krus96exit: Simulation of AP krus96 with EXIT model

In catlearn: Formal Psychological Models of Categorization and Learning

Simulation of AP krus96 with EXIT model

Description

Runs a simulation of the krus96 AP using the slpEXIT model implementation and krus96train as the input representation.

Usage

  krus96exit (params = c(2.87, 2.48, 4.42, 4.42, .222, 1.13, .401))

Arguments

params

A vector containing values for c, P, phi, l_gain, l_weight, l_ex, and sigma_bias (i.e. the sigma for the bias unit), in that order. See slpEXIT for an explanation of these parameters.

Details

A simulation using slpEXIT and krus96train. The stored exemplars are the four stimuli present during the training phase, using the same representation as in krus96train.

Other parameters of slpEXIT are set as follows: iterations = 10, sigma for the non-bias units = 1. These values are conventions of modeling with EXIT, and should not be considered as free parameters. They are set within the krus96exit function, and hence can't be changed without re-writing the function.

This simulation is discussed in Spicer et al. (n.d.). It produces the same response probabilities (within rounding error) as the simulation reported in Kruschke (2001), with the same parameters.

56 simulated participants are used in this simulation, the same number as used by Kruschke (2001). Kruschke reports using the same trial randomizations as used for his 56 real participants. These randomizations were not published, so it we couldn't reproduce that part of his simulation. It turns out that the choice of set of 56 randomizations matters, it affects some of the predicted response probabilities. We chose a random seed that reproduced Kruschke's response probabilities to within rounding error. As luck would have it, Kruschke's reported response probabilities (and hence this simulation) are the same (within rounding error) as the results of large sample (N = 500) simulations we have run.

Value

A matrix of predicted response probabilities, in the same order and format as the observed data contained in krus96.

Author(s)

René Schlegelmilch, Andy Wills

References

Kruschke, J. K. (2001). The inverse base rate effect is not explained by eliminative inference. Journal of Experimental Psychology: Learning, Memory & Cognition, 27, 1385-1400.

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

See Also

krus96, krus96train, slpEXIT

catlearn documentation built on April 4, 2023, 5:12 p.m.