nosof94train {catlearn}

Input representation of nosof94 for models input-compatible with slpALCOVE or slpSUSTAIN

Description

Create randomized training blocks for CIRP nosof94, in a format suitable for the slpALCOVE or slpSUSTAIN models, and other models that use the same input representation formats.

Usage

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

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 seed.

missing

If set to 'geo', output missing dimension flags (see below). If set to 'pad', use the padded stimulus representation format of slpSUSTAIN. If set to 'pad', set absval to zero.

blkstyle

If set to 'accurate', reproduce the randomization of this experiment, as described in Nosofsky et al. (1994). If set to 'eights', use instead the randomization used in the Gureckis (2016) simulation of this experiment.

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 of each simulated subject, set to zero (normal trial) for all other trials.

blk - training block

stim - Stimulus number, ranging from 1 to 8. The numbering scheme is the same as in Nosofsky et al. (1994, Figure 1), under the mapping of dim_1_left = 0, dim_1_right = 1, dim_2_front = 0, dim_2_back = 1, dim_3_bottom = 0, dim_3_top = 1.

x1, x2, ... - input representation. Where missing='geo', x1, x2, and x3 are returned, each set at 1 or 0. This is the binary dimensional representation required by models such as slpALCOVE, where e.g. x2 is the value on the second dimension. Where missing='pad', x1, x2, y1, y2, z1, z2, are returned. This is the padded represenation required by models such as slpSUSTAIN; e.g. y1 and y2 represent the two possible values on dimension 2, so if y1 is black, y2 is white, and the stimulus is white, then [y1, y2] = [0, 1].

t1, t2 - Category label (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'.

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 Wills et al. (n.d.).

Value

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

Author(s)

Andy Wills, Lenard Dome

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

Gureckis, T. (2016). https://github.com/NYUCCL/sustain_python

Wills et al. (n.d.). Benchmarks for category learning. Manuscript in preparation.

See Also

nosof94train, nosof94oat