Biased associative model with attention scaled to trial uncertainty

A variant of uncfam() (Kachergis et al. 2012's uncertainty- and familiarity-biased associative model) in which the associative weight distributed on a trial is additionally scaled by the ratio of that trial's mean object entropy to the mean entropy of all objects seen so far. This implements the "system-level" theory proposed by Fitneva & Christiansen (2015): that learners are more alert (i.e. allocate a higher learning rate) on trials with more uncertain items, such as in a Low Initial Accuracy condition where many items have been mis-paired.

Usage

uncfam_attention(X, B, C)

Arguments

X

Associative weight to distribute

B

Weighting of uncertainty vs. familiarity

C

Decay

Value

An object of class xslMod

Examples

mod <- uncfam_attention(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 0.3926256 0.3926256
#> 
#> $fits[[1]]$perf
#>         1         2         3         4 
#> 0.3888889 0.3888889 0.3926256 0.3926256 
#> 
#> $fits[[1]]$matrix
#>       1     2          3          4
#> 1 0.035 0.035 0.01000000 0.01000000
#> 2 0.035 0.035 0.01000000 0.01000000
#> 3 0.010 0.010 0.03656604 0.03656604
#> 4 0.010 0.010 0.03656604 0.03656604
#> 
#> $fits[[1]]$sse
#> [1] 0.04774988
#> 
#> $fits[[1]]$data
#> xslData object with label "example condition" and condition "ambiguous"
#>   training trials: 2
#>       test trials: 0
#>             words: 4
#>           objects: 4
#>        accuracies: 4
#> 
#> 
#> 
#> $sse
#> [1] 0.04774988
#> 
#> $unweighted_sse
#> [1] 0.04774988
#>