shin92exalcove: Simulation of CIRP shin92 with ex-ALCOVE model
In catlearn: Formal Psychological Models of Categorization and Learning

Description Usage Arguments Details Value Author(s) References

Runs a simulation of the shin92 CIRP using the slpALCOVE model implementation as an exemplar model and shin92train as the input representation.

1	shin92exalcove(params = NULL)

params

A vector containing values for c, phi, la, and lw, in that order, e.g. params = c(2.1, 0.6, 0.09, 0.9). See slpALCOVE for an explanation of these parameters. Where params = NULL, best-fitting parameters are derived from optimzation archive shin92exalcove_opt

An exemplar-based simulation using slpALCOVE and shin92train. The co-ordinates for the radial-basis units are derived from the test stimuli in shin92train. The output is the average of 100 simulated subjects.

The defaults for params are the best fit of the model to the shin92 CIRP. They were derived through minimization of SSE using non-linear optimization from 16 different initial states (using code not included in this archive).

The other parameters of slpALCOVE are set as follows: r = 2, q = 1, initial alpha = 1 / (number of input dimensions), inital w = 0. These values are conventions of modeling with ALCOVE, and should not be considered as free parameters. They are set within the shin92exaclove function, and hence can't be changed without re-writing the function.

This simulation was reported in Wills et al. (2017).

A matrix of predicted response probabilities, in the same order and format as the observed data contained in shin92.

Andy Wills & Garret O'Connell

Shin, H.J. & Nosofsky, R.M. (1992). Similarity-scaling studies of dot-pattern classification and recognition. Journal of Experimental Psychology: General, 121, 278–304.

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 May 2, 2019, 4:41 p.m.