A tutorial on fitting joint models of M/EEG and behavior to understand cognition.

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.

The "'Crisis' Crisis" in psychology.

The recent trend to label dilemmas in psychology as "crises" is insidious. The "'Crisis' Crisis" in psychology can distract us from actionable practices. As a case in point, "The Generalizability Crisis" offers the valuable central thesis that verbal-quantitative gaps imperil psychological science. Focusing on the key issues rather than crisis narratives can lead to progress in our discourse and research.

Context-Dependent Risk & Benefit Sensitivity Mediate Judgments About Cognitive Enhancement.

Opinions about cognitive enhancement (CE) are context-dependent. Prior research has demonstrated that factors like peer pressure, the influence of authority figures, competition, moral relevance, familiarity with enhancement devices, expertise, and the domain of CE to be enhanced can influence opinions. The variability and malleability of patient, expert, and public attitudes toward CE is important to describe and predict because these attitudes can influence at-home, clinical, research, and regulatory decisions. If individual preferences vary, they could influence opinions about practices and regulations due to disagreements about the desirable levels of risks and benefits. The study of attitudes about CE would benefit from psychological theories that explain judgments. In particular, we suggest that variability in risk and benefit sensitivity could psychologically mediate judgments about CE in many contexts. Drawing from prospect theory, which originated in behavioral economics, it is likely that framing effects, shifted reference points, and the tendency to weigh losses (risks) more heavily than gains (benefits) predict decisions about CE. We suggest that public policy could benefit from a shared conceptual framework, such as prospect theory, that allows us to describe and predict real-world decisions about CE by patients, experts, and the public.