Psychological therapies are among the most effective treatments for common mental health problems—however, we still know relatively little about how exactly they improve symptoms. Here, we demonstrate the power of combining theory with computational methods to parse effects of different components of cognitive-behavioral therapies onto underlying mechanisms. Specifically, we present data from a series of randomized-controlled experiments testing the effects of brief components of behavioral and cognitive therapies on different cognitive processes, using well-validated behavioral measures and associated computational models. A goal setting intervention, based on behavioral activation therapy activities, reliably and selectively reduced sensitivity to effort when deciding how to act to gain reward. By contrast, a cognitive restructuring intervention, based on cognitive therapy materials, reliably and selectively reduced the tendency to attribute negative everyday events to self-related causes. The effects of each intervention were specific to these respective measures. Our approach provides a basis for beginning to understand how different elements of common psychotherapy programs may work.
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