# Attention Learning COVEring map model (ALCOVE)

### Description

Constructs the learning submodel of an Attention Learning COVEring map model.

### Usage

1 2 3 |

### Arguments

`formula` |
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). |

`parStruct` |
(optional) ParStruct object. Note that if parStruct is given, the ‘fixed’ argument above will be ignored. |

`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? |

### Details

ALCOVE (Kruschke, 1992) is based on the `GCM`

model, but
has a mechanism to learn the attention weights. It is formulated as an ANN.

### Value

A (fitted) object of class `ALCOVElearning`

### Author(s)

Maarten Speekenbrink

### References

Kruschke, J. K. (1992). ALCOVE: An exemplar-based connectionist model of
category learning. *Psychological Review*, *99*, 22-44.

### Examples

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 <- ALCOVElearning(y~x1+x2+x3+x4,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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