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.

Keywords: memory retrieval; open-ended decisions; option generation; valuation.

Fig. 1.

Task Paradigms. (A) Two decision conditions, EMC and IMC, are distinguished by the presence or absence, respectively, of an explicit menu of choice options. (B) Consistent with the role of constrained memory retrieval on choices in IMC, (Left) deviations between decisions in EMC and IMC are common, as reflected in the off-diagonal items. (Right) Items chosen more often in IMC than in EMC (negative EMC − IMC values) are represented by orange bars, with positive EMC − IMC differences in blue. Statistical significance of item-wise choice share differences was determined by permutation tests (Bonferroni corrected). *P < 0.05; **P < 0.01; ***P < 0.001. (C) To quantify the mnemonic accessibility of different items independently of choice, a third group of participants completed a semantic fluency task in which they listed as many items from memory as possible. (D) Cumulative recall probability (y axis) of different items as a function of recall position (x-axis). (E) Items chosen more often in IMC than EMC are significantly more likely to be recalled in the fluency task than under the reverse case (P < 0.001 across categories, mixed-effects model). Error bars indicate the SEM for each group, collapsing across categories.

Fig. 2.

The RCV Model. (A) IMC, EMC, and fluency responses derived from different subject samples were used to construct decision models that incorporate preference only, memory only, or both preference and memory information. (B) An algorithmic framework for interaction between memory retrieval and valuation, where the probability of choosing item i in the IMC condition, P_IMC(i), is proportional to the product of the probability of retrieving some recall set m, P(m), and the probability of choosing item i from m, P(i|m).

Fig. 3.

Modeling Semantic Retrieval and Valuation. (A) Schematic depicting retrieval as traversals on an associative semantic network from a category node (e.g., Fast Food Chains) to associated nodes consisting of eligible items. (B) Recall probability and recall order in a holdout semantic fluency sample were well-captured by the semantic network model (Left), with out-of-sample (OOS) $R^2$ ranging from 0.90 to 0.96 across tested categories (Right). Coloring of the data points follows the same assignment as in panel D below, and the multiple data points for the same item represent the cumulative recall probabilities at different recall positions. (C) Valuation processes in the EMC condition were modeled according to a multinomial logit choice rule. (D) Participant choices in the holdout sample were well-captured by the logit model (Left), with OOS $R^2$ ranging from 0.89 to 0.98 (Right).

Fig. 4.

Predicting IMC. (A) Prediction of IMC shares in the Fast Food Chains category using the valuation model calibrated using EMC behavior (out-of-sample [OOS] $R^2=0.45$) and (B) the memory retrieval model calibrated using semantic fluency responses (OOS $R^2=0.81$). (C) Fast Food Chains IMC shares were better predicted by the RCV model capturing interaction of memory and valuation (OOS $R^2=0.96$). Vertical error bars in A through C indicate 95% confidence intervals of the observed IMC shares. Horizontal error bars indicate 95% confidence intervals of the predicted IMC shares obtained through a bootstrap procedure. (D) Across all categories tested, the retrieval-constrained model demonstrated consistently high accuracy in OOS predictions of IMC behavior and significantly outperformed both memory-only and valuation-only models. Abbreviations for categories are identical to Fig. 1E.

Fig. 5.

Neural Substrates of IMC. (A) Compared to semantic fluency, IMC elicited greater activity in a priori valuation regions (vmPFC: ventromedial prefrontal cortex, PCC: posterior cingulate). Compared to EMC, IMC elicited greater activity in a priori semantic retrieval regions (aPFC: anterior prefrontal cortex; IFG, inferior frontal gyrus; aINS, anterior insula; dACC, dorsal anterior cingulate cortex; dmPFC, dorsomedial prefrontal cortex). (B) Functional connectivity between valuation (vmPFC) and retrieval (aPFC) regions was significantly stronger in IMC, while in EMC, vmPFC was more strongly connected with fusiform gyrus (visual processing).