| Bayesian parameter estimation and model comparison | Bayesian parameter estimation and model comparison |
| Bayesian parameter estimation and model comparison | To maximize our chances to find global, rather than local maxima using the gradient ascent algorithm, parameter estimation was repeated over a grid of initialization values. |
| Experimental task and procedure | The reward magnitude associated with each force level was adjusted on a trial-by-trial basis using an adaptive staircase algorithm, independently for each effort level ( Parameter Estimation by Sequential Testing, PEST, see [91]). |
| Results are not trivially explained by a larger number of model parameters, the exerted force, or fatigue | Bayesian parameter estimation and model comparison were performed as before, but using the force level produced on a given trial instead of the required target force. |
| Supporting Information | The supplementary methods and results report an analysis of response time and choice based on simple regression analyses, and include additional tables reporting model parameter estimates , accuracy and complexity terms, and results of control analyses. |
| Supporting Information | B, Mean (i SEM) parameter estimates from a logistic regression analysis of each participant’s choice pattern. |
| Supporting Information | Individual parameter estimates (Experiment 1). |
| Using utility instead of reward magnitude | Parameter estimation and model comparisons were repeated for all models using this generic measure of utility. |
| Inferring functional connectivity from spikes | Here we use L1-regularization and find the maximum a posteriori (MAP) parameters estimates |
| U | Gray lines show parameters estimated from bootstrap samples; solid colored lines show their averages. |
| input experiments. | Since the bilinear model has many more parameters, it is not unsurprising that there is more uncertainty in the parameter estimates given the same amount of data (200s in this case). |