Helping Humans Adapt to Changing Choice Environments: Effects of Interventions and Direction of Change in Binary Choice Tasks

Adapting to dynamic environments poses significant challenges for humans, even in seemingly simple scenarios. Researchers have explored different directions to address this issue, including the use of cognitive models to predict human adaptive capabilities. This research investigates the effectiveness of an intervention and the role that an Instance-Based Learning (IBL) cognitive model could play in facilitating adaptation to changing conditions. We conducted an experiment involving a binary choice task, manipulating the presence of an intervention and the direction of change in outcome payoffs: either increasing (where one option improves over time) or decreasing (where one option deteriorates over time). Our findings reveal that: a) the intervention appears effective primarily in increasing conditions, b) adaptation is better in decreasing conditions, c) the IBL model outperforms human participants in adaptation, and d) the model exhibits greater accuracy in predicting humans' choices in increasing rather than decreasing conditions. These results are discussed in the context of experiential decision-making literature and the potential of using IBL models for intervention to improve human adaptation.