.g., Oberauer, 2009; Unsworth et al., 2009; Vergauwe et al., 2014). Thus, in Experiment 4, we aim to explore how the availability of LTM influences the ability to process recently encoded information. We therefore plan to adapt the task used in the first and second experiments to allow us to better investigate processing: The memoranda will consist of word-number pairs (e.g. pie = 12), which will be presented at varying set sizes to the participants sequentially. At test, participants will be presented with mathematical equations incorporating a word from that trial, and asked to determine whether the equations are correct (e.g.: pie + 11 = 23 ?). Critically, the pairs presented in each WM trial consist of varying numbers of new pairs (LTM unavailable) and again previously learned LTM pairs. In short, here we aim to extend the findings of Experiment 1 and 2, which hinted at a flexible gate in WM, opening to LTM when representations of memoranda are available. Specifically, we plan to investigate whether the LTM advantage found in those previous experiments persists in cases where the memoranda have to be processed upon recall, or whether the necessity for their retrieval into WM for processing abolishes this advantage. This will help us to understand the scope of the utility that LTM for specific contents might provide to WM functions that involve manipulating those contents, which could have implications for areas as diverse as reading, problem-solving, reasoning, and decision-making. Methods Participants We will first collect data from 30 participants who did not complete Experiment 1, 2 or 3, online (recruitment via Prolific academic). We will then run a first analysis and check the evidence (indexed by the Bayes Factor, BF) for differences between our conditions of interest. Our goal is to report BF ≥ 3 for or against interaction effects of LTM load with WMload, and all main effects (WMload, LTMload). If we do not reach the targeted BF after the initial data collection, we will add bouts of 10 participants and re-run the analyses. We will stop data collection once the BF ≥ 3, or once we have collected usable data from 100 participants. Participants will be replaced (i.e., their data considered unusable) if: (a) their overall response accuracy across all conditions is greater than 2 standard deviations below the overall mean; (b) their recall accuracy in the LTM test for the second recall instance of each pair is below 70% correct (at this point, they will have had two chances to encode and one testing instance to learn the pairs) (c) they do not complete all experimental conditions; or (d) if they do not comply with the instructions. In order to assess the latter, data from participants who spend less than 10 seconds reading the instructions will be excluded from the analysis. We will also exclude data from any trials with response times &lt; 200 ms, as participants are unlikely to be able to read an equation and identify the correct response in such a short duration. Only participants whose mother tongue is English, aged between 18-35 years, will be recruited for the experiment. Participants will have to indicate consent prior to the study and will be debriefed at the end. The experimental protocol is in accordance with the Institutional Review Board of the Psychology Institute from the University of Zurich. Materials and Procedure The study consists of 3 phases: an LTM learning phase in which subjects are presented with 16 number-word pairs (consisting of of concrete words, and numbers ranging from 1-64) for 5 seconds each, and a subsequent LTM test, in which their memory for each of the LTM pairs is tested twice in random order. In this LTM test, participants are cued with a word and have to type in the correct number. Feedback in the form of the correct answer is given immediately afterwards. This will be followed by the WM task phase in which either 2, 4, or 6 word-number-pairs will be presented sequentially, for 2.5 seconds each. The WM task in the present study will be an immediate memory test in which participants remember arbitrary word-number pairs (e.g., dog–56, tree–14) and are tested with a processing task, in which an participants are asked to verify equations where the left-hand side consists of a word presented during that trial, an operator (+ or -), and a novel number, and the right-hand side is a plausible result (e.g., tree + 7 = 21?). Participants have to determine whether or not the equation is correct, and make a binary response. The number of equations per trial equals the set size (i.e., each pair is tested once). The result is either correct, or differs from the correct value by |1|, |2|, or |3|. The WM trials either consist of new pairs only (LTM load 0, WM load 2 or 4), or comprise new pairs of their respective WM load plus the two LTM pairs (LTM load 2), adding up to the respective set sizes 2, 4, 6. Stimuli will consist of 64 concrete English nouns randomly associated with numbers from 1 – 64 (with associations uniquely randomised for each participant). Planned analyses WM task performance Bayesian generalized linear mixed models. The data of the WM task will be analysed using Bayesian generalized linear mixed models (BGLMM) implemented in the R package brms (Bürkner, 2017, 2018). The dependent variable will be the correctness of each response in each condition per participant. Correct responses are defined as rejecting false equations and accepting correct equations by the respective button presses. Therefore, we assume a Bernoulli data distribution predicted by a linear model through a logit link function (i.e., a repeated-measures logistic regression). The fixed effects are WM load (2 vs. 4), and LTM load (LTM load 0 vs. LTM load 2) as well as their interaction. Following the recommendation of Barr and colleagues (Barr, Levy, Scheepers, &amp; Tily, 2013; see also Schielzeth &amp; Forstmeier, 2009) we will implement the maximal random-effects structure justified