Quantifying the contribution of individual variation in timing to delay-discounting

Lukinova, Evgeniya; Erlich, Jeffrey C

doi:10.1038/s41598-021-97496-w

Cited by 6 publications

(6 citation statements)

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“…delay perceptions. This is in line with studies indicating that time perception plays a significant role in delay discounting [ (103)(104)(105)(106)(107); see also (108)]. This scaling parameter s, as well as the discount parameter κ, moreover differed significantly between the reward and the loss condition.…”

Section: Discussionsupporting

confidence: 90%

A Model Guided Approach to Evoke Homogeneous Behavior During Temporal Reward and Loss Discounting

et al. 2022

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BackgroundThe tendency to devaluate future options as a function of time, known as delay discounting, is associated with various factors such as psychiatric illness and personality. Under identical experimental conditions, individuals may therefore strongly differ in the degree to which they discount future options. In delay discounting tasks, this inter-individual variability inevitably results in an unequal number of discounted trials per subject, generating difficulties in linking delay discounting to psychophysiological and neural correlates. Many studies have therefore focused on assessing delay discounting adaptively. Here, we extend these approaches by developing an adaptive paradigm which aims at inducing more comparable and homogeneous discounting frequencies across participants on a dimensional scale.MethodThe proposed approach probabilistically links a (common) discounting function to behavior to obtain a probabilistic model, and then exploits the model to obtain a formal condition which defines how to construe experimental trials so as to induce any desired discounting probability. We first infer subject-level models on behavior on a non-adaptive delay discounting task and then use these models to generate adaptive trials designed to evoke graded relative discounting frequencies of 0.3, 0.5, and 0.7 in each participant. We further compare and evaluate common models in the field through out-of-sample prediction error estimates, to iteratively improve the trial-generating model and paradigm.ResultsThe developed paradigm successfully increases discounting behavior during both reward and loss discounting. Moreover, it evokes graded relative choice frequencies in line with model-based expectations (i.e., 0.3, 0.5, and 0.7) suggesting that we can successfully homogenize behavior. Our model comparison analyses indicate that hyperboloid models are superior in predicting unseen discounting behavior to more conventional hyperbolic and exponential models. We report out-of-sample error estimates as well as commonalities and differences between reward and loss discounting, demonstrating for instance lower discounting rates, as well as differences in delay perception in loss discounting.ConclusionThe present work proposes a model-based framework to evoke graded responses linked to cognitive function at a single subject level. Such a framework may be used in the future to measure cognitive functions on a dimensional rather than dichotomous scale.

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Section: Discussionsupporting

confidence: 90%

A Model Guided Approach to Evoke Homogeneous Behavior During Temporal Reward and Loss Discounting

et al. 2022

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“…We confirmed that in the general population discount factors across time-horizons were highly related as in the students population in our studies before (Lukinova et al, 2019; Lukinova and Erlich, 2021). In this paper, however, we improved our modeling.…”

Section: Discussionsupporting

confidence: 90%

“…We find that time preferences are stable across time-horizons: between days and seconds, r = 0.46 and between days and weeks, r = 0.96 (Figure 2A-D). Therefore, we are able to replicate our previous results (Lukinova et al, 2019; Lukinova and Erlich, 2021) in a nonstudent population. We also report the between tasks and between sessions Pearson correlations of subjects’ discount factors (log( k )) in Table 1 for all models under consideration.…”

Section: Resultssupporting

confidence: 84%

“…In our recent studies, we found that students’ delay discounting for offers in seconds and days was strongly correlated and stable (Lukinova et al, 2019; Lukinova and Erlich, 2021). This longitudinal within-subject study builds on our previous results about stability of time preferences and explores a biological basis for changes in time preferences over time that may be due to individual responses to the environment (e.g., stressful negative income shocks or non-economic stressors, like divorce, health problems, etc.)…”

Section: Introductionmentioning

confidence: 96%

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Willingness to wait covaries with endogenous variation in cortisol

Lukinova

Erlich

2021

Preprint

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It has been argued that one dimension of the cycle of poverty is that poverty is a state of chronic stress and that chronic stress impairs decision-making. These poor decisions, made under chronic stress, might include carrying high-interest loans, failure to buy health insurance, gambling or drug use. As such, these decisions can contribute to the cycle of poverty. More specifically, a few studies suggest that increased stress may lead to more risk-aversion and steeper delay-discounting. While the deleterious effects of chronic stress on brain function are well established, much less is known about how chronic stress influences financial decision making specifically. Here, in a longitudinal design within six weeks period we aimed to incorporate biological mechanisms to improve our understanding of how stress influences economic decisions. We used a combination of decision-making tasks, questionnaires, saliva and hair samples within-subject (N=41). We assessed time and risk preferences using hierarchical Bayesian techniques to both pool data and allow heterogeneity in decision making and compared those to cortisol levels and self-reported stress. We found only weak links between endogenous variation in stress and model-based estimates of risk and time preferences. In particular, we found that fluctuations in the stress level measured via hair sample were not only positively correlated with time preferences in the short delay task and risk preferences, but also the decision noise in the risk task. However, relationships for the risk task disappeared when an outlier was removed. Also, we found model-free task measures in the short delay task to be moderately related to both hair cortisol as well as the stressful life events questionnaire measure. For example, we observed that endogenous stress fluctuations and the life change units were negatively correlated with the proportion of later choices. Finally, we established that for the reaction times the curvilinear relationship was preferred to the linear one for those with increase in biological stress level compared to the baseline: when cortisol level increased slightly, participants decided slower, but when stress increased to higher levels, they decided quicker.

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“…An important but unaddressed issue is the cognitive components underlying delay discounting. Delay discounting consists of a variety of cognitive components, such as reward valuation, time perception, conflict detection, and cognitive control ( Wittmann and Paulus, 2008 ; Claus et al, 2011 ; Frost and McNaughton, 2017 ; Dennis et al, 2020 ; Lukinova and Erlich, 2021 ). However, the behavioral index of the delay discounting used in our study ( k value) was not able to be further decomposed into these cognitive components.…”

Section: Discussionmentioning

confidence: 99%

The Resting-State Neural Network of Delay Discounting

Yang

2022

Front. Psychol.

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Delay discounting is a common phenomenon in daily life, which refers to the subjective value of a future reward decreasing as a function of time. Previous studies have identified several cortical regions involved in delay discounting, but the neural network constructed by the cortical regions of delay discounting is less clear. In this study, we employed resting-state functional magnetic resonance imaging (RS-fMRI) to measure the spontaneous neural activity in a large sample of healthy young adults and used the Monetary Choice Questionnaire to directly measure participants’ level of delay discounting. To identify the neural network of delay discounting at rest, we used an individual difference approach to explore brain regions whose spontaneous activities were related to delay discounting across the whole brain. Then, these brain regions served as seeds to identify the neural network of delay discounting. We found that the fractional amplitude of low-frequency fluctuations (fALFF) of the left insula were positively correlated to delay discounting. More importantly, its connectivity to the anterior cingulate cortex was read out for participants’ behavioral performance in the task of delay discounting. In short, our study provides empirical evidence that insula-anterior cingulate cortex connectivity may serve as a part of the neural network for delay discounting.

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Quantifying the contribution of individual variation in timing to delay-discounting

Cited by 6 publications

References 55 publications

A Model Guided Approach to Evoke Homogeneous Behavior During Temporal Reward and Loss Discounting

A Model Guided Approach to Evoke Homogeneous Behavior During Temporal Reward and Loss Discounting

Willingness to wait covaries with endogenous variation in cortisol

The Resting-State Neural Network of Delay Discounting

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