On the Difficulty of Overcoming One's Accuracy Bias for Choosing an Optimal Speed-Accuracy Tradeoff

Under time pressure, it is usually not possible to respond quickly and accurately at the same time. Therefore, people must trade speed for accuracy, depending on the current payoff conditions. Ideally, they should choose a speed-accuracy tradeoff (SAT) that optimizes their monetary reward. However, this is hardly the case. Rather, persons exhibit an accuracy bias, which is often disadvantageous. To further investigate the role of errors for optimizing reward, we conducted a flanker-task study with different payoff and framing conditions. Whereas the reward for correct responses always increased continuously with speed, the costs of errors varied. In three of four conditions, responding very fast, even with low accuracy, was favorable. Furthermore, in addition to the usual gain framing, half of our participants were instructed according to a loss frame. Whereas framing had little effect on performance, we found a substantial accuracy bias. Only in the most extreme condition some participants overcame their bias and responded very quickly. To examine how SAT strategies differed between participants, we modeled the performance with a sequential-sampling model. The results suggest that various mechanisms were involved in realizing specific SATs. However, they were hardly applied to optimize reward. Rather, participants seem to have optimized their well-being. Public Significance Statement For optimizing reward under time pressure (e.g., at the stock market), it is sometimes favorable to trade accuracy for speed. However, this is not what persons usually do. Rather, they exhibit an accuracy bias, that is, they are reluctant to make errors. In the present study we investigated effects of payoff schemes and loss versus gain framing on speed-accuracy tradeoff and observed that even in the most extreme condition, only few persons overcame their accuracy bias. Rather than optimizing monetary reward, it seems that most persons optimized their well-being by avoiding making errors.