shin92oat: Ordinal adequacy test for simulations of shin92 CIRP

In catlearn: Formal Psychological Models of Categorization and Learning

Ordinal adequacy test for simulations of shin92 CIRP

Description

Tests whether a model output passes the ordinal adequacy criterion for the shin92 CIRP.

Usage

  shin92oat(dta, xtdo=FALSE)

Arguments

dta	Matrix containing model output. The matrix must have the same format, row order, and column names, as that returned by shin92exalcove; with that proviso, the output of any simulation implementation can be handled by this function.
xtdo	eXTenDed Output: Either TRUE or FALSE

Details

This function implements the Wills et al. (2017) ordinal adequacy test for the shin92 CIRP. Specifically, a model passes this test if response accuracy is higher for novel items from the size-10 condition than novel items from the size-3 condition.

Alternatively, by setting xtdo to TRUE, this function returns the summary model predictions reported by Wills et al. (2017).

Value

Where xtdo=FALSE, this function returns TRUE if the ordinal adequacy test is passed, and FALSE otherwise.

Where xtdo=TRUE, this function returns a summary matrix. The rows are the two category sizes, the columns are the three principal stimulus types (old, prototype, new), and the values are predicted accuracy scores.

Author(s)

Andy Wills and Garret O'Connell

References

Shin, H.J. & Nosofsky, R.M. (1992). Similarity-scaling studies of dot-pattern classification and recognition. Journal of Experimental Psychology: General, 121, 278–304.

Wills, A.J., O'Connell, G., Edmunds, C.E.R. & Inkster, A.B. (2017). Progress in modeling through distributed collaboration: Concepts, tools, and category-learning examples. The Psychology of Learning and Motivation, 66, 79-115.

See Also

shin92

catlearn documentation built on April 4, 2023, 5:12 p.m.