Modeling Dynamic Allocation of Effort in a Sequential Task Using Discounting Models

• Main Authors: Darío Cuevas Rivera, Alexander Strobel, Thomas Goschke, Stefan J. Kiebel
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-03-01
• Series: Frontiers in Neuroscience
• Subjects: effort; discounting; sequential; computational modeling; decision making; goal-directed action
• Online Access: https://www.frontiersin.org/article/10.3389/fnins.2020.00242/full

Most rewards in our lives require effort to obtain them. It is known that effort is seen by humans as carrying an intrinsic disutility which devalues the obtainable reward. Established models for effort discounting account for this by using participant-specific discounting parameters inferred from experiments. These parameters offer only a static glance into the bigger picture of effort exertion. The mechanism underlying the dynamic changes in a participant's willingness to exert effort is still unclear and an active topic of research. Here, we modeled dynamic effort exertion as a consequence of effort- and probability-discounting mechanisms during goal reaching, sequential behavior. To do this, we developed a novel sequential decision-making task in which participants made binary choices to reach a minimum number of points. Importantly, the time points and circumstances of effort allocation were decided by participants according to their own preferences and not imposed directly by the task. Using the computational model to analyze participants' choices, we show that the dynamics of effort exertion arise from a combination of changing task needs and forward planning. In other words, the interplay between a participant's inferred discounting parameters is sufficient to explain the dynamic allocation of effort during goal reaching. Using formal model comparison, we also inferred the forward-planning strategy used by participants. The model allowed us to characterize a participant's effort exertion in terms of only a few parameters. Moreover, the model can be adapted to a number of tasks used in establishing the neural underpinnings of forward-planning behavior and meta-control, allowing for the characterization of behavior in terms of model parameters.