by the design; by-participant random-intercept and by-participant random-slope for WM load and LTM load. In addition, we will estimate the correlation among the random-effects parameters. The regression coefficients will be Cauchy priors with a scale of 0.353. These scales were chosen because these priors were recently proposed as default priors for logistic models (Oberauer, 2019). We will use completely non-informative priors for the correlation matrices, so-called LKJ priors with shape parameter 1. We will calculate Bayes Factors to estimate the strength of evidence for the null or the alternative hypothesis. For instance, with the BF we can calculate the evidence for the effect of WM load (BF10) by comparing the evidence for a model including this factor against an intercept-only model that serves as the null model. Additionally, we can calculate evidence against a difference between the WM loads (BF01), where BF01 = (1/BF10). A BF10 larger than 1 gives evidence for an effect, a BF10 lower than 1 yields evidence against an effect and hence evidence for the null hypothesis. A BF10 of 10 indicates that the data are 10 times more likely under the alternative hypothesis than under the null hypothesis. Usually, BFs &gt; 3 are regarded as providing substantial evidence for one hypothesis over the other. We will use an MCMC algorithm (implemented in Stan; Carpenter et al., 2017) that estimates the posteriors by sampling parameter values proportional to the product of prior and likelihood. These samples are generated through 4 independent Markov chains, with 1000 warmup samples each, followed by 50000 samples drawn from the posterior distribution which are retained for analysis. Following Gelman and colleagues (2013), we will confirm that the 4 chains converge to the same posterior distribution by verifying that the rhat statistic – reflecting the ratio of between-chain variance to within-chain variance – is &lt; 1.05 for all parameters, and we will visually inspect the chains for convergence. Finally, we will use the bayes_factor function in the brms package, which implements the bridge sampler (Gronau, Singmann, &amp; Wagenmakers, 2017), for computing the BFs. As a next step of the analyses, we will focus on performance for new pairs only and investigate whether performance in remembering those new pairs varies between trials which included LTM available pairs and trials that did not. The fixed effects are again WM load (i.e., 0, 2 or 4) by condition LTMload (LTM load 0 vs. LTM load 2). This analysis will help us distinguish between possible explanations for a main effect of LTM load in the overall analysis. If a hypothetical performance advantage in the LTM load 2 trials in that analysis results from WM workload being outsourced to LTM, we would expect to find a similar effect here. On the other hand, if an advantage results from generally better memory for LTM pairs (vs. new pairs), no effect should be present in this analysis. Furthermore, because the equation evaluations provide binary choice response times, we will undertake exploratory analyses on these data using a hierarchical Wiener diffusion model, as implemented in brms. This is a four-parameter diffusion model variant which uses drift rate, boundary separation, bias, and non-decision time to fit choice response time data. In the context of WM tasks, drift rate has been associated with the quality of information entering the decision process, and tends to decrease as the size of a memory set increases (e.g., Shepherdson et al., 2018). Non-decision time, by contrast, increases with set size, and has been associated with the time taken to select an item into the focus of attention (e.g., Sewell et al., 2016; Shepherdson, 2020; Shepherdson et al., 2018). We are not aware of any existing research investigating how the quality and time taken to select information in WM relate to the use of LTM; thus, we will allow both the drift rate and non-decision parameters to vary across all levels of the design. By contrast, we will fix the bias and boundary separation parameters across the levels of the pair type tested (i.e., we assume these will be equivalent whether an LTM or a WM pair is involved in the equation), and across the correct and incorrect equations. We base our choice of the priors on prior knowledge of likely parameter values for the Wiener model, but otherwise try to specify them in a weakly informative manner. or the intercept of drift rate we will use a cauchy(0, 5) prior, with normal(0, 1) priors on effects. For the intercept of non-decision time we will use a normal(0.2, 0.1) prior, with normal(0, 0.1) priors on effects. For the intercept of boundary separation we will use a normal(1.5, 1) prior, with normal(0, 0.5) priors on effects. Finally, for the intercept of bias, we will use a normal(0.5, 0.2) distribution, with normal(0, 0.05) priors on effects. Predictions Hypotheses We will evaluate the effect of WM load (i.e., 2, or 4) by LTM load (i.e., 0 vs 2) on performance in the WM processing task (verifying math equations). We aim to assess the likelihood ratio (i.e., BF) of two hypotheses: H1 Increasing in LTM load does not affect WM capacity: processing task performance in the WM task does not decline with increases in set size if the increase in set size is operationalized through the addition of LTM available pairs than through unavailable (new) pairs. H2 LTM does not free WM capacity: processing task performance in the WM task deteriorates due to increases in set size, independent of whether the increase in set size is operationalized through the addition of LTM available pairs or through unavailable (new) pairs. References Barr, D. J., Levy, R., Scheepers, C., &amp; Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68(3), 255–278. https://doi.org/10.1016/j.jml.2012.11.001 Bürkner, P.-C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80(1), 1–28. Bürkner, P.-C. 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