Clinical Manifestations.

Background: Beginning new Alzheimer's disease (AD) treatments before AD symptoms are prominent would optimize the benefits of these disease slowing treatments. To accomplish this goal, clinicians must identify measures of early disease progression. As a step in doing this, we set out to characterize the relationships between cognitive complaints, wellbeing, cognitive performance, and metacognitive calibration in older adults in order to inform a model of cognition in typical older adults.

Lesions to different regions of frontal cortex have dissociable effects on voluntary persistence.

Deciding how long to keep waiting for uncertain future rewards is a complex problem. Previous research has shown that choosing to stop waiting results from an evaluative process that weighs the subjective value of the awaited reward against the opportunity cost of waiting. Activity in ventromedial prefrontal cortex (vmPFC) tracks the dynamics of this evaluation, while activation in the dorsomedial prefrontal cortex (dmPFC) and anterior insula (AI) ramps up before a decision to quit is made.

Leveraging Decision Science to Characterize Depression.

This brief review examines the potential to use decision science to objectively characterize depression. We provide a brief overview of the existing literature examining different domains of decision-making in depression. Because this overview highlights the specific role of reinforcement learning as an important decision process affected in the disorder, we then introduce reinforcement learning modeling and explain how this approach has identified specific reinforcement learning deficits in depression.

Lesions to different regions of frontal cortex have dissociable effects on voluntary persistence.

Deciding how long to keep waiting for uncertain future rewards is a complex problem. Previous research has shown that choosing to stop waiting results from an evaluative process that weighs the subjective value of the awaited reward against the opportunity cost of waiting. In functional neuroimaging data, activity in ventromedial prefrontal cortex (vmPFC) tracks the dynamics of this evaluation, while activation in the dorsomedial prefrontal cortex (dmPFC) and anterior insula (AI) ramps up before a decision to quit is made.

Reinforcement-based responsiveness, depression, and anhedonia: A multi-method investigation of intergenerational risk.

Offspring of depressed parents are at an increased risk for depression. Reward- and punishment-based systems might be mechanisms linking maternal outcomes to offspring depression and anhedonia. The current study was designed to investigate the intergenerational relations between maternal markers of reward and punishment responsiveness and their offspring's depression and anhedonia in a community sample of 40 mother (mean age = 44.

Reinforcement learning with associative or discriminative generalization across states and actions: fMRI at 3 T and 7 T.

The model-free algorithms of "reinforcement learning" (RL) have gained clout across disciplines, but so too have model-based alternatives. The present study emphasizes other dimensions of this model space in consideration of associative or discriminative generalization across states and actions. This "generalized reinforcement learning" (GRL) model, a frugal extension of RL, parsimoniously retains the single reward-prediction error (RPE), but the scope of learning goes beyond the experienced state and action.

An energizing role for motivation in information-seeking during the early phase of the COVID-19 pandemic.

The COVID-19 pandemic has highlighted the importance of understanding and managing information seeking behavior. Information-seeking in humans is often viewed as irrational rather than utility maximizing. Here, we hypothesized that this apparent disconnect between utility and information-seeking is due to a latent third variable, motivation.

Learned temporal statistics guide information seeking and shape memory.

Curiosity drives information seeking and promotes learning. Prior work has focused on how curiosity is elicited by intrinsic qualities of information, leaving open questions about how curiosity, exploration, and learning are shaped by the environment. Here we examine how temporal dynamics of the learning environment shape curiosity and learning.