Asymmetric representation of aversive prediction errors in Pavlovian threat conditioning.

Survival in biological environments requires learning associations between predictive sensory cues and threatening outcomes. Such aversive learning may be implemented through reinforcement learning algorithms that are driven by the signed difference between expected and encountered outcomes, termed prediction errors (PEs). While PE-based learning is well established for reward learning, the role of putative PE signals in aversive learning is less clear.

Inhibiting Human Aversive Memory by Transcranial Theta-Burst Stimulation to the Primary Sensory Cortex.

Background: Predicting adverse events from past experience is fundamental for many biological organisms. However, some individuals suffer from maladaptive memories that impair behavioral control and well-being, e.g.

Measuring learning in human classical threat conditioning: Translational, cognitive and methodological considerations.

Threat conditioning is a laboratory model of associative learning across species that is often used in research on the etiology and treatment of anxiety disorders. At least 10 different conditioned responses (CR) for quantifying learning in human threat conditioning are found in the literature. In this narrative review, we discuss these CR by considering the following questions: (1) Are the CR indicators of amygdala-dependent threat learning? (2) To what components of formal learning models do the CR relate? (3) How well can threat learning be inferred from the CR? Despite a vast literature, these questions can only be answered for some CR.

Dopaminergic Drug Effects on Probability Weighting during Risky Decision Making.

Dopamine has been associated with risky decision-making, as well as with pathological gambling, a behavioral addiction characterized by excessive risk-taking behavior. However, the specific mechanisms through which dopamine might act to foster risk-taking and pathological gambling remain elusive. Here we test the hypothesis that this might be achieved, in part, via modulation of subjective probability weighting during decision making.