nosof94train {catlearn} | R Documentation |
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.
nosof94train(cond = 1, blocks = 16, absval = -1, subjs = 1, seed = 7624, missing = 'geo')
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) |
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.).
R by C matrix, where each row is one trial, and the columns contain model input.
Andy Wills
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.