Abstract
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.
Highlights
It has been known for long that physical effort appears to bear an inherent cost both in humans and other animals (Hull, 1943; Walton et al, 2006)
We divided the participants according to three behavioral categories, based on their strategies. This is followed by formal Bayesian model comparison to identify the best among eight different models, which differ in terms of how forward planning computes the subjective value of reward, and which out of two discount functions is used
We designed a sequential decision-making task in which participants could choose, in each trial, to exert mental effort in order to improve their chances of obtaining reward at the end of a mini-block of ten trials
Summary
It has been known for long that physical effort appears to bear an inherent cost both in humans and other animals (Hull, 1943; Walton et al, 2006). See (Talmi and Pine, 2012; Klein-Flügge et al, 2015; Białaszek et al, 2017) for comparisons between these different models While these studies established a mathematical description of how required effort affects the valuation of a reward, the experiments were typically constrained to the particular case where the decision to invest effort to obtain reward must be made immediately. All possible courses of actions (do the effort early or late) have their advantages and disadvantages and put individuals into a decision dilemma We believe that this dilemma is central to the meta-control question of how effort discounts potential reward because the dilemma emerges typically when one is pursuing goals that cannot be obtained but only after some extended time (Goschke, 2014). We present a computational-experimental approach, in the form of a novel experimental task and a sequential decision-making model, that enables future studies into the effects of pursuing long-term goals based on moment-by-moment decisions about effort investment in human participants
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