Can Delay Probability Discounting Be Explained By Delay And Probability Discounting? - Info and Reading Options

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People often face choices with delays and uncertain outcomes - when this happens, the outcome loses some of its perceived value and it may become preferable to choose a smaller but immediate and/or certain reward. This devaluation is called discounting and it has been extensively studied separately for delays (Furrebøe, 2023; Bailey et al., 2021) and probabilities (Kyonka and Schutte, 2018; Bruce et al., 2016), but less so for their combination. However, life choices typically involve both latency and uncertainty. For example, a person might spend their money to buy something (which would be an immediate, certain reward) or invest it to have the possibility of the investment paying off after some time. In the case of substance use, the choice lies between an immediate certain pleasurable experience and a potentially better health and social life later. Thus, both types of discounting are part of the cognitive process that shapes our everyday decision-making. What remains unclear to date is whether choices which integrate probability discounting (PD) and delay discounting (DD), commonly referred to as delay probability discounting (DPD) processes (e.g., Bialaszek et al., 2020), can be entirely explained by PD and DD processes alone, or whether new phenomena emerge through their integration (i.e., DPD is more than just the multiplication of its parts). While it is mostly assumed that DPD is indeed simply a product of PD and DD, some evidence indicates that this may not be the case (Cox & Dallery, 2016). In an own pilot DPD study, we observed that inferred DD parameters were larger in a DPD than in a pure DD task, while the opposite was found for PD parameters. Crucially, the discounting parameters between DPD and the pure DD and PD tasks were only moderately correlated, suggesting that DD processes are differentially engaged when evaluating future probabilistic outcomes. However, these results require further replication. If confirmed, they would indicate that integrating all relevant processes into one decision-making model and task is imperative to validly study DPD. In turn, such results would have immediate consequences for studying pathological decision making processes and biomarkers of substance use disorder (SUD), which commonly involve both PD and DD. Here, we therefore explicitly test the hypothesis whether DPD is qualitatively distinct from PD and DD alone, with an experimental design explicitly tailored to this question. Addressing this question is not simple, given the high interindividual variability in discounting processes across participants (Vogel and Awh, 2008; Lonsdorf and Merz, 2017; Escobar et al., 2023). Variability here refers to differences in the degree of discounting, making it difficult to assess and compare the psychophysiological and neural correlates of the process between participants, as well as between PD, DD, and DPD tasks. To overcome these obstacles we leverage a previously established adaptive model-based framework for homogenizing discounting behavior across participants (Thome et al., 2022; 2023). In this framework, we use behavior sampled on an initial fixed experiment to infer behavioral models which are then leveraged to generate a second adaptive experiment, inducing homogeneous behavior. Essentially, the models are used to induce the same (predetermined) discounting frequencies in all participants in the second experiment. These induced frequencies can also be understood as model predictions. We now reason that if DPD can be entirely explained by DD and PD, then behavior in DPD can be equally well predicted by behavior in isolated DD and PD trials, as compared to integrated DPD trials. We therefore perform two experiments in which these two predictions are compared and evaluated.

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