Search

Many psychotherapies aim to help people replace maladaptive mental behaviors (such as those leading to unproductive worry) with more adaptive ones (such as those leading to active problem solving). Yet, little is known empirically about how challenging it is to learn adaptive mental behaviors. Mental behaviors entail taking mental operations and thus may be more challenging to perform than motor actions; this challenge may enhance or impair learning. In particular, challenge when learning is often desirable because it improves retention. Yet, it is also plausible that the necessity of carrying out mental operations interferes with learning the expected values of mental actions by impeding credit assignment: the process of updating an action's value after reinforcement. Then, it may be more challenging not only to perform—but also to learn the consequences of—mental (vs. motor) behaviors. We designed a task to assess learning to take adaptive mental versus motor actions via matched probabilistic feedback. In two experiments (N = 300), most participants found it more difficult to learn to select optimal mental (vs. motor) actions, as evident in worse accuracy not only in a learning but also test (retention) phase. Computational modeling traced this impairment to an indicator of worse credit assignment (impaired construction and maintenance of expected values) when learning mental actions, accounting for worse accuracy in the learning and retention phases. The results suggest that people have particular difficulty learning adaptive mental behavior and pave the way for novel interventions to scaffold credit assignment and promote adaptive thinking. General Scientific Summary: We are often asked to think harder, remain positive, and not sweat the small stuff. But why is it so challenging to ingrain adaptive mental behaviors, such as those that lead to thinking in healthy and productive ways? Using a novel task that directly compared the ability to learn optimal mental versus motor behaviors via trial and error, we found that (overall) people had more difficulty learning optimal mental behaviors and traced this difficulty to the formation of less robust expected values for mental actions. [ABSTRACT FROM AUTHOR]

This article summarizes the outcomes of the second national conference on the Future of Bioscience Graduate and Postdoctoral Training. Five topics were addressed during the conference: diversity in leadership positions; mentoring; modernizing the curriculum; experiential learning; and the need for better data on trainees. The goal of the conference was to develop a consensus around these five topics and to recommend policies that can be implemented by academic and research institutions and federal funding agencies in the United States.

Computational psychiatry applies advances fromcomputational neuroscience to psychiatric disorders. A coreaim is to develop tasks and modeling approaches that canadvance clinical science. Special interest has centered onreinforcement learning (RL) tasks and models. However,laboratory tasks in general often have psychometricweaknesses and RL tasks pose special challenges. Thesechallenges must be addressed if computational psychiatry is tocapitalize on its promise of developing sensitive, replicableassays of cognitive function. Few resources identify thesechallenges and discuss strategies to mitigate them. Here, wefirst overview general psychometric challenges associated withlaboratory tasks, as these may be unfamiliar to cognitivescientists. Next, we illustrate how these challenges interactwith issues specific to RL tasks, in the context of presenting acase example of preparing an RL task for computationalpsychiatry. Throughout, we highlight how consideringmeasurement issues prior to a clinical science study can informstudy design.