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Using dynamic monitoring of choices to predict and understand risk preferences.

ABSTRACT: Navigating conflict is integral to decision-making, serving a central role both in the subjective experience of choice as well as contemporary theories of how we choose. However, the lack of a sensitive, accessible, and interpretable metric of conflict has led researchers to focus on choice itself rather than how individuals arrive at that choice. Using mouse-tracking-continuously sampling computer mouse location as participants decide-we demonstrate the theoretical and practical uses of dynamic assessments of choice from decision onset through conclusion. Specifically, we use mouse tracking to index conflict, quantified by the relative directness to the chosen option, in a domain for which conflict is integral: decisions involving risk. In deciding whether to accept risk, decision makers must integrate gains, losses, status quos, and outcome probabilities, a process that inevitably involves conflict. Across three preregistered studies, we tracked participants' motor movements while they decided whether to accept or reject gambles. Our results show that 1) mouse-tracking metrics of conflict sensitively detect differences in the subjective value of risky versus certain options; 2) these metrics of conflict strongly predict participants' risk preferences (loss aversion and decreasing marginal utility), even on a single-trial level; 3) these mouse-tracking metrics outperform participants' reaction times in predicting risk preferences; and 4) manipulating risk preferences via a broad versus narrow bracketing manipulation influences conflict as indexed by mouse tracking. Together, these results highlight the importance of measuring conflict during risky choice and demonstrate the usefulness of mouse tracking as a tool to do so.

SUBMITTER: Stillman PE

PROVIDER: S-EPMC7749332 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCongenital heart defects are a leading cause of morbidity and mortality in children, and despite advanced surgical treatments, many patients progress to heart failure. Currently, transplantation is the only effective cure and is limited by donor availability and organ rejection. Recently, cell therapy has emerged as a novel method for treating pediatric heart failure with several ongoing clinical trials. However, efficacy of stem cell therapy is variable, and choosing stem cells with the highest reparative effects has been a challenge.MethodsWe previously demonstrated the age-dependent reparative effects of human c-kit+ progenitor cells (hCPCs) in a rat model of juvenile heart failure. Using a small subset of patient samples, computational modeling analysis showed that regression models could be made linking sequencing data to phenotypic outcomes. In the current study, we used a similar quantitative model to determine whether predictions can be made in a larger population of patients and validated the model using neonatal hCPCs. We performed RNA sequencing from c-kit+ progenitor cells isolated from 32 patients, including 8 neonatal samples. We tested 2 functional parameters of our model, cellular proliferation and chemotactic potential of conditioned media.ResultsInterestingly, the observed proliferation and migration responses in each of the selected neonatal hCPC lines matched their predicted counterparts. We then performed canonical pathway analysis to determine potential mechanistic signals that regulated hCPC performance and identified several immune response genes that correlated with performance. ELISA analysis confirmed the presence of selected cytokines in good performing hCPCs and provided many more signals to further validate.ConclusionsThese data show that cell behavior may be predicted using large datasets like RNA sequencing and that we may be able to identify patients whose c-kit+ progenitor cells exceed or underperform expectations. With systems biology approaches, interventions can be tailored to improve cell therapy or mimic the qualities of reparative cells.

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Using dynamic monitoring of choices to predict and understand risk preferences.

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