Runs several simulations and returns correlative effect sizes between the frequency/total duration/single duration of each pattern and the output activation of the network for each pattern, respectively. Comparable to running an empirical experiment in judgments of frequency and duration and analyzing the data.
run_exp(
frequency,
duration,
lrate_onset,
lrate_drop_time,
lrate_drop_perc,
patterns = diag(length(duration)),
number_of_participants = 100,
cor_noise_sd = 0
)
presentation frequency for each pattern in the matrix
presentation duration for each pattern in the matrix
learning rate at the onset of a stimulus
point at which the learning rate drops, must be lower than duration
how much the learning rate drops at lrate_drop_time
matrix with input patterns, one row is one pattern
corresponds with number of simulations run
the amount of noise added to the final activations of the network, set to 0 if you do not want any noise
data frame with three columns: f_dv, td_dv, t_dv which are the correlations between the frequency/total duration/single duration of each pattern and the activation of the network for each pattern, respectively.
# NOT RUN {
run_exp(10:1, 1:10, 0.05, 2, 0.2)
# }
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