Theories of reward learning in neuroscience have focused on two families of algorithms
thought to capture deliberative versus habitual choice. ‘Model-based’ algorithms compute the …

Humans and animals are capable of evaluating actions by considering their long-run future
rewards through a process described using model-based reinforcement learning (RL) …

Evaluating choices in multi-step tasks is thought to involve mentally simulating trajectories.
Recent theories propose that the brain simplifies these laborious computations using …

Managing multiple goals is essential to adaptation, yet we are only beginning to understand
computations by which we navigate the resource demands entailed in so doing. Here, we …

Background: Behavioral activation is an evidence-based treatment for depression. Theoretical
considerations suggest that treatment response depends on reinforcement learning …

We discuss opportunities in applying the resource-rationality framework toward answering
questions in emotion and mental health research. These opportunities rely on …

Background: Behavioral activation is an evidence-based treatment for depression. Theoretical
considerations suggest that treatment response depends on reinforcement learning …

(6)[QMB t+ 1 (press= g) QMB t+ 1 (press= j)]=[β MB reward P (f= reward| press= g) t β MB
punish P (f= punish| press= g) t β MB reward P (f= reward| press= j) t β MB punish P (f …

… Russek … Lastly, I thank my parents, Michael and Holly Russek, who have provided me
with incredible opportunities and unwavering encouragement throughout my life. …

… measurement tool that employs electromagnetic (EM) transmit and receive channels to non-…
complex EM material properties on images and MRI-based measurement of EM properties …