Contrary to previous reports, in both experiments effort costs devalued reward in a manner opposite to delay, with small devaluations for lower efforts, and progressively larger devaluations for higher effort-levels (concave shape).

More generally, data from two tasks in which effort costs were unconfounded from delay costs provide robust evidence against the notion that effort discounting is best explained by a hyperbolic function as previously suggested [29,38,39].

Critically, we note that previous studies did not directly compare the performance of the hyperbolic or linear model to any alternative models, and did not dissociate choices involving delay and effort costs .

Thus, effort costs were uncon-founded from delay costs.

This ensured that effort costs were unconfounded from delay costs.

The most commonly applied models of effort discounting assume that effort costs decrease reward value either linearly [31,37] or hyperbolically [29,38,39] , and thus in the same way as delay (although see [40]).

Indeed, several theoretical arguments suggest that individuals may incorporate effort costs into their decisions in a different way to delay costs.

Finally, from a psychological perspective, it has been argued that the two costs differ fundamentally with regard to what the cost is ascribed to: effort costs are ascribed to actions, whereas delay costs (when not caused by movement times) are ascribed to outcomes [43, but see 44,45].

We conducted a control analysis to test whether this nonlinear variability increase in the produced force could explain the concave shape of discounting observed for effort costs .

Thus, effort discounting is concave independent of whether participant’s evaluation relies on the required or the predicted produced effort cost .

It combines dynamic neural field theory with stochastic optimal control theory, and includes circuitry for perception, eXpected reward, effort cost and decision-making.

It is comprised of a series of dynamic neural fields (DNFs) that simulate the neural processes underlying motor plan formation, expected reward and effort cost .

It builds on successful models in dynamic neural field theory [25] and stochastic optimal control theory [26] and includes circuitry for perception, expected reward, selection bias, decision-making and effort cost .

The basic architecture of the framework is a set of dynamic neural fields (DNFs) that capture the neural processes underlying cue perception, motor plan formation, valuation of goods (e.g., eXpected reward/punishment, social reward, selection bias, cognitive bias) and valuation of actions (e.g., effort cost , precision required), Fig.

Together with an effort cost I“, the overall expected endpoint cost is then given by a sum of the accuracy costs associated with each target, weighted by their beliefs (Equation 9).

In addition to this accuracy cost, we assume an effort cost Iu that penalizes large motor commands.

Following standard approaches [20], however, we assume that this effort cost is a quadratic function of the overall sequence of motor commands:

According to the optimal feedback control hypothesis [20], the motor system selects motor commands at that minimize the sum of accuracy and effort costs: