Intertemporal choice requires a dynamic interaction between valuation and deliberation processes. While evidence identifying candidate brain areas for each of these processes is well established, the precise mechanistic role carried out by each brain region is still debated. In this article, we present a computational model that clarifies the unique contribution of frontoparietal cortex regions to intertemporal decision making. The model we develop samples reward and delay information stochastically on a moment-by-moment basis. As preference for the choice alternatives evolves, dynamic inhibitory processes are executed by way of asymmetric lateral inhibition. We find that it is these lateral inhibition processes that best explain the contribution of frontoparietal regions to intertemporal decision making exhibited in our data.

Recurrent outbreaks of the coronavirus disease 2019 (COVID-19) have occurred in many countries around the world. We developed a twofold framework in this study, which is composed by one novel descriptive model to depict the recurrent global outbreaks of COVID-19 and one dynamic model to understand the intrinsic mechanisms of recurrent outbreaks. We used publicly available data of cumulative infected cases from 1 January 2020 to 2 January 2021 in 30 provinces in China and 43 other countries around the world for model validation and further analyses. These time series data could be well fitted by the new descriptive model. Through this quantitative approach, we discovered two main mechanisms that strongly correlate with the extent of the recurrent outbreak: the sudden increase in cases imported from overseas and the relaxation of local government epidemic prevention policies. The compartmental dynamical model (Susceptible, Exposed, Infectious, Dead and Recovered (SEIDR) Model) could reproduce the obvious recurrent outbreak of the epidemics and showed that both imported infected cases and the relaxation of government policies have a causal effect on the emergence of a new wave of outbreak, along with variations in the temperature index. Meanwhile, recurrent outbreaks affect consumer confidence and have a significant influence on GDP. These results support the necessity of policies such as travel bans, testing of people upon entry, and consistency of government prevention and control policies in avoiding future waves of epidemics and protecting economy.

Context effects are phenomena of multiattribute, multialternative decision-making that contradict normative models of preference. Numerous computational models have been created to explain these effects, communicated through the estimation of model parameters. Historically, parameters have been estimated by fitting these models to choice response data alone. In other contexts, such as those conventionally studied in perceptual decision-making, the times associated with choice responses have proven effective in improving understanding and testing competing theoretical accounts of various experimental manipulations. Here, we explore the advantages of incorporating response time distributions into the inference procedure, using the most recent model of context effects-the multiattribute linear ballistic accumulator (MLBA) model-as a case study. First, we establish in a simulation study that incorporating response time data in the inference procedure does indeed produce more constrained estimates of the model parameters, and the extent of this constraint is modulated by the number of observations within the data. Second, we generalize our results beyond the MLBA model by using likelihood-free techniques to estimate model parameters. Finally, we investigate parameter differences when choice or choice response time data are used to fit the MLBA model by fitting different model variants to real data from a perceptual decision-making experiment with context effects. Based on likelihood-free and likelihood-based estimations of both simulated and real data, we conclude that response time measures offer an important source of constraint for models of context effects.

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Intertemporal choice requires choosing between a smaller reward available after a shorter time delay and a larger reward available after a longer time delay. Previous studies suggest that intertemporal preferences are formed by generating a subjective value of the monetary rewards that depends on reward amount and the associated time delay. Neuroimaging results indicate that this subjective value is tracked by ventral medial prefrontal cortex (vmPFC) and ventral striatum. Subsequently, an accumulation process, subserved by a network including dorsal medial frontal cortex (dmFC), dorsal lateral prefrontal cortex (dlPFC) and posterior parietal cortex (pPC), selects a choice based on the subjective values. The mechanisms of how value accumulation interacts with subjective valuation to make a choice, and how brain regions communicate during decision making are undetermined. We developed and performed an EEG experiment that parametrically manipulated the probability of preferring delayed larger rewards. A computational model equipped with time and reward information transformation, selective attention, and stochastic value accumulation mechanisms was constructed and fit to choice and response time data using a hierarchical Bayesian approach.Phase-based functional connectivity between putative dmFC and pPC was found to be associated with stimulus processing and to resemble the reconstructed accumulation dynamics from the best performing computational model across experimental conditions. By combining computational modeling and phase-based functional connectivity, our results suggest an association between value accumulation, choice competition, and frontoparietal connectivity in intertemporal choice.Author summaryIntertemporal choice is a prominent experimental assay for impulsivity. Behavior in the task involves several cognitive functions including valuation, action selection and self-control. It is unknown how these different functions are temporally implemented during the course of decision making. In the current study, we combined formal computational models of intertemporal choice with a phase-based EEG measure of activity across brain regions to show that functional connectivity between dmFC and pPC reflects cognitive mechanisms of both visual stimulus processing and choice value accumulation. The result supports the notion that dynamic interaction between frontopatietal regions instantiates the critical value accumulation process in intertemporal choice.