Kachergis 2012

Kachergis et al. 2012 uncertainty- and familiarity-biased associative model

Usage

uncfam(X, B, C, variant = c("entropy", "novelty", "uncertainty-only"))

Arguments

X

Associative weight to distribute

B

Weighting of uncertainty vs. familiarity

C

Decay

variant

Which variant of the model to fit (one of "entropy", "novelty", "uncertainty-only").

Value

An object of class xslMod

Examples

mod <- uncfam(X = .1, C = 1, B = .98)
xsl_run(mod, get_example_ambiguous_condition())
#> $fits
#> $fits[[1]]
#> $fits[[1]]$sims
#> NULL
#> 
#> $fits[[1]]$responses
#>           [,1]      [,2]      [,3]      [,4]
#> [1,] 0.3888889 0.3888889 0.3888889 0.3888889
#> 
#> $fits[[1]]$perf
#>         1         2         3         4 
#> 0.3888889 0.3888889 0.3888889 0.3888889 
#> 
#> $fits[[1]]$matrix
#>       1     2     3     4
#> 1 0.035 0.035 0.010 0.010
#> 2 0.035 0.035 0.010 0.010
#> 3 0.010 0.010 0.035 0.035
#> 4 0.010 0.010 0.035 0.035
#> 
#> $fits[[1]]$sse
#> [1] 0.04938272
#> 
#> $fits[[1]]$data
#> xslData object with label "example condition" and condition "ambiguous"
#>   training trials: 2
#>       test trials: 0
#>             words: 4
#>           objects: 4
#>        accuracies: 4
#> 
#> 
#> 
#> $sse
#> [1] 0.04938272
#> 
#> $unweighted_sse
#> [1] 0.04938272
#> 

mod <- uncfam(X = .1, C = 1, B = .98, variant = "novelty")
xsl_run(mod, get_example_ambiguous_condition())
#> $fits
#> $fits[[1]]
#> $fits[[1]]$sims
#> NULL
#> 
#> $fits[[1]]$responses
#>           [,1]      [,2]      [,3]      [,4]
#> [1,] 0.3888889 0.3888889 0.3888889 0.3888889
#> 
#> $fits[[1]]$perf
#>         1         2         3         4 
#> 0.3888889 0.3888889 0.3888889 0.3888889 
#> 
#> $fits[[1]]$matrix
#>       1     2     3     4
#> 1 0.035 0.035 0.010 0.010
#> 2 0.035 0.035 0.010 0.010
#> 3 0.010 0.010 0.035 0.035
#> 4 0.010 0.010 0.035 0.035
#> 
#> $fits[[1]]$sse
#> [1] 0.04938272
#> 
#> $fits[[1]]$data
#> xslData object with label "example condition" and condition "ambiguous"
#>   training trials: 2
#>       test trials: 0
#>             words: 4
#>           objects: 4
#>        accuracies: 4
#> 
#> 
#> 
#> $sse
#> [1] 0.04938272
#> 
#> $unweighted_sse
#> [1] 0.04938272
#> 

mod <- uncfam(X = .1, C = 1, B = 0) # familiarity-only
xsl_run(mod, get_example_ambiguous_condition())
#> $fits
#> $fits[[1]]
#> $fits[[1]]$sims
#> NULL
#> 
#> $fits[[1]]$responses
#>           [,1]      [,2]      [,3]      [,4]
#> [1,] 0.3888889 0.3888889 0.3888889 0.3888889
#> 
#> $fits[[1]]$perf
#>         1         2         3         4 
#> 0.3888889 0.3888889 0.3888889 0.3888889 
#> 
#> $fits[[1]]$matrix
#>       1     2     3     4
#> 1 0.035 0.035 0.010 0.010
#> 2 0.035 0.035 0.010 0.010
#> 3 0.010 0.010 0.035 0.035
#> 4 0.010 0.010 0.035 0.035
#> 
#> $fits[[1]]$sse
#> [1] 0.04938272
#> 
#> $fits[[1]]$data
#> xslData object with label "example condition" and condition "ambiguous"
#>   training trials: 2
#>       test trials: 0
#>             words: 4
#>           objects: 4
#>        accuracies: 4
#> 
#> 
#> 
#> $sse
#> [1] 0.04938272
#> 
#> $unweighted_sse
#> [1] 0.04938272
#> 

mod <- uncfam(X = .1, C = 1, B = .98, variant = "uncertainty-only")
xsl_run(mod, get_example_ambiguous_condition())
#> $fits
#> $fits[[1]]
#> $fits[[1]]$sims
#> NULL
#> 
#> $fits[[1]]$responses
#>           [,1]      [,2]      [,3]      [,4]
#> [1,] 0.3888889 0.3888889 0.3888889 0.3888889
#> 
#> $fits[[1]]$perf
#>         1         2         3         4 
#> 0.3888889 0.3888889 0.3888889 0.3888889 
#> 
#> $fits[[1]]$matrix
#>       1     2     3     4
#> 1 0.035 0.035 0.010 0.010
#> 2 0.035 0.035 0.010 0.010
#> 3 0.010 0.010 0.035 0.035
#> 4 0.010 0.010 0.035 0.035
#> 
#> $fits[[1]]$sse
#> [1] 0.04938272
#> 
#> $fits[[1]]$data
#> xslData object with label "example condition" and condition "ambiguous"
#>   training trials: 2
#>       test trials: 0
#>             words: 4
#>           objects: 4
#>        accuracies: 4
#> 
#> 
#> 
#> $sse
#> [1] 0.04938272
#> 
#> $unweighted_sse
#> [1] 0.04938272
#>