| Type: | Package |
| Title: | Probability Associator Time (PASS-T) |
| Version: | 0.1.3 |
| Description: | Simulates judgments of frequency and duration based on the Probability Associator Time (PASS-T) model. PASS-T is a memory model based on a simple competitive artificial neural network. It can imitate human judgments of frequency and duration, which have been extensively studied in cognitive psychology (e.g. Hintzman (1970) <doi:10.1037/h0028865>, Betsch et al. (2010) https://psycnet.apa.org/record/2010-18204-003). The PASS-T model is an extension of the PASS model (Sedlmeier, 2002, ISBN:0198508638). The package provides an easy way to run simulations, which can then be compared with empirical data in human judgments of frequency and duration. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.0.2 |
| URL: | https://github.com/johannes-titz/passt |
| BugReports: | https://github.com/johannes-titz/passt/issues |
| Suggests: | knitr, ggplot2, plyr, testthat (≥ 2.1.0), covr, markdown, rmarkdown |
| VignetteBuilder: | knitr |
| Imports: | magrittr,methods,dplyr,tidyr,rlang |
| NeedsCompilation: | no |
| Packaged: | 2021-05-03 13:50:51 UTC; jt |
| Author: | Johannes Titz [aut, cre] |
| Maintainer: | Johannes Titz <johannes.titz@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2021-05-03 14:30:02 UTC |
passt: Simulates judgments of frequency and duration with a competitive learning network based on the PASS-family
Description
To run a simulation you will just need to use the function
run_sim, which will take care of the rest. To run
several simulations and get a standard analysis use the function
run_exp. Both functions take care of the details,
while you are still able to set the most important parameters such
as the learning rate development, the input patterns and the
independent variables of exposure frequency and exposure duration.
Author(s)
Maintainer: Johannes Titz johannes.titz@gmail.com
See Also
Useful links:
Pipe operator
Description
See magrittr::%>% for details.
Usage
lhs %>% rhs
Run simulations and analyze data
Description
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.
Usage
run_exp(
frequency,
duration,
lrate_onset,
lrate_drop_time,
lrate_drop_perc,
patterns = diag(length(duration)),
number_of_participants = 100,
cor_noise_sd = 0
)
Arguments
frequency |
presentation frequency for each pattern in the matrix |
duration |
presentation duration for each pattern in the matrix |
lrate_onset |
learning rate at the onset of a stimulus |
lrate_drop_time |
point at which the learning rate drops, must be lower than duration |
lrate_drop_perc |
how much the learning rate drops at lrate_drop_time |
patterns |
matrix with input patterns, one row is one pattern |
number_of_participants |
corresponds with number of simulations run |
cor_noise_sd |
the amount of noise added to the final activations of the network, set to 0 if you do not want any noise |
Value
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.
See Also
Examples
run_exp(10:1, 1:10, 0.05, 2, 0.2)
Run simulations
Description
Runs several simulations and returns output activation for each simulation and each input pattern
Usage
run_sim(
patterns,
frequency,
duration,
lrate_onset,
lrate_drop_time,
lrate_drop_perc,
n_runs = 100,
n_output_units = ncol(patterns),
pulses_per_second = 1
)
Arguments
patterns |
matrix with input patterns, one row is one pattern |
frequency |
presentation frequency for each pattern in the matrix |
duration |
presentation duration for each pattern in the matrix |
lrate_onset |
learning rate at the onset of a stimulus |
lrate_drop_time |
point at which the learning rate drops, must be lower than duration |
lrate_drop_perc |
how much the learning rate drops at lrate_drop_time |
n_runs |
number of simulations to be run, default is 100 |
n_output_units |
number of output units, defaults to number of input units |
pulses_per_second |
how many time steps should be simulated per second |
Value
list with following elements
output: the sum of the activation strengths of the output units for each input pattern
weight_matrix: final weight_matrix
pres_matrix: presentation matrix
See Also
Examples
run_sim(diag(10), 1:10, 10:1, 0.05, 2, 0.2)