A little goes a long way: Fitting one-shot decisions with cognitive models

Cognitive models provide profound insights into the psychological processes underlying behavior. However, a significant limitation has constrained their application: the reliance on extensive, repeated-trial data from each participant. The data-hungry nature of cognitive models has largely precluded their application to infrequent but consequential one-shot decisions common in economic, social, and clinical contexts, and has excluded populations unable to complete lengthy experiments. Here, we address this methodological constraint by proposing a conceptual shift: instead of requiring many trials from a few individuals, we leverage few trials from many individuals. By treating between-subject variability as a source of information, we demonstrate that cognitive models can be successfully fit to one-shot data. Through a series of simulations, we first establish that we can recover known parameter values from single-trial data. We then validate the approach empirically by showing that with only a single trial per participant it can replicate canonical findings; namely, the speed-accuracy trade-off, the influence of food quality and expectations on choice, and the task-specificity of linear vs. non-linear numeric representations. This work overcomes a major limitation to the widespread application of cognitive models, opening new frontiers for understanding the cognitive mechanisms of real-world choices.