The influence of Markov decision process structure on the possible strategic use of working memory and episodic memory.
Researchers use a variety of behavioral tasks to analyze the effect of biological manipulations on memory function. This research will benefit from a systematic mathematical method for analyzing memory demands in behavioral tasks. In the framework of reinforcement learning theory, these tasks can be mathematically described as partially-observable Markov decision processes. While a wealth of evidence collected over the past 15 years relates the basal ganglia to the reinforcement learning framework, only recently has much attention been paid to including psychological concepts such as working memory or episodic memory in these models. This paper presents an analysis that provides a quantitative description of memory states sufficient for correct choices at specific decision points. Using information from the mathematical structure of the task descriptions, we derive measures that indicate whether working memory (for one or more cues) or episodic memory can provide strategically useful information to an agent. In particular, the analysis determines which observed states must be maintained in or retrieved from memory to perform these specific tasks. We demonstrate the analysis on three simplified tasks as well as eight more complex memory tasks drawn from the animal and human literature (two alternation tasks, two sequence disambiguation tasks, two non-matching tasks, the 2-back task, and the 1-2-AX task). The results of these analyses agree with results from quantitative simulations of the task reported in previous publications and provide simple indications of the memory demands of the tasks which can require far less computation than a full simulation of the task. This may provide a basis for a quantitative behavioral stoichiometry of memory tasks.
Zilli
Eric A
EA
Center for Memory and Brain, Boston University, Boston, Massachusetts, United States of America. zilli@bu.edu
Hasselmo
Michael E
ME
D000465:Algorithms; D000818:Animals; D001331:Automation; D003198:Computer Simulation; D006801:Humans; D007858:Learning; D008390:Markov Chains; D008568:Memory; D008570:Memory, Short-Term; D011939:Mental Recall; D008954:Models, Biological; D008959:Models, Neurological; D015233:Models, Statistical; D008962:Models, Theoretical