generate_responses.RdCreates an N-individuals by T-trials matrix of press-right decisions and resulting outcomes. It takes task definitions, as well as individually varying parameters for each parameter governing the reinforcement learning algorithm.
generate_responses(N, M, K, mm, Tsubj, cue, n_cues, condition, outcome, beta_xi, beta_b, beta_eps, beta_rho, press_right = NULL)
| N | number of individuals |
|---|---|
| M | number of samples |
| K | number of conditions |
| mm | a vector of group labels (1:M) for each individual from 1:N |
| Tsubj | a vector of trial counts for each individual |
| cue | an N by max(Tsubj) matrix of integers in 1:n_cues identifying the cue displayed on each trial |
| n_cues | total number of unique cues |
| condition | an N by max(Tsubj) matrix of integers in 1:K identifying the condition for each trial |
| outcome | an N by max(Tsubj) by 2 array of real numbers identifying the outcome reward if the individual chooses left (outcome[,,1]) or right (outcome[,,2]) |
| beta_xi | an N by K matrix of coefficients governing the amount of irreducible noise |
| beta_b | an N by K matrix of coefficients governing the amount of press-right bias |
| beta_eps | an N by K matrix of coefficients governing the learning rate |
| beta_rho | an N by K matrix of coefficients adjusting the relative amount of reward |
| press_right | an N by max(Tsubj) matrix of press-right responses (either 0 or 1). If supplied, this is used in place of draws from the binomial distribution. Useful if one wants to retrieve expected probabilities of pressing right for a given set of model paramters and response patterns. Defaults to NULL. |
a list with two N by max(Tsubj) matrices (with NA in unused trial
cells). The first matrix, press_right contains 0 if the decision was
to press left, and 1 if the decision was to press right. The second matrix,
outcome_realized contains the amount of the feedback received after
the press. The third matrix, p_press_right, contains the trial-by-trial
probability of a right-key press.