Adding or removing context components equally disrupts extinction in human predictive learning

In two human predictive learning experiments, we investigated the impact of adding or removing context components on extinction performance toward a cue. In each experiment, participants initially received repeated pairings of a cue and an outcome in a context composed of two distinctive components (context AB). Initial training was followed by a series of trials in which the cue was no longer followed by the outcome. This extinction treatment was conducted in the presence of a different pair of distinctive context components (context CD). During a final test, we observed that changing the extinction context CD disrupted extinction performance toward the cue regardless of whether the context was changed by adding or removing context components. We discuss implications of our results for theories of associative learning.