Humans combine value learning and hypothesis testing strategically in multi-dimensional probabilistic reward learning

Fear attenuation collaborations to optimize translation

Here, we describe the efforts we dedicated to the challenge of modifying entrenched emotionally laden memories. In recent years, through a number of collaborations and using a combination of behavioral, molecular, and computational approaches, we: (a) developed novel approaches to fear attenuation that engage mechanisms that differ from those engaged during extinction (Monfils), (b) examined whether our approaches can generalize to other reinforcers (Lee, Gonzales, Chaudhri, Cofresi, and Monfils), (c) derived principled explanations for the differential outcomes of our approaches (Niv, Gershman, Song, and Monfils), (d) developed better assessment metrics to evaluate outcome success (Shumake and Monfils), (e) identified biomarkers that can explain significant variance in our outcomes of interest (Shumake and Monfils), and (f) developed better basic research assays and translated efforts to the clinic (Smits, Telch, Otto, Shumake, and Monfils). We briefly highlight each of these milestones and conclude with final remarks and extracted principles. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Naturalistic reinforcement learning

Humans possess a remarkable ability to make decisions within real-world environments that are expansive, complex, and multidimensional. Human cognitive computational neuroscience has sought to exploit reinforcement learning (RL) as a framework within which to explain human decision-making, often focusing on constrained, artificial experimental tasks. In this article, we review recent efforts that use naturalistic approaches to determine how humans make decisions in complex environments that better approximate the real world, providing a clearer picture of how humans navigate the challenges posed by real-world decisions. These studies purposely embed elements of naturalistic complexity within experimental paradigms, rather than focusing on simplification, generating insights into the processes that likely underpin humans' ability to navigate complex, multidimensional real-world environments so successfully.

Correction: Humans combine value learning and hypothesis testing strategically in multi-dimensional probabilistic reward learning

[This corrects the article DOI: 10.1371/journal.pcbi.1010699.].