  Inferring processes or constructs from performance data is a major hallmark
of cognitive psychometrics. Particularly, diffusion modeling of response times
(RTs) from correct and erroneous responses using the Wiener distribution has
become a popular measurement tool because it provides a set of psychologically
interpretable parameters. However, an important precondition to identify all of
these parameters is a sufficient number of RTs from erroneous responses. In the
present article, we show by simulation that the parameters of the Wiener
distribution can be recovered from tasks yielding very high or even perfect
response accuracies using the shifted Wald distribution. Specifically, we argue
that error RTs can be modeled as correct RTs that have undergone censoring by
using techniques from parametric survival analysis. We illustrate our reasoning
by fitting the Wiener and (censored) shifted Wald distribution to RTs from six
participants who completed a Go/No-go task. In accordance with our simulations,
diffusion modeling using the Wiener and the shifted Wald distribution yielded
identical parameter estimates when the number of erroneous responses was
predicted to be low. Moreover, the modeling of error RTs as censored correct
RTs substantially improved the recovery of these diffusion parameters when
premature trial timeout was introduced to increase the number of omission
errors. Thus, the censored shifted Wald distribution provides a suitable means
for diffusion modeling in situations when the Wiener distribution cannot be
fitted without parametric constraints.
