Shifting Episodic Prediction With Online Cognitive Bias Modification: A Randomized Controlled Trial

Negative future thinking pervades emotional disorders. This hybrid efficacy-effectiveness trial tested a four-session, scalable online cognitive-bias-modification program for training more positive episodic prediction. Nine hundred fifty-eight adults (73.3% female, 86.5% White, 83.4% from United States) were randomly assigned to positive conditions with ambiguous future scenarios that ended positively, 50/50 conditions that ended positively or negatively, or a control condition with neutral scenarios. As hypothesized (preregistration: ), positive-training participants improved more than control participants in negative expectancy bias (d = -0.58), positive expectancy bias (d = 0.80), and self-efficacy (d = 0.29). Positive training was also superior to 50/50 training for expectancy bias and optimism (d = 0.31). Training gains attenuated yet remained by 1-month follow-up. Unexpectedly, participants across conditions improved comparably in anxiety and depression symptoms and growth mindset. Targeting a transdiagnostic process with a scalable program may improve bias and outlook; however, further validation of outcome measures is required.

Automated summary

Negative future thinking is common in emotional disorders. This study tested an online cognitive-bias-modification program consisting of four sessions to train individuals to have more positive predictions about the future. Results showed that participants who received positive training had improved negative expectancy bias, positive expectancy bias, and self-efficacy compared to the control group. Positive training was also more effective than 50/50 training in improving expectancy bias and optimism. Although improvements attenuated over time, they were still present at the 1-month follow-up. Surprisingly, participants in all conditions showed comparable improvements in anxiety, depression symptoms, and growth mindset. These findings suggest that targeting a transdiagnostic process with a scalable program may improve cognitive biases and outlook, but further validation of outcome measures is needed.