Process and Content in Decisions from Memory

Abstract

Information stored in memory influences the formation of preferences and beliefs in most everyday decision tasks. The richness of this information, and the complexity inherent in interacting memory and decision processes, makes the quantitative model-driven analysis of such decisions very difficult. In this paper we present a general framework that is capable of addressing the theoretical and methodological barriers to building formal models of naturalistic memory-based decision making. Our framework implements established theories of memory search and decision making within a single integrated cognitive system, and uses computational language models to quantify the thoughts over which memory and decision processes operate. It can thus describe both the content of the information that is sampled from memory, as well as the processes involved in retrieving and evaluating this information in order to make a decision. Furthermore, our framework is tractable, and the parameters that characterize memory-based decisions can be recovered using thought-listing and choice data from existing experimental tasks, and in turn be used to make quantitative predictions regarding choice probability, length of deliberation, retrieved thoughts, and the effects of decision context. We showcase the power and generality of our framework by applying it to study risk perception, consumer behavior, financial decision making, ethical decision making, legal decision making, food choice, and judgments about well-being, society and culture.

Mixing memory and desire: How memory reactivation supports deliberative decision-making.

“…In this section, we outline the features of content and process (Zhao et al, 2019) that mediate the impacts of memories on decisions.…”

“…An emerging framework describes this phenomenon as a simulation-driven estimation process, examining what might result from each available action by consulting memories of similar previous settings. This approach, generally referred to as memory sampling (Bordalo et al, 2020;Gershman & Daw, 2017;Kuwabara & Pillemer, 2010;Lengyel & Dayan, 2008;Lieder et al, 2018;Ritter et al, 2018;Shadlen & Shohamy, 2016;Zhao et al, 2019) , can approximate the sorts of option value estimates that would be learned across repeated experience by, e.g., temporal-difference reinforcement learning (TDRL; (Gershman & Daw, 2017;Lengyel & Dayan, 2008) ), while retaining the flexibility to diverge from long-run averages when doing so may be adaptive. At one extreme, drawing on individual memories in this way allows one to effectively tackle choice problems even in the low-data limit (e.g., in novel environments), where processes that rely on abstraction over multiple experiences are unreliable (Lengyel & Dayan, 2008) .…”

Risky choice frames shift the structure and emotional valence of internal arguments: A query theory account of the unusual disease problem

“…Other, closely related work, measures the relationship between the content of arguments to their structure (Zhao, Richie & Bhatia, 2020). Instead of using sentiment analysis and relative-sentiment index to describe the contents of thoughts, they used semantic space models and computational memory models.…”