slpDIVA: DIVA category learning model

Description Usage Arguments Details Value Note Author(s) References

View source: R/slpDIVA.R

Description

DIVergent Autoencoder (Kurtz, 2007; 2015) artificial neural network category learning model

Usage

1
slpDIVA(st, tr, xtdo = FALSE)

Arguments

st

List of model parameters

tr

R-by-C matrix of training items

xtdo

When set to TRUE, produce extended output

Details

This function works as a stateful list processor (Wills et al., 2017). Specifically, it takes a matrix as an argument, where each row is one trial for the network, and the columns specify the input representation, teaching signals, and other control signals. It returns a matrix where each row is a trial, and the columns are the response probabilities for each category. It also returns the final state of the network (connection weights and other parameters), hence its description as a 'stateful' list processor.

Argument st must be a list containing the following items:

st must contain the following principal model parameters:

learning_rate - Learning rate for weight updates through backpropagation. The suggested learning rate default is learning_rate = 0.15

beta_val - Scalar value for the Beta parameter. beta_val controls the degree of feature focusing (not unlike attention) that the model uses to make classification decisions (see: Conaway & Kurtz, 2014; Kurtz, 2015). beta_val = 0 turns feature focusing off.

phi - Scalar value for the phi parameter. phi is a real-valued mapping constant, see Kruschke (1992, Eq. 3).

st must also contain the following information about network architecture:

num_feats - Number of input features.

num_hids - Number of hidden units. A rough rule of thumb for this hyperparameter is to start with num_feats = 2 and add additional units if the model fails to converge.

num_cats - Number of categories.

continuous - A Boolean value to indicate if the model should work in continuous input or binary input mode. Set continuous = TRUE when the inputs are continuous.

st must also contain the following information about the initial state of the network:

in_wts - A matrix of initial input-to-hidden weights with num_feats + 1 rows and num_hids columns. Can be set to NULL when the first line of the tr matrix includes control code 1, ctrl = 1.

out_wts - A matrix of initial hidden-to-output weights with num_feats + 1 rows, num_hids columns and with the third dimension being num_cats in extent. Can be set to NULL when the first line of the tr matrix includes control code 1, ctrl = 1.

st must also contain the following information so that it can reset these weights to random values when ctrl = 1 (see below):

wts_range - A scalar value for the range of the randomly-generated weights. The suggested weight range deafult is wts_range = 1

wts_center - A scalar value for the center of the randomly-generated weights. This is commonly set to wts_center = 0

st must also contain the following parameters that describe your tr array:

colskip - Skip the first N columns of the tr array, where N = colskip. colskip should be set to the number of optional columns you have added to matrix tr, PLUS ONE. So, if you have added no optional columns, colskip = 1. This is because the first (non-optional) column contains the control values, below.

Argument tr must be a matrix, where each row is one trial presented to the network. Trials are always presented in the order specified. The columns must be as described below, in the order described below:

ctrl - column of control codes. Available codes are: 0 = normal learning trial, 1 = reset network (i.e. initialize a new set of weights following the st parameters), 2 = Freeze learning. Control codes are actioned before the trial is processed.

opt1, opt2, ... - optional columns, which may have any names you wish, and you may have as many as you like, but they must be placed after the ctrl column, and before the remaining columns (see below). These optional columns are ignored by this function, but you may wish to use them for readability. For example, you might include columns for block number, trial number, and stimulus ID number. The argument colskip (see above) must be set to the number of optional columns plus 1.

x1, x2, ... - input to the model, there must be one column for each input unit. Each row is one trial. Dichotomous inputs should be in the format -1, 1. Continuous inputs should be scaled to the range of -1, 1. As the model's learning objective is to accurately reconstruct the inputs, the input to the model is also the teaching signal. For testing under conditions of missing information, input features can be set to 0 to negate the contribution of the feature(s) for the classification decision of that trial.

t1, t2, ... - Category membership of the current stimulus. There must be one column for each category. Each row is one trial. If the stimulus is a member of category X, then the value in the category X column must be set to +1, and the values for all other category columns must be set to -1.

Value

Returns a list containing two components: (1) matrix of response probabilities for each category on each trial, (2) an st list object that contains the model's final state. A weight initialization history is also available when the extended output parameter is set xtdo = TRUE in the slpDIVA call.

Note

A faster (Rcpp) implementation of slpDIVA is planned for a future release of catlearn.

Author(s)

Garrett Honke, Nolan B. Conaway, Andy Wills

References

Conaway, N. B., & Kurtz, K. J. (2014). Now you know it, now you don't: Asking the right question about category knowledge. In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the Thirty-Sixth Annual Conference of the Cognitive Science Society (pp. 2062-2067). Austin, TX: Cognitive Science Society.

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

Kurtz, K.J. (2007). The divergent autoencoder (DIVA) model of category learning. Psychonomic Bulletin & Review, 14, 560-576.

Kurtz, K. J. (2015). Human Category Learning: Toward a Broader Explanatory Account. Psychology of Learning and Motivation, 63.

Wills, A.J., O'Connell, G., Edmunds, C.E.R., & Inkster, A.B.(2017). Progress in modeling through distributed collaboration: Concepts, tools, and category-learning examples. The Psychology of Learning and Motivation, 66, 79-115.


catlearn documentation built on July 18, 2018, 9 a.m.