shin92exalcove_opt: Parameter optimization of ex-ALCOVE model with shin92 CIRP
In catlearn: Formal Psychological Models of Categorization and Learning

Description Usage Arguments Details Value Author(s) References

Uses shin92exalcove to find best-fitting parameters for the ex-ALCOVE model for the shin92 CIRP.

1 2	shin92exalcove_opt(params = c(2, 1, 0.25, 0.75), recompute = FALSE, trace = 0)

`params`	A vector containing the initial values for c, phi, la, and lw, in that order. See `slpALCOVE` for an explanation of these parameters. Where `recompute` is FALSE, this argument has no effect.
`recompute`	When set to TRUE, the function re-runs the optimization (which takes about 25 minutes on a 2.4 GHz processor). When set to FALSE, the function returns a stored copy of the results of the optimization (which is instantaneous).
`trace`	Sets the level of tracing information (i.e. information about the progress of the optimization), as defined by the `optim` function. Set to 6 for maximally verbose output. Where `recompute` is FALSE, this argument has no effect.

This function is an archive of the optimization procedure used to derive the best-fitting parameters for the shin92exalcove simulation; see Spicer et al. (2017) for a tutorial introduction to the concept of simulation archives.

Optimization used the L-BFGS-B method from the optim function of the standard R stats package. The objective function was sum of squared errors. Please inspect the source code for further details (e.g. type shin92exalcove_opt).

This function was run in 16 times from different starting points, using 8 threads on a Core i7 3.6 GHz processor. The default parameters of this function are those for the best fit from those 16 starting points. The 16 starting points were

pset <- rbind( c(2,1,.25,.25),c(2,1,.25,.75),c(2,1,.75,.25),c(2,1,.75,.75), c(2,3,.25,.25),c(2,3,.25,.05),c(2,3,.75,.25),c(2,3,.75,.75), c(8,1,.25,.25),c(8,1,.25,.75),c(8,1,.75,.25),c(8,1,.75,.75), c(8,3,.25,.25),c(8,3,.25,.75),c(8,3,.75,.25),c(8,3,.75,.75) )

not all of which converged successfully.

A vector containing the best-fitting values for c, phi, la, and lw, in that order. See slpALCOVE for an explanation of these parameters.

Andy Wills

Spicer, S., Jones, P.M., Inkster, A.B., Edmunds, C.E.R. & Wills, A.J. (2017). Progress in learning theory through distributed collaboration: Concepts, tools, and examples. Manuscript in preparation.

catlearn documentation built on May 2, 2019, 4:41 p.m.