Search for: All records

Abstract Research shows that skills for improving Psychological Well‐Being (PWB) may belearnedthrough PWB interventions; however, the dynamic mechanisms underlying this learning process are not well understood. Using an Ecological Momentary Intervention (EMI) design, we conducted an 8‐week Randomized Controlled Trial (N = 160; aged 18–22 years), implemented in a mobile Health (mHealth) platform to characterize these dynamical mechanisms. College‐attending early adults were randomized to three groups: an active control group (N = 55); an intervention group (N = 51) with positive practices intervention; and a second intervention group (N = 54) with positive practices and meditation intervention. The mHealth implementation allowed us to introduce the interventions in participants' daily lives while also assessing their PWB (in terms of positive emotions and relationship quality) several times a day. We used a Bayesian process model to analyze changes in PWB in terms of the underlying dynamical characteristics of change. Findings suggested that the mobile assessment tool itself may have longitudinally improved college‐attending early adults' PWB, as evidenced by instances of directional changes in dynamic characteristics (increased within‐person mean levels, decreased intra‐individual variability, and increased regulation) of PWB measures. Moderation analysis also revealed that people who were low on negative affect improved the most in terms of their mean levels of positive emotions and relationship quality. 

Testing for Granger causality relies on estimating the capacity of dynamics in one time series to forecast dynamics in another. The canonical test for such temporal predictive causality is based on fitting multivariate time series models and is cast in the classical null hypothesis testing framework. In this framework, we are limited to rejecting the null hypothesis or failing to reject the null -- we can never validly accept the null hypothesis of no Granger causality. This is poorly suited for many common purposes, including evidence integration, feature selection, and other cases where it is useful to express evidence against, rather than for, the existence of an association. Here we derive and implement the Bayes factor for Granger causality in a multilevel modeling framework. This Bayes factor summarizes information in the data in terms of a continuously scaled evidence ratio between the presence of Granger causality and its absence. We also introduce this procedure for the multilevel generalization of Granger causality testing. This facilitates inference when information is scarce or noisy or if we are interested primarily in population-level trends. We illustrate our approach with an application on exploring causal relationships in affect using a daily life study. 

Cyber resilience is the ability of a system to resist and recover from a cyber attack, thereby restoring the system’s functionality. Effective design and development of a cyber resilient system requires experimental methods and tools for quantitative measuring of cyber resilience. This article describes an experimental method and test bed for obtaining resilience-relevant data as a system (in our case—a truck) traverses its route, in repeatable, systematic experiments. We model a truck equipped with an autonomous cyber-defense system and which also includes inherent physical resilience features. When attacked by malware, this ensemble of cyber-physical features (i.e., “bonware”) strives to resist and recover from the performance degradation caused by the malware’s attack. We propose parsimonious mathematical models to aid in quantifying systems’ resilience to cyber attacks. Using the models, we identify quantitative characteristics obtainable from experimental data and show that these characteristics can serve as useful quantitative measures of cyber resilience. 

I provide a personal perspective on metastudies and emphasize lesser-known benefits. I stress the need for integrative theories to establish commensurability between experiments. I argue that mathematical social scientists should be engaged to develop integrative theories, and that likelihood functions provide a common mathematical framework across experiments. The development of quantitative theories promotes commensurability engineering on a larger scale. 

The Bayesian HDI+ROPE decision rule is an increasingly common approach to testing null parameter values. The decision procedure involves a comparison between a posterior highest density interval (HDI) and a pre-specified region of practical equivalence (ROPE). One then accepts or rejects the null parameter value depending on the overlap (or lack thereof) between these intervals. Here we demonstrate, both theoretically and through examples, that this procedure is logically incoherent. Because the HDI is not transformation invariant, the ultimate inferential decision depends on statistically arbitrary and scientifically irrelevant properties of the statistical model. The incoherence arises from a common confusion between probability density and probability proper. The HDI+ROPE procedure relies on characterizing posterior densities as opposed to being based directly on probability. We conclude with recommendations for alternative Bayesian testing procedures that do not exhibit this pathology and provide a "quick fix" in the form of quantile intervals. 

As the field of computational cognitive neuroscience continues to expand and generate new theories, there is a growing need for more advanced methods to test the hypothesis of brain-behavior relationships. Recent progress in Bayesian cognitive modeling has enabled the combination of neural and behavioral models into a single unifying framework. However, these approaches require manual feature extraction, and lack the capability to discover previously unknown neural features in more complex data. Consequently, this would hinder the expressiveness of the models. To address these challenges, we propose a Neurocognitive Variational Autoencoder (NCVA) to conjoin high-dimensional EEG with a cognitive model in both generative and predictive modeling analyses. Importantly, our NCVA enables both the prediction of EEG signals given behavioral data and the estimation of cognitive model parameters from EEG signals. This novel approach can allow for a more comprehensive understanding of the triplet relationship between behavior, brain activity, and cognitive processes. 

Abstract We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document. 

Evidence-accumulation models (EAMs) are powerful tools for making sense of human and animal decision-making behavior. EAMs have generated significant theoretical advances in psychology, behavioral economics, and cognitive neuroscience and are increasingly used as a measurement tool in clinical research and other applied settings. Obtaining valid and reliable inferences from EAMs depends on knowing how to establish a close match between model assumptions and features of the task/data to which the model is applied. However, this knowledge is rarely articulated in the EAM literature, leaving beginners to rely on the private advice of mentors and colleagues and inefficient trial-and-error learning. In this article, we provide practical guidance for designing tasks appropriate for EAMs, relating experimental manipulations to EAM parameters, planning appropriate sample sizes, and preparing data and conducting an EAM analysis. Our advice is based on prior methodological studies and the our substantial collective experience with EAMs. By encouraging good task-design practices and warning of potential pitfalls, we hope to improve the quality and trustworthiness of future EAM research and applications.