The perils of omitting omissions when modeling evidence accumulation — UC Berkeley

Author summary Many studies of human decision-making use tasks that impose time limits, but researchers often ignore trials in which participants fail to respond before the deadline. This paper shows that such “omissions” are more informative than they might appear—and that leaving them out can lead to misleading conclusions. The authors demonstrate that ignoring omissions distorts the estimated parameters that describe how people accumulate evidence and make choices under time pressure. To address this, they develop a new computational approach that models both responses and omissions together. Using modern machine-learning tools, specifically Likelihood Approximation Networks (LANs) combined with a new Omission Probability Network (OPN), the method efficiently estimates how likely omissions are to occur under different model settings. Across a range of decision-making models and conditions, this joint modeling approach greatly improves the accuracy and reliability of parameter estimation, even when omissions are rare. The work highlights how small analytic shortcuts—like excluding no-response trials—can meaningfully affect scientific inference and proposes a practical solution implemented in an open-source software package, making it easier for researchers to include omissions in their models and produce more trustworthy conclusions about the mechanisms of human decision-making.