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Psychological researchers often identify with psychological disciplines, such as social or clinical psychology. The current study analyzed Google Scholar profiles from 6,532 international scientists who attracted more than 100 citations in 2019 and self-identified with at least one of 10 common psychological disciplines (psychoanalysis; clinical psychology; (cognitive) neuroscience; developmental psychology; educational psychology; experimental psychology; biological psychology/psychophysiology; mathematical psychology/psychometrics; social psychology; personality psychology). Results indicated that almost half of all psychologists self-identified with either social psychology or cognitive neuroscience. There were 487 topics that were endorsed at least five times, ranging from highly discipline-specific topics to more integrative ones, such as emotion and personality. We also factor-analyzed frequencies of topical endorsement across disciplines and found two factors, which we interpreted as reflecting correlational and experimental research traditions (with social psychology being the largest discipline within the former tradition and cognitive neuroscience being the largest discipline within the latter tradition). Differences in productivity and impact were also found, with researchers identifying with psychometrics being the most productive and researchers identifying with personality psychology, cognitive neuroscience, and multidisciplinary psychology as the most impactful in terms of citation increases per additional output. Recommendations for promoting cross-fertilization across psychological disciplines are formulated. [ABSTRACT FROM AUTHOR]

In cognitive neuroscience and psychology, reaction times are an important behavioral measure. However, in instrumental learning and goal-directed decision making experiments, findings often rely only on choice probabilities from a value-based model, instead of reaction times. Recent advancements have shown that it is possible to connect value-based decision models with reaction time models. However, typically these models do not provide an integrated account of both value-based choices and reaction times, but simply link two types of models. Here, we propose a novel integrative joint model of both choices and reaction times by combining a computational account of Bayesian sequential decision making with a sampling procedure. This allows us to describe how internal uncertainty in the planning process shapes reaction time distributions. Specifically, we use a recent context-specific Bayesian forward planning model which we extend by a Markov chain Monte Carlo (MCMC) sampler to obtain both choices and reaction times. As we will show this makes the sampler an integral part of the decision making process and enables us to reproduce, using simulations, well-known experimental findings in value based-decision making as well as classical inhibition and switching tasks. Specifically, we use the proposed model to explain both choice behavior and reaction times in instrumental learning and automatized behavior, in the Eriksen flanker task and in task switching. These findings show that the proposed joint behavioral model may describe common underlying processes in these different decision making paradigms. Author summary: Many influential results in psychology and cognitive neuroscience rest on reaction time effects in behavioral experiments, for example in studies about human decision making. For the particular case of decisions based on the value of options, however, findings often rely on analyses of choices using specific computational models. Until recently, these models did not allow for analysis of reaction times. In this article we introduce a new model of how to explain both choices and reaction times in decision making experiments that involve evaluating expected outcomes over multiple steps. Importantly, the model explains how the brain can make good decisions quickly, even in the face of many potential choices and in complex environments. [ABSTRACT FROM AUTHOR]