Humans often simultaneously pursue multiple plans at different time scales. The successful realization of non-immediate plans (e.g., post package after work) requires keeping track of a future plan while accomplishing other intermediate tasks (e.g., write a paper), a capacity known as prospective memory . This capacity requires the integration of noisy evidence from perceptual input with evidence from short-term working memory (WM) and longer-term or episodic memory (LTM/EM). Here we formulate a set of dual-task problems in empirical studies of prospective memory as problems of computational rationality, and ask how a rational model should exploit noisy perception and memory to maximize payoffs. The model combines reinforcement learning (optimal action selection) with evidence accumulation (optimal inference) in order to derive good decision parameters for optimal task performance (i.e., performing an ongoing task while monitoring for a cue that triggers executing a second prospective task). We compare model behavior to key accuracy and reaction time phenomena in human performance. Thus, we offer a normative approach to theorizing and modeling these phenomena without assumptions about mechanisms of attention or retrieval. This approach can be extended to study meta-parameters governing the boundedly rational use of memory in planned action in health, as well as compensatory mnemonic strategies that may be rational responses to disturbances of these mechanisms in neuropsychiatric disorders.
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