Stochastic satisficing account of confidence in uncertain value-based decisions

Every day we make choices under uncertainty; choosing what route to work or which queue in a supermarket to take, for example. It is unclear how outcome variance, e.g. uncertainty about waiting time in a queue, affects decisions and confidence when outcome is stochastic and continuous. How does one evaluate and choose between an option with unreliable but high expected reward, and an option with more certain but lower expected reward? Here we used an experimental design where two choices payoffs took continuous values, to examine the effect of outcome variance on decision and confidence. We found that our participants probability of choosing the good (high expected reward) option decreased when the good or the bad options payoffs were more variable. Their confidence ratings were affected by outcome variability, but only when choosing the good option. Unlike perceptual detection tasks, confidence ratings correlated only weakly with decisions time, but correlated with the consistency of trial-by-trial choices. Inspired by the satisficing heuristic, we propose a \"stochastic satisficing\" (SSAT) model for evaluating options with continuous uncertain outcomes. In this model, options are evaluated by their probability of exceeding an acceptability threshold, and confidence reports scale with the chosen options thus-defined satisficing probability. Participants decisions were best explained by an expected reward model, while the SSAT model provided the best prediction of decision confidence. We further tested and verified the predictions of this model in a second experiment. Our model and experimental results generalize the models of metacognition from perceptual detection tasks to continuous-value based decisions. Finally, we discuss how the stochastic satisficing account of decision confidence serves psychological and social purposes associated with the evaluation, communication and justification of decision-making.\n\nAuthor SummaryEvery day we make several choices under uncertainty, like choosing a queue in a supermarket. However, the computational mechanisms underlying such decisions remain unknown. For example, how does one choose between an option with unreliable high expected reward, like the volatile express queue, and an option with more certain but lower expected reward in the standard queue? Inspired by bounded rationality and the notion of satisficing, i.e. settling for a good enough option, we propose that such decisions are made by comparing the likelihood of different actions to surpass an acceptability threshold. When facing uncertain decisions, our participants confidence ratings were not consistent with the expected outcomes rewards, but instead followed the satisficing heuristic proposed here. Using an acceptability threshold may be especially useful when evaluating and justifying decisions under uncertainty.