using_time_models
In calmr: Canonical Associative Learning Models and their Representations

options(width = 300)

```{R, include = FALSE} knitr::opts_chunk$set( fig.width = 7, collapse = TRUE, comment = "#>", message = FALSE, warning = FALSE )

## Time models in calmr

Version 0.5 of `calmr` introduced its first time-based model, 
ANCCR [@jeong_mesolimbic_2022], and with it, 
I wrote several additional tools for future time-based models.

#### Changes to trial-based models

The biggest change in `calmr` version 0.5 is the use of the ">" character and its effect on 
trial-based models. With the advent of time-based models, some generalizations had to be made 
to enable those models to update across adjacent trial periods. You can learn more about this 
in the [directional_models](directional_models.html) vignette.

#### Specifying a design for time-based models

The designs for time-based models are nearly identical to those for trial-based models. 
However, clever use of the ">" character will
enrich them. Let's specify a serial feature discrimination experiment:

```r
library(calmr)
fpfn <- data.frame(
  group = c("FP", "FN"),
  phase1 = c("!100F>T>(US)/100T", "!100F>T/100T>(US)")
)
parse_design(fpfn)

We can manually specify the timing for the above experiment by calling the get_timings() function. Manipulating the list returned by that function will result in a manipulation of the timing between the experimental events.

ts <- get_timings(fpfn, model = "ANCCR")
ts

And now let's get the parameters for the ANCCR model.

pars <- get_parameters(fpfn, model = "ANCCR")
# increase learning rates
pars$alpha_reward <- 0.8
pars$alpha <- 0.08
# increase sampling interval to speed up the model
pars$sampling_interval <- 5
pars

Let's make the model's experience and look at the first 20 entries.

experiment <- make_experiment(fpfn,
  parameters = pars,
  timings = ts,
  model = "ANCCR"
)
head(experiences(experiment)[[1]], 20)

As you can see above, there are several rows per trial, each specifying a different stimulus. Time-based models like ANCCR run over a time log because they make ample use of the temporal difference between events.

Let's run the model and see some plots.

experiment <- run_experiment(experiment)

# Action values
patch_plots(plot(experiment, type = "action_values"))
# ANCCR
patch_plots(plot(experiment, type = "anccrs"))
# Dopamine transients
patch_plots(plot(experiment, type = "dopamines"))