Noisy Memory Generates Value in Changing Environments

Experimental data suggest that episodic memory is involved in sequential value-based decision-making. By contrast, standard computational models of decision-making assume that prior reward outcomes are integrated into subjective values rather than remembered discretely. Previous work developed a minimal computational framework for sequential value-based decision-making that is based on noisy sampling of episodic memories, rather than calculating value. We called these agents "Imperfect Memory Programs" (IMPs) and showed how their single free parameter optimizes the trade-off between the magnitude of error and the complexity of imperfect recall. Here, we develop biologically plausible approximations to the IMPs with lossy agents (LIMPs) that maintain only 1 bit of reward memory for binary outcomes but fail to encode rewards with some probability. Both IMPs and LIMPs perform similarly to or better than a simple agent with perfect memory in multiple classic decision-making tasks and generate phenomenology that resembles value-based computations. We find that allowing different encoding probabilities for rewards and omissions improves performance further and allows to trade-off matching versus maximizing behavior, as well as flexible versus stable performance. Together, these results suggest that episodic agents can approximate value-based agents through capitalizing on realistic encoding and/or sampling noise. This suggests that mnemonic errors (1) can improve, rather than impair decision-making and (2) provide a plausible alternative explanation for some behavioral correlates of "value".