nosof94train {catlearn}R Documentation

Input representation of nosof94 for models input-compatible with slpALCOVE.

Description

Create randomized training blocks for CIRP nosof94, in a format suitable for the slpALCOVE model, and other models that use the same input representation format. The stimulus co-ordinates are assumed, and use the same binary representation as the abstract category structure.

Usage


nosof94train(cond = 1, blocks = 16, absval = -1, subjs = 1, seed = 7624,
missing = 'geo')

Arguments

cond

Category structure type (1-6), as defined by Shepard et al. (1961).

blocks

Number of blocks to generate. Omit this argument to get the same number of blocks (16) as used in the simulations reported by Nosofsky et al. (1994).

absval

Teaching value to be used where category is absent.

subjs

Number of simulated subjects to be run.

seed

Sets the random seet.

missing

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

Details

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 trials.

blk - training block

stim - stimulus number (arbitrary, but consistent across different values of problem)

x1, x2, x3 - input representation

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

m1, m2, m3 - Missing dimension flags (always set to zero in this experiment, indicating all input dimensions are present on all trials). Only produced if missing = 'geo'.

In Nosofsky et al. (1994), block 1 was randomized differently to later blocks. This feature is retained in this implementation.

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 routine was originally developed to support simulations reported in Wills & O'Connell (n.d.).

Value

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

Author(s)

Andy Wills

References

Nosofsky, R.M., Gluck, M.A., Plameri, T.J., McKinley, S.C. and Glauthier, P. (1994). Comparing models of rule-based classification learning: A replication and extension of Shepaard, Hovland, and Jenkins (1961). Memory and Cognition, 22, 352–369

Shepard, R.N., Hocland, C.I., & Jenkins, H.M. (1961). learning and memorization of classifications. Psychological Monographs, 75, Whole No. 517.

Wills, A.J. & O'Connell (n.d.). Averaging abstractions. Manuscript in preparation.

See Also

nosof94train, nosof94oat


[Package catlearn version 0.5 Index]