Inferring sources of suboptimality in perceptual decision making using a causal inference task

Perceptual decision-making has been extensively modeled using the ideal observer framework. However, a range of deviations from optimality demand an extension of this framework to characterize the different sources of suboptimality. Prior work has mostly formalized these sources by adding biases and variability in the context of specific process models but are hard to generalize to more complex tasks. Here, we formalize suboptimalities as part of the brains probabilistic model of the task. Data from a traditional binary discrimination task cannot separate between different kinds of biases, or between sensory noise and approximate computations. We showed that this was possible using a recently developed causal inference task in which observers discriminated auditory cues in the presence of choice-uninformative visual cues. An extension of the task with different stimulus durations provided evidence for an increase in the precision of the computations with stimulus duration, separate from a decrease in observation noise.