Overview

Code 
library(tidyverse)
library(targets)
library(GGally)
library(kableExtra)
# load "R/*" scripts and saved R objects from the targets pi
tar_source()
tar_load(c(exp1_data_agg_enc, exp2_data_agg_enc, fits3))

Here I employ the same model as in Model 1 with one difference. In the previous model, the encoding time was not included. It assumed that during encoding of the triplets (0.9 seconds), nothing happened, and recovery of resources only started after the encoding was completed. In Popov & Reder (2020), we assumed that depletion happens instantaneously, and recovery continuous thoughout the encoding and inter-stimulus interval.

So here we just add the 0.9 seconds of encoding time to the ISI. For example, this is coded in the ISI column:

Code 
exp1_data_agg_enc |>
  kbl() |>
  kable_styling()
chunk gap itemtype n_total n_correct p_correct ISI item_in_ltm
known 500 SP1-3 1855 1715 0.9245283 1.4 TRUE
known 500 SP4-6 1858 1368 0.7362756 1.4 FALSE
known 500 SP7-9 1851 943 0.5094543 1.4 FALSE
known 3000 SP1-3 1859 1721 0.9257665 3.9 TRUE
known 3000 SP4-6 1858 1443 0.7766416 1.4 FALSE
known 3000 SP7-9 1856 1032 0.5560345 1.4 FALSE
random 500 SP1-3 1853 1495 0.8067998 1.4 FALSE
random 500 SP4-6 1856 1177 0.6341595 1.4 FALSE
random 500 SP7-9 1855 727 0.3919137 1.4 FALSE
random 3000 SP1-3 1856 1553 0.8367457 3.9 FALSE
random 3000 SP4-6 1858 1287 0.6926803 1.4 FALSE
random 3000 SP7-9 1856 838 0.4515086 1.4 FALSE
Code 
# calculate deviance and predictions
fits3 <- fits3 |>
  mutate(
    deviance = pmap_dbl(
      list(fit, data, exclude_sp1),
      ~ overall_deviance(params = `..1`$par, data = `..2`, exclude_sp1 = `..3`)
    ),
    pred = map2(fit, data, ~ predict(.x, .y, group_by = c("chunk", "gap")))
  )

(just like in Model 1, I fit the data by either excluding the first serial position or not, and including priors on the gain and rate parameters or not).

Overall best parameters by experiment and scenarious

These are the best fitting parameters for each experiment, prior scenario, and whether the first serial position was excluded or not:

Code 
final3 <- fits3 |>
  filter(convergence == 0) |>
  group_by(exp, priors_scenario, exclude_sp1) |>
  arrange(deviance) |>
  slice(1) |>
  arrange(desc(exclude_sp1), exp, priors_scenario) |>
  mutate(
    deviance = round(deviance, 1),
    priors_scenario = case_when(
      priors_scenario == "none" ~ "None",
      priors_scenario == "gain" ~ "Gain ~ N(25, 0.1)",
      priors_scenario == "rate" ~ "Rate ~ N(0.1, 0.01)"
    )
  )

final3 |>
  select(exp, priors_scenario, exclude_sp1, prop:gain, deviance) |>
  mutate_all(round, 3) |>
  kbl() |>
  kable_styling()
exp priors_scenario exclude_sp1 prop prop_ltm rate tau gain deviance
1 Gain ~ N(25, 0.1) TRUE 0.231 0.617 0.018 0.162 25.001 33.1
1 None TRUE 0.110 0.606 0.009 0.094 99.996 31.9
1 Rate ~ N(0.1, 0.01) TRUE 0.435 0.646 0.043 0.222 8.364 42.4
2 Gain ~ N(25, 0.1) TRUE 0.211 0.834 0.013 0.153 25.000 40.7
2 None TRUE 0.106 0.826 0.006 0.091 91.036 40.5
2 Rate ~ N(0.1, 0.01) TRUE 0.473 0.855 0.030 0.212 6.286 46.5
1 Gain ~ N(25, 0.1) FALSE 0.239 0.660 0.016 0.163 24.998 145.4
1 None FALSE 0.302 0.658 0.020 0.184 16.186 144.9
1 Rate ~ N(0.1, 0.01) FALSE 0.472 0.673 0.044 0.220 7.604 152.2
2 Gain ~ N(25, 0.1) FALSE 0.222 0.879 0.011 0.155 24.998 113.3
2 None FALSE 0.379 0.868 0.019 0.198 9.421 109.7
2 Rate ~ N(0.1, 0.01) FALSE 0.497 0.865 0.030 0.209 5.950 113.4

for comparison, here are the results of Model 1

Model 1 Results

Doesn’t make much of a difference. Do not pursue further.

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