shin92protoalcove: Simulation of CIRP shin92 with proto-ALCOVE model
In catlearn: Formal Psychological Models of Categorization and Learning
View source: R/shin92protoalcove.R
shin92protoalcove R Documentation
Simulation of CIRP shin92 with proto-ALCOVE model
Description
Runs a simulation of the shin92 CIRP using the
slpALCOVE model implementation as a prototype model and
shin92train as the input representation.
Usage
shin92protoalcove(params = NULL)
Arguments
params
A vector containing values for c, phi, la, and lw, in
that orderr, 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
Details
An exemplar-based simulation using slpALCOVE and
shin92train. The co-ordinates for the radial-basis units
for the two prototypes are derived from the arithmetic means of 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).
Value
A matrix of predicted response probabilities, in the same order and
format as the observed data contained in shin92.
Author(s)
Andy Wills & Garret O'Connell
References
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 April 4, 2023, 5:12 p.m.
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View source: R/shin92protoalcove.R
| shin92protoalcove | R Documentation |
Simulation of CIRP shin92 with proto-ALCOVE model
Description
Runs a simulation of the shin92 CIRP using the
slpALCOVE model implementation as a prototype model and
shin92train as the input representation.
Usage
shin92protoalcove(params = NULL)
Arguments
params |
A vector containing values for c, phi, la, and lw, in
that orderr, e.g. params = c(2.1, 0.6, 0.09, 0.9). See
|
Details
An exemplar-based simulation using slpALCOVE and
shin92train. The co-ordinates for the radial-basis units
for the two prototypes are derived from the arithmetic means of 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).
Value
A matrix of predicted response probabilities, in the same order and
format as the observed data contained in shin92.
Author(s)
Andy Wills & Garret O'Connell
References
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.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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