Dopamine and serotonin in human substantia nigra track social context and value signals during economic exchange

Dopamine and serotonin are hypothesized to guide social behaviours. In humans, however, we have not yet been able to study neuromodulator dynamics as social interaction unfolds. Here, we obtained subsecond estimates of dopamine and serotonin from human substantia nigra pars reticulata during the ultimatum game. Participants, who were patients with Parkinson’s disease undergoing awake brain surgery, had to accept or reject monetary offers of varying fairness from human and computer players. They rejected more offers in the human than the computer condition, an effect of social context associated with higher overall levels of dopamine but not serotonin. Regardless of the social context, relative changes in dopamine tracked trial-by-trial changes in offer value—akin to reward prediction errors—whereas serotonin tracked the current offer value. These results show that dopamine and serotonin fluctuations in one of the basal ganglia’s main output structures reflect distinct social context and value signals

Analysis of adding-runs strategy for peak-hour regular bus services

This paper proposes an adding-runs strategy to alleviate in-vehicle crowding for peak-hour bus services. Passengers’ departure time choices under user equilibrium and system optimum conditions are investigated with and without adding-runs strategy. A bi-level programming model is developed to determine the optimal adding-runs strategy. An artificial bee colony algorithm is adopted to solve the proposed bi-level problem. Numerical examples show that the adding-runs strategy is effective in alleviating crowding effects and reducing schedule delay in peak-hour bus services. The total system cost can be reduced by more than 8% with the optimal adding-runs strategy

Undoing in human planning

From writing to hiking, people’s real-world sequential decision-making often benefits from “undoing” (e.g. deleting sentences or backtracking). Surprisingly, undoing has not been studied in experiments on human planning. To investigate how much, when, and why people undo, we introduce a task that is a cross between the “Traveling Salesperson” and the “Knapsack” problems with an undo option. Within a length budget, subjects sequentially connect as many dots as possible on a map. On “undo” trials, they are allowed to take back actions without constraints. We find that undoing is beneficial, that subjects exhibit great individual variability in the number of undos, that undos are more frequent after errors than after correct actions, and that long response times tend to precede sequences of undos. Together, these results suggest that undo actions serve a dual role of correcting errors and of exploring alternative paths, where path evaluation benefits from full play-outs

Distance-Based Congestion Pricing with Day-to-Day Dynamic Traffic Flow Evolution Process

This paper studies the distance-based congestion pricing in a network considering the day-to-day dynamic traffic flow evolution process. It is well known that, after an implementation or adjustment of a new congestion toll scheme, the network environment will change and traffic flows will be nonequilibrium in the following days; thus it is not suitable to take the equilibrium-based indexes as the objective of the congestion toll. In the context of nonequilibrium state, prior research proposed a mini–max regret model to solve the distance-based congestion pricing problem in a network considering day-to-day dynamics. However, it is computationally demanding due to the calculation of minimal total travel cost for each day among the whole planning horizon. Therefore, in order to overcome the expensive computational burden problem and make the robust toll scheme more practical, we propose a new robust optimization model in this paper. The essence of this model, which is an extension of our prior work, is to optimize the worst condition among the whole planning period and ameliorate severe traffic congestions in some bad days. Firstly, a piecewise linear function is adopted to formulate the nonlinear distance toll, which can be encapsulated to a day-to-day dynamics context. A very clear and concise model named logit-type Markov adaptive learning model is then proposed to depict commuters’ day-to-day route choice behaviors. Finally, a robust optimization model which minimizes the maximum total travel cost among the whole planning horizon is formulated and a modified artificial bee colony algorithm is developed for the robust optimization model

Ma Lab Resources

This is the overview project for the resources from Weiji Ma's lab at NYU