In mobile crowdsensing (MCS), users are repeatedly asked to make choices between a set of alternatives, i.e., whether to contribute to a task or not and which task to contribute to. The platform coordinating the MCS campaigns engineers these choices by selecting the tasks to present to each user and offering incentives to ensure user contributions and maximize the benefit from them. In this paper, we revisit the well-investigated question of how to optimize the contributions of crowds of mobile end users to MCS tasks. However, we depart from the bulk of related literature by explicitly accounting for the bounded rationality of human decision making. Bounded rationality is a consequence of cognitive and other kinds of constraints, (e.g., time pressure) and has been studied extensively in behavioral science.We model bounded rationality after two instances of lexicographic decision-making models that originate in the field of cognitive psychology: Fast-and-Frugal-Trees (FFTs) and Discrete Elimination by Aspects (DEBA). With each MCS task modeled as a vector of feature values, the decision process under both models proceeds through sequentially parsing lexicographically ordered features, resulting in choices that are satisfying, but not necessarily optimal. We study, in particular, scenarios where a single task or a pair of tasks are presented to MCS users together with reward offers that adhere to per-task budget constraints. We formulate the optimization problems that emerge for the MCS campaign organizers as instances of the Generalized Assignment Problem (GAP), an NP-hard problem for which approximate algorithms are available. Our evaluation suggests that our optimization approach exhibits significant gains when compared to heuristic rules that do not account for the lexicographic structure in human decision making.