Retrieval-constrained valuation: Toward prediction of open-ended decisions

Abstract

Real-world decisions are often open ended, with goals, choice options, or evaluation criteria conceived by decision-makers themselves. Critically, the quality of decisions may heavily rely on the generation of options, as failure to generate promising options limits, or even eliminates, the opportunity for choosing them. This core aspect of problem structuring, however, is largely absent from classical models of decision-making, thereby restricting their predictive scope. Here, we take a step toward addressing this issue by developing a neurally inspired cognitive model of a class of ill-structured decisions in which choice options must be self-generated. Specifically, using a model in which semantic memory retrieval is assumed to constrain the set of options available during valuation, we generate highly accurate out-of-sample predictions of choices across multiple categories of goods. Our model significantly and substantially outperforms models that only account for valuation or retrieval in isolation or those that make alternative mechanistic assumptions regarding their interaction. Furthermore, using neuroimaging, we confirm our core assumption regarding the engagement of, and interaction between, semantic memory retrieval and valuation processes. Together, these results provide a neurally grounded and mechanistic account of decisions with self-generated options, representing a step toward unraveling cognitive mechanisms underlying adaptive decision-making in the real world.

Sequential consumer choice as multi-cued retrieval

“…In addition, individual differences in the extent to which each representation was recruited predicted how many products would be forgotten or removed. This would not be predicted by many existing models of semantic memory retrieval (Abbott et al, 2015;Hills et al, 2012) and option generation (Bhatia, 2019;Zhang et al, 2021), which rely on a single measure of association. Associative knowledge likely takes several forms (e.g., see Mirman et al, 2017), which is consistent with our modelling approach and results.…”

“…This likely explains why the sequential choices of online grocery shoppers clustered over time, as they do in fluency tasks (Abbott et al, 2015;Avery & Jones, 2018;Bousfield & Sedgewick, 1944;Gruenewald & Lockhead, 1980;Hills et al, 2012). We build upon past research (Bhatia, 2019;Kaiser et al, 2013;Kalis et al, 2013;Keller & Ho, 1988;Zhang et al, 2021) by showing how memory retrieval mechanisms influence the generation of options in sequential decisionmaking tasks. These results would not be predicted by many classical models of preferential choice, which consider option-retrieval to be out-of-scope (Busemeyer & Rieskamp, 2014;Glimcher & Rustichini, 2004;Rangel et al, 2008).…”

“…Although we focused on sequential retrieval of choice options, determining whether an option is goal relevant is also key to choice (see Figure 1c). For example, a person's subjective preferences will affect the perceived value of a retrieved option (Hornsby & Love, 2020;Levy & Glimcher, 2011;Zhang et al, 2021) and partly determine whether the item is added to their basket. Choice itself can affect preferences (Hornsby & Love, 2020), which in turn may affect memory retrieval.…”

“…Yet, in real-world tasks like online grocery shopping, the space of possible options is too large to be considered at once. Much therefore depends on how options are retrieved from long-term memory (Bhatia, 2019;Kalis et al, 2013;Keller & Ho, 1988;Zhang et al, 2021). The task itself can provide the context to retrieve associated choice options.…”

Neural support for contributions of utility and narrative processing of evidence in juror decision making

“…We have separated these utility and narrative processes according to a split between quantitative and qualitative reasoning. However, we note that modern efforts have proposed the integration of features of recall into utility models of decision making (Shadlen and Shohamy, 2016;Zhang et al, 2021). To preserve the ability to compare these models, we maintain the sets of cognitive processes as two, non-overlapping, groups of cognitive processes.…”