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The aim of a number of psychophysics tasks is to uncover how mammals makedecisions in a world that is in flux. Here we examine the characteristics ofideal and near-ideal observers in a task of this type. We ask when and howperformance depends on task parameters and design, and, in turn, what observerperformance tells us about their decision-making process. In the dynamic clickstask subjects hear two streams (left and right) of Poisson clicks withdifferent rates. Subjects are rewarded when they correctly identify the sidewith the higher rate, as this side switches unpredictably. We show that areduced set of task parameters defines regions in parameter space in whichoptimal, but not near-optimal observers, maintain constant response accuracy.We also show that for a range of task parameters an approximate normative modelmust be finely tuned to reach near-optimal performance, illustrating apotential way to distinguish between normative models and their approximations.In addition, we show that using the negative log-likelihood and the 0/1-lossfunctions to fit these types of models is not equivalent: the 0/1-loss leads toa bias in parameter recovery that increases with sensory noise. These findingssuggest ways to tease apart models that are hard to distinguish when tunedexactly, and point to general pitfalls in experimental design, model fitting,and interpretation of the resulting data.

Performance of normative and approximate evidence accumulation on the dynamic clicks task