No abstract

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. Among the CR considered, heart period, startle eye-blink and Pavlovian-to-instrumental transfer are most clearly related to amygdala-dependent threat learning. Formal learning models have mostly been studied for skin conductance responses, which are likely to reflect threat prediction and its uncertainty. Startle eyeblink and pupil size appear to best differentiate CS+/CS−, although few direct comparisons between CR exist. We suggest future directions for improving the quantification of threat conditioning.

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. Among the CR considered, heart period, startle eye-blink and Pavlovian-to-instrumental transfer are most clearly related to amygdala-dependent threat learning. Formal learning models have mostly been studied for skin conductance responses, which are likely to reflect threat prediction and its uncertainty. Startle eye-blink and pupil size appear to best differentiate CS+/CS−, although few direct comparisons between CR exist. We suggest future directions for improving the quantification of threat conditioning.

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. Human healthy controls (n = 21) and pathological gamblers (n = 16) played a decision-making task involving choices between sure monetary options and risky gambles both in the gain and loss domains. Each participant played the task twice, either under placebo or the dopamine D2/D3 receptor antagonist sulpiride, in a double-blind counterbalanced design. A prospect theory modelling approach was used to estimate subjective probability weighting and sensitivity to monetary outcomes. Consistent with prospect theory, we found that participants presented a distortion in the subjective weighting of probabilities, i.e., they overweighted low probabilities and underweighted moderate to high probabilities, both in the gain and loss domains. Compared with placebo, sulpiride attenuated this distortion in the gain domain. Across drugs, the groups did not differ in their probability weighting, although gamblers consistently underweighted losing probabilities in the placebo condition. Overall, our results reveal that dopamine D2/D3 receptor antagonism modulates the subjective weighting of probabilities in the gain domain, in the direction of more objective, economically rational decision making.