Description Usage Arguments Details Value Author(s) References See Also

Runs a simulation of the `krus96`

AP using the
`slpEXIT`

model implementation and
`krus96train`

as the input representation.

1 | ```
krus96exit (params = c(2.87, 2.48, 4.42, 4.42, .222, 1.13, .401))
``` |

`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 |

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.

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

.

René Schlegelmilch, Andy Wills

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*.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.