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Gaussian-Process Pupil Trajectories

library(gp3bayes)
sim <- simulate_advanced_pupil_timecourse(
  n_participants = 12,
  trials_per_participant = 4,
  time_points = 41,
  seed = 3020
)

Approximate GP is the default

gp32 <- create_pupil_gp_spec("matern32", "approximate", k = 30)
gp52 <- create_pupil_gp_spec("matern52", "approximate", k = 30)
gpeq <- create_pupil_gp_spec("exp_quad", "approximate", k = 30)

gp32
#> $kernel
#> [1] "matern32"
#> 
#> $basis
#> [1] "approximate"
#> 
#> $k
#> [1] 30
#> 
#> $scale
#> [1] TRUE
#> 
#> attr(,"class")
#> [1] "gp3bayes_pupil_gp_spec"
gp52
#> $kernel
#> [1] "matern52"
#> 
#> $basis
#> [1] "approximate"
#> 
#> $k
#> [1] 30
#> 
#> $scale
#> [1] TRUE
#> 
#> attr(,"class")
#> [1] "gp3bayes_pupil_gp_spec"
gpeq
#> $kernel
#> [1] "exp_quad"
#> 
#> $basis
#> [1] "approximate"
#> 
#> $k
#> [1] 30
#> 
#> $scale
#> [1] TRUE
#> 
#> attr(,"class")
#> [1] "gp3bayes_pupil_gp_spec"
spec <- specify_advanced_pupil_timecourse_model(
  sim$data,
  temporal_structure = "gaussian_process",
  gp_spec = gp32,
  family = "gaussian",
  autocorrelation = "none",
  predictive_target = "future_segment"
)

spec
#> <gp3bayes_pupil_advanced_specification>
#>   Version: 0.5.0.9000 
#>   Family: gaussian 
#>   Temporal structure: gaussian_process 
#>   Residual scale: constant 
#>   GP: matern32 / approximate 
#>   ARMA: (0,0) 
#>   Predictive target: future_segment 
#>   Complexity: ok 
#>   Fit performed: FALSE
plot_pupil_model_complexity(spec)

Exact GP computation remains available, but the complexity audit requires explicit review when the unique time-by-condition grid becomes large.

Hyperparameters are posterior estimands

fit <- fit_advanced_pupil_model_backend(spec, backend = "cmdstanr")
hyper <- pupil_gp_hyperparameters(fit)
pupil_gp_table(hyper)
plot_pupil_gp_hyperparameters(hyper)

trajectory <- predict_advanced_pupil_trajectory(fit)
plot_advanced_pupil_trajectory(trajectory)

Length scale and marginal GP standard deviation describe the fitted temporal function prior/posterior. They are not direct psychological constructs.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.