LAUSR

Humans often simultaneously pursue multiple plans at different time scales, a capacity known as prospective memory (PM). 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). Prospective memory capacity requires the integration of noisy evidence from perceptual input with evidence from both short-term working memory (WM) and longer-term or episodic memory (LTM/EM). Here we formulate a set of empirical studies of prospective memory, all dual-task problems, as problems of computational rationality. We ask how a rational model should integrate noisy perceptual evidence and memory to maximize payoffs in these PM studies. 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 human behavioral evidence of key accuracy and reaction time phenomena in PM. Notably, our normative approach to theorizing and modeling these phenomena makes no assumptions about mechanisms of attention or retrieval. This approach can be extended to study the learning and use of meta-parameters governing the boundedly rational use of memory in planned action in health and disease. A computational psychiatry extension of the model can capture compensatory mnemonic strategies in neuropsychiatric disorders that may be rational responses to disturbances of inference, memory, and action selection.