Abstract
The effort required to obtain a rewarding outcome is an important factor in decision-making. Describing the reward devaluation by increasing effort intensity is substantial to understanding human preferences, because every action and choice that we make is in itself effortful. To investigate how reward valuation is affected by physical and cognitive effort, we compared mathematical discounting functions derived from research on discounting. Seven discounting models were tested across three different reward magnitudes. To test the models, data were collected from a total of 114 participants recruited from the general population. For one-parameter models (hyperbolic, exponential, and parabolic), the data were explained best by the exponential model as given by a percentage of explained variance. However, after introducing an additional parameter, data obtained in the cognitive and physical effort conditions were best described by the power function model. Further analysis, using the second order Akaike and Bayesian Information Criteria, which account for model complexity, allowed us to identify the best model among all tested. We found that the power function best described the data, which corresponds to conventional analyses based on the R2 measure. This supports the conclusion that the function best describing reward devaluation by physical and cognitive effort is a concave one and is different from those that describe delay or probability discounting. In addition, consistent magnitude effects were observed that correspond to those in delay discounting research.
Highlights
Choosing between two rewarding outcomes in day-to-day life is often not an easy feat, even more when the multiple types of costs that impact the subjective value of a particular rewarding outcome are considered
To be able to identify the single best model, which is impossible when using only R2 measure, we used AICc and Bayesian Information Criterion (BIC) that account for model complexity
The power function model performed better 69.3% and tied 5.3% of the time (79 and 6 cases, respectively) when compared to Rachlin’s model (Z = 5.036; p< .001; r = .33); and performed better 82.5% and tied 5.3% of the time (94 and 6 cases, respectively) in comparison to Myerson and Green’s model (Z = 7.451; p< .001; r = .49)
Summary
Choosing between two rewarding outcomes in day-to-day life is often not an easy feat, even more when the multiple types of costs that impact the subjective value of a particular rewarding outcome are considered. There are multiple factors influencing such decision: delay of consequences and their probabilistic nature, and effort that is exerted to maintain our preferences. Physical and cognitive effort discounting across different reward magnitudes probability of obtaining it [1]. Substantial research from the fields of biology, neuroeconomics, behavioral economics, and psychology has been made to understand and model reward discounting by delay and probability, defined as the devaluation of a reward as the delay or uncertainty associated with obtaining that reward increases [2]
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