| shin92 {catlearn} | R Documentation |
Category size CIRP
Description
Category size is the number of examples of a category that have been presented to the participant. The category-size effect (e.g. Homa et al., 1973) is the phenomenon that, as category size increases, the accuracy of generalization to new members of that category also increases. The equal-frequency conditions of Experiment 3 of Shin & Nosofsky (1992) provides the data for this CIRP.
Usage
data(shin92)Format
A data frame with the following columns:
- catsize
Experimental condition (category size). Takes values : 3, 10
- cat
Category membership of stimulus. Takes values: 1, 2
- stim
Stimulus code, as defined by Shin & Nosofsky (1992). Stimuli beginning 'RN' or 'URN' are the 'novel' stimuli. Stimuli beginning 'P' are prototypes. The remaining stimuli are the 'old' (training) stimuli.
- c2acc
Mean probability, across participants, of responding that the item belongs to category 2.
Details
Wills et al. (2017) discuss the derivation of this CIRP, with Wills et
al. (n.d.) providing further details. In brief, the effect has been
independently replicated. Experiment 3 of Shin & Nosofsky (1992) was
selected due to the availability of a multi-dimensional scaling
solution for the stimuli, see shin92train.
Experiment 3 of Shin & Nosofsky (1992) involved the classification of nine-vertex polygon stimuli drawn from two categories. Category size was manipulated between subjects (3 vs. 10 stimuli per category). Participants received eight blocks of training, and three test blocks.
The data are as shown in Table 10 of Shin & Nosofsky (1992). The data are mean response probabilities for each stimulus in the test phase, averaged across test blocks and participants.
Author(s)
Andy J. Wills andy@willslab.co.uk
Source
Shin, H.J. & Nosofsky, R.M. (1992). Similarity-scaling studies of dot-pattern classification and recognition. Journal of Experimental Psychology: General, 121, 278-304.
References
Wills et al. (n.d.). Benchmarks for category learning. Manuscript in preparation.
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.