Description Usage Arguments Details Value Author(s) References Examples
Constructs an Attention Learning COVEring map model.
1 |
learning |
an object of class |
response |
an object of class |
parameters |
an (optional) list with (starting) values of the parameters. If no values are supplied, defaults are used. |
humble |
logical. If TRUE, humble teaching signal is used. |
exemplar.locations |
(optional) list with exemplar node locations. See details. |
data |
(optional) data frame for evaluation of the formula. |
subset |
(optional) subset of the data. |
fixed |
(optional) logical vector indicating whether parameters are fixed (TRUE) or freely estimable (FALSE). |
random.locations |
If no exemplar.locations are given, should they be determined randomly? If FALSE (default), then unique values of training cues are used as exemplar locations. |
n.locations |
Number of randomly generated exemplar locations, if random.locations = TRUE |
base |
which level of the criterion variable is considered the base category? Defaults to the first level. |
ntimes |
an optional vector with, for each repetition in the data, the total number of trials. |
replicate |
are the repeated series true replications, i.e., are the model parameters considered identical for each series? |
... |
other arguments (currently not used) |
ALCOVE (Kruschke, 1992) is based on the GCM
model, but
has a mechanism to learn the attention weights. It is formulated as an ANN.
A (fitted) object of class ALCOVE
Maarten Speekenbrink
Kruschke, J. K. (1992). ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99, 22-44.
1 2 3 4 5 6 7 8 9 | ## open weather prediction data
data(WPT)
controls <- subset(WPT,id %in% paste("C",1:16,sep=""))
## initialize model
mod <- ALCOVE(learning=y~x1+x2+x3+x4,response=r~1,data=controls,
fix=list(r=TRUE,q=TRUE),ntimes=rep(200,16))
## estimate free parameters (discounting first 5 trials)
## Not run: mod <- fit(mod,discount=5)
summary(mod)
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