nosof88train {catlearn}

Input representation of nosof88 for models input-compatible with slpALCOVE.

Description

Create randomized training blocks for CIRP nosof88, 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 Nosofsky (1987) for the same stimuli.

Usage

nosof88train(condition = 'B', blocks = 3, absval = -1, subjs = 1, seed =
4182, missing = 'geo')

Arguments

condition

Experimental condition 'B', 'E2', or 'E7', as defined by Nosofsky (1988).

blocks

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

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)

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.

cond - 1 = condition B, 2 = condition E2, 3 = condition E7

blk - training block

stim - stimulus number (as defined by Nosofsky, 1988)

x1, x2 - input representation. These are the co-ordinates of an MDS solution for these stimuli (see Nosofsky, 1987).

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

m1, m2 - 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 implementation assumes a block length of 64 trials for conditions E2 and E7, rather than the 63 trials reported by Nosofsky (1988).

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 & Garret O'Connell

References

Nosofsky, R.M. (1987). Attention and learning processes in the identification and categorization of integral stimuli, Journal of Experimental Psychology: Learning, Memory and Cognition, 13, 87-108.

Nosofsky, R.M. (1988). Similarity, frequency, and category representations, Journal of Experimental Psychology: Learning, Memory and Cognition, 14, 54-65.

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

See Also

nosof88, nosof88oat, slpALCOVE