krus96train | R Documentation |
Create randomized training blocks for AP krus96
, in a format
suitable for the slpEXIT
model, and other models that use the
same input representation format.
krus96train(blocks = 15, subjs = 56, ctxt = TRUE, seed = 1)
blocks |
Number of training blocks to generate. Omit this
argument to get the same number of blocks (15) as used in
|
subjs |
Number of simulated subjects to be run. |
ctxt |
If |
seed |
Sets the random seed. |
A data frame 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 training trials, and
set to 2 for test trials (i.e. those with no feedback).
block
- training block
stim
- Stimulus code, as described in Kruschke (1996).
x1, x2, ...
- symptom representation. Each column represents
one symptom, in the order I1, PC1, PR1, I2, PC2, PR2, context. 1 =
symptom present, 0 = symptom absent
t1, t2, ...
- Disease representation. Each column represents
one disease, in the order C1, R1, C2, R2. 1 = disease present. 0 =
disease absent.
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.).
A data frame, where each row is one trial, and the columns contain model input.
René Schlegelmilch, Andy Wills
Kruschke, J.K. (1996). Base rates in category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 3-26.
Wills et al. (n.d.). Benchmarks for category learning. Manuscript in preparation.
krus96
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