shin92 {catlearn} | R Documentation |
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.
data(shin92)
A data frame with the following columns:
Experimental condition (category size). Takes values : 3, 10
Category membership of stimulus. Takes values: 1, 2
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.
Mean probability, across participants, of responding that the item belongs to category 2.
Wills et al. (2016) discuss the derivation of this CIRP. In brief, a
between-subjects manipulation of category size was selected to avoid a
potential response-bias confound in within-subject studies of the
category-size effect. The between-subjects effect has been
demonstrated independently on three occasions (DiNardo & Toppino,
1984; Homa et al., 1987; Shin & Nosofsky, 1992). 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.
Andy J. Wills andy@willslab.co.uk
Shin, H.J. & Nosofsky, R.M. (1992). Similarity-scaling studies of dot-pattern classification and recognition. Journal of Experimental Psychology: General, 121, 278-304.
DiNardo, M.J. & Toppino, T.C. (1984). Formation of ill-defined concepts as a function of category size and category exposure. Bulletin of the Psychonomic Society, 22, 317-320.
Homa, D., Burruel, L. & Field, D. (1987). The changing composition of abstracted categories under manipulations of decisional change, choice difficulty, and category size. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 401-412.
Homa, D., Cross, J., Cornell, D., Goldman, D. & Shwartz, S. (1973). Prototype abstraction and classification of new instances as a function of number of instances defining the prototype. Journal of Experimental Psychology, 101, 116-122.
Wills, A.J., O'Connell, G., Edmunds, C.E.R. & Inkster, A.B. (2016). Progress in modeling through distributed collaboration: Concepts, tools, and category-learning examples. The Psychology of Learning and Motivation.