Abstract
Suicide is a leading cause of death worldwide. Despite decades of clinical and theoretical accounts that suggest that suicidal thoughts and behaviors are efforts to escape painful emotions, little prior research has examined decision making involved in escaping aversive states. We compared the performance of 85 suicidal participants to 44 nonsuicidal psychiatric patients on a novel reinforcement learning task with choices to make either active (i.e., "go") or passive responses (i.e., "no-go") to either escape or avoid an aversive stimulus. We used a computational cognitive model to isolate decision-making biases. We hypothesized that suicidal participants would exhibit a relatively elevated bias for making active responses to escape an aversive state and would show worse performance when escape required a passive response (i.e., "doing nothing" to escape). Our hypotheses were supported: The computational model revealed that suicidal participants exhibited a higher bias for an active response to escape compared with nonsuicidal psychiatric controls, suggesting that this finding was not just the result of the presence of psychopathology. The bias parameter also accounted for unique variance in predicting group status among several constructs previously related to suicidal thoughts and behaviors. This study provides a new method for testing escape decision making and does so using a computational cognitive model, allowing us to precisely index processes underlying suicidal and related behaviors. Future research examining escape decision making from a computational perspective could help link neural processes or environmental stressors to suicidal thoughts or behaviors.
