Decision models such as the drift-diffusion model (DDM) are a widely used and broadly accepted tool that performs remarkably well in accounting for binary choices and their response time distributions, as a function of the option values. Such models are built on an evidence accumulation to bound concept, where a decision maker repeatedly samples a mental representation of the values of the options on offer until satisfied that there is enough evidence in favor of one option over the other. The value estimates that drive the DDM evidence are derived from the relative strength of value signals that are not stable across time, so that repeated sequential samples are necessary to average out noise. The DDM, however, typically does not allow for different options to have different levels of variability in their value representations. However, studies have shown that a decision maker often reports levels of certainty regarding value estimates that vary across options. We thus propose that future versions of DDM should include an option-specific value certainty component. We present four different versions of such a model and validate them against empirical data from four previous studies. The data show that a model built around a sort of signal-to-noise ratio for each option (rather than a pure signal that randomly fluctuates) performs best, accounting for the positive impact of value certainty on choice consistency and the negative impact of value certainty on response time.
bioRxiv Subject Collection: Neuroscience