shin92train: Input representation of shin92 for models input-compatible...

View source: R/shin92train.R

shin92trainR Documentation

Input representation of shin92 for models input-compatible with slpALCOVE.


Creates randomized training and transfer blocks for CIRP shin92 , in a format suitable for the slpALCOVE model, and any other model that uses the same input representation format. The stimulus co-ordinates come from a MDS solution reported by Shin & Nosofsky (1992).


  shin92train(condition = 'equal3', learn.blocks = 8, trans.blocks = 3,
          absval = -1, format = 'mds', subjs = 1, seed = 8416, missing =



Experimental condition 'equal3', 'equal10', 'unequal3', or 'unequal10', as defined by Shin & Nosofsky (1992).


Number of training blocks to generate. Omit this argument to get the same number of training blocks as the published study (8).


Number of transfer blocks to generate. Omit this argument to get the same number of transfer blocks as the published study (3).


Teaching value to be used where category is absent.


Specifies format used for input representation. Only one format is currently suported, so this option is provided solely to support future development.


Number of simulated subjects to be run.


Sets the random seed


If set to 'geo', output missing dimension flags (see below)


A matrix is produced, with one row for each trial, and with the following columns:

ctrl - Set to 1 (reset model) for trial 1, set to zero (normal trial) for all other training trials, and set to 2 (freeze learning) for all transfer trials.

cond - 1 = equal3, 2 = equal10, 3 = unequal3, 4 = unequal10

phase - 1 = training, 2 = transfer

blk - block of trials

stim - stimulus number; these correspond to the rows in Tables A3 and A4 of Shin & Nosofsky (1992)

x1 ... x6 - input representation. These are the co-ordinates of an MDS solution for these stimuli (see Shin & Nosofsky, 1992, Tables A3 and A4). Note: Size 3 conditions have a four-dimensional MDS solution, so the output is x1 ... x4

t1, t2 - teaching signal (1 = category present, absval = category absent)

m1 ... m6 - Missing dimension flags (always set to zero in this experiment, indicating all input dimensions are present on all trials). Note: ranges from m1 to m4 for Size 3 conditions. Only produced if missing = 'geo'.

Although the trial ordering is random, a random seed is used, so multiple calls of this function with the same parameters should produce the same output. This is usually desirable for reproducibility and stability of non-linear optimization. To get a different order, use the seed argument to set a different seed.

This function was originally developed to support simulations reported in Wills et al. (2017).


R by C matrix, where each row is one trial, and the columns contain model input.


Andy Wills


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.

See Also

shin92, shin92oat, slpALCOVE

catlearn documentation built on March 26, 2022, 1:07 a.m.