Cognitive models of optimal sequential search with recall

Many everyday decisions require sequential search, according to which available choice options are observed one at a time, with each observation involving some cost the decision maker. In these tasks, makers need trade-off chances of finding better search. Optimal strategies in such tasks involve threshold rules, terminate search as soon an option exceeding reward value is found. Threshold rules can be seen special cases well-known algorithmic processes, satisficing heuristic. Prior work has found that do use however stopping thresholds data typically smaller than (expected maximizing) optimal threshold. We put forward array cognitive models and parametric model fits on participant-level examine why adopt seemingly suboptimal thresholds. find people's behavior consistent if we allow participants display risk aversion, psychological effort cost, error. Thus, appear able resource-rational manner maximizes stochastic averse utility. Our findings shed light factors guide making, show how used describe both computational aspects behavior.