Modulating prospective memory and attentional control with high-definition transcranial current stimulation: Study protocol of a randomized, double-blind, and sham-controlled trial in healthy older adults.

The ability to remember future intentions (i.e., prospective memory) is influenced by attentional control. At the neuronal level, frontal and parietal brain regions have been related to attentional control and prospective memory. It is debated, however, whether more or less activity in these regions is beneficial for older adults' performance. We will test that by systematically enhancing or inhibiting activity in these regions with anodal or cathodal high-definition transcranial direct current stimulation in older adults. We will include n = 105 healthy older volunteers (60-75 years of age) in a randomized, double-blind, sham-controlled, and parallel-group design. The participants will receive either cathodal, anodal, or sham high-definition transcranial direct current stimulation of the left or right inferior frontal gyrus, or the right superior parietal gyrus (1mA for 20 min). During and after stimulation, the participants will complete tasks of attentional control and prospective memory. The results of this study will clarify how frontal and parietal brain regions contribute to attentional control and prospective memory in older healthy adults. In addition, we will elucidate the relationship between attentional control and prospective memory in that age group. The study has been registered with ClinicalTrials.gov on the 12th of May 2021 (trial identifier: NCT04882527)

Evaluating the Robustness of Parameter Estimates in Cognitive Models: A Meta-Analytic Review of Multinomial Processing Tree Models Across the Multiverse of Estimation Methods

Researchers have become increasingly aware that data-analysis decisions affect results. Here, we examine this issue systematically for multinomial processing tree (MPT) models, a popular class of cognitive models for categorical data. Specifically, we examine the robustness of MPT model parameter estimates that arise from two important decisions: the level of data aggregation (complete pooling, no pooling, or partial pooling) and the statistical framework (frequentist or Bayesian). These decisions span a multiverse of estimation methods. We synthesized the data from 13,956 participants (164 published data sets) with a meta-analytic strategy and analyzed the magnitude of divergence between estimation methods for the parameters of nine popular multinomial processing tree (MPT) models in psychology (e.g., process dissociation, source monitoring). We further examined moderators as potential sources of divergence. We found that the absolute divergence between estimation methods was small on average (< .04; with MPT parameters ranging between 0 and 1); in some cases, however, divergence amounted to nearly the maximum possible range (.97). Divergence was partly explained by few moderators (e.g., the specific MPT model parameter, uncertainty in parameter estimation), but not by other plausible candidate moderators (e.g., parameter trade-offs, parameter correlations) or their interactions. Partial-pooling methods showed the smallest divergence within and across levels of pooling and thus seem to be an appropriate default method. Using MPT models as an example, we show how transparency and robustness can be increased in the field of cognitive modeling