Optimization of time and effort in decision and action: experiments meet theory

Host laboratory and collaborators

Laurent Raymond / CPT /

David Robbe / INMED /

Fabrice Sarlegna / Institut Science du Mouvement /


Imagine that you need to catch a bus to return home after work. Will you decide to run as fast as possible to catch the first incoming bus? Or maybe you are tired and will prefer to walk slowly to save your energy, at the risk of missing this first bus and arriving late at home? This example illustrates that our daily decisions and actions involve some kind of economic computation in which we adjust effort to obtain reward (here arriving comfortably at home) sooner than later. While the impact of reward on decision making and its underlying neural bases have been extensively studied, much less is known regarding how effort and time affect reward-related decisions and movements. The goal of this proposal is to take advantage of the control theory to better understand how rodents and humans optimize their decisions and the kinematics of their movement according to their sensitivity to the cost of time and effort. Such knowledge should help to understand what conditions favor seemingly irrational human decisions.


Optimal control theory, neuroeconomics, animal behavior, human behavior


The objective of the proposal is to take advantage of the optimal control theory to develop computational models capable of explaining (and simulating) the decisions and movement dynamics of human and rodents engaged in ethological foraging tasks in which subjects try to maximize their reward capture rate (the sum of all rewards acquired, minus all efforts expended, divided by total time). Those models will serve to generate new experimental predictions regarding the determinants of decision and movement at the behavioural and neural levels.

Proposed approach (experimental / theoretical / computational)

The proposal will be primarily based on ongoing experiments in which rats are engaged in a foraging task.  Briefly, thirsty rats have to decide whether, to obtain water, they spend effort to run back-and-forth between two distant water sources or they remain in front of one of them. In this experiment, the rate of water deliveries at the two sources, their temporal relation and the physical distance between the sources are manipulated. The goal of this proposal will be to understand computationally what determines the strategy of each animal. This will be addressed within the  optimal control framework where the challenge is to uncover what optimality criterion underlies a system’s behavior. For instance, optimality will differ for an animal highly sensitive to the cost of time (i.e., impulsive) compared to an animal sensitive to effort. In a second step, the validity of the computational models explaining acquired data will be tested in new foraging experiments in rodents but also in human.


The question of the mechanisms generating adaptive decisions and movements is often addressed from the point of view of neuroscience. Clearly, brains are needed to take decisions and move. But the principles governing movements and decisions might be more efficiently discovered through theoretical approaches, by considering behavior control from the point of view of physical sciences, economics and in terms of optimality. Such approach requires careful observation of the biomechanics of the body and the decision taken by subject (human/animal), under various and well-controlled experimental conditions, and their abstraction in mathematical models. Once the main determinants of behavior have been determined and included in a model, then it is fruitful to test the model robustness in different experimental conditions (new tasks in rodents, validity in human) and, finally, examine its implementation in the brain. Thus, this proposal will require interaction between behavioral science, physics and neuroscience.

Expected profile

We are looking for a candidate with a strong background in physics and ideally a good knowledge of optimal control theory. An interest in the study of animal behavior and machine learning will be a plus.