People are at least as good at optimizing reward rate under equivalent fixed-trial compared to fixed-time conditions.

Finding an optimal decision-making strategy requires a careful balance between the competing demands of accuracy and urgency. In experimental settings, researchers are typically interested in whether people can optimise this trade-off, typically operationalised as reward rate, with evidence accumulation models serving as the key framework to determine whether people are performing optimally. However, recent studies have suggested that inferences about optimality can be highly dependent on the task design, meaning that inferences about whether people can achieve optimality may not generalise across contexts. Here, we investigate one typically overlooked design factor: whether participants spend a fixed amount of time on each block (fixed time) or have a fixed number of trials in each block (fixed trials). While fixed-time designs are typically thought to be the most appropriate for optimality studies, as to maximise the number of correct responses participants must optimise RR, our Experiments 1 and 2 indicate that people are at least as good at optimising reward rate under fixed-trial designs as fixed-time designs. However, Experiment 3 provides some evidence that fixed-trial designs with no instructions may not be at least as good as fixed-time designs with very specific instructions. Importantly, these findings challenge the idea that fixed-time designs are the most appropriate for reward rate optimality studies, and further emphasise the importance of carefully considering study design factors when making inferences about optimality in decision-making.