Full reaction time distributions reveal the complexity of neural decision-making.

Measurement of the stochastic distribution of reaction time or latency has become a popular technique that can potentially provide precise, quantitative information about the underlying neural decision mechanisms. However, this approach typically requires data from large numbers of individual trials, in order to enable reliable distinctions to be made between different models of decision. When data are not plentiful, an approximation to full distributional information can be provided by using a small number of quantiles instead of full distributions - often, just five are used. Although this can often be adequate when the proposed underlying model is a relatively simple one, we show here that, with more complex tasks, and correspondingly extended models, this kind of approximation can often be extremely misleading, and may hide important features of the underlying mechanisms that only full distributional analysis can reveal.