A Parallel Accumulator Model Accounts for Decision Randomness when Deciding on Risky Prospects with Different Expected Value

In decision-making situations individuals rarely have complete information available to select the best option and often show decisional randomness, i.e. given the same amount of knowledge individuals choose different options at different times. Dysfunctional processes resulting in altered decisional randomness can be considered a target process for psychiatric disorders, yet these processes remain poorly understood. Advances in computational modeling of decision-making offer a potential explanation for decisional randomness by positing that decisions are implemented in the brain through accumulation of noisy evidence, causing a generally less preferred option to be chosen at times by chance. One such model, the linear ballistic accumulator (LBA), assumes that individuals accumulate information for each option independently over time and that the first option to reach a threshold will be selected. To investigate the mechanisms of decisional randomness, we applied the LBA to a decision-making task in which risk and expected value (EV) were explicitly signaled prior to making a choice, and estimated separate drift rates for each of the four task stimuli (representing high and low EV and high and low risk). We then used the fitted LBA parameters to predict subject response rates on held-out trials for each of the 6 possible stimulus pairs. We found that choices predicted by LBA were correlated with actual choices across subjects for all stimulus pairs. Taken together, these findings suggest that sequential sampling models can account for decisional randomness on an explicit probabilistic task, which may have implications for understanding decision-making in healthy individuals and in psychiatric populations.