Abstract
Formal models of categorization make different predictions about the theoretical importance of linear separability. Prior research, most of which has failed to find support for a linear separability constraint on category learning, has been conducted using tasks that involve learning two categories with a small number of members. The present experiment used four categories with three or nine patterns per category that were either linearly separable or not linearly separable. With overall category structure equivalent across category types, the linearly separable categories were found to be easier to learn than the not linearly separable categories. An analysis of individual participants' data showed that there were more participants operating under a linear separability constraint when learning large categories than when learning small ones. Formal modeling showed that an exemplar model could not account for many of these data. These results are taken to support the existence of multiple processes in categorization.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1153-1164 |
| Number of pages | 12 |
| Journal | Memory and Cognition |
| Volume | 29 |
| Issue number | 8 |
| State | Published - 2001 |
Fingerprint
ASJC Scopus subject areas
- Psychology(all)
- Experimental and Cognitive Psychology
Cite this
Expanding the search for a linear separability constraint on category learning. / Blair, Mark; Homa, Donald.
In: Memory and Cognition, Vol. 29, No. 8, 2001, p. 1153-1164.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Expanding the search for a linear separability constraint on category learning
AU - Blair, Mark
AU - Homa, Donald
PY - 2001
Y1 - 2001
N2 - Formal models of categorization make different predictions about the theoretical importance of linear separability. Prior research, most of which has failed to find support for a linear separability constraint on category learning, has been conducted using tasks that involve learning two categories with a small number of members. The present experiment used four categories with three or nine patterns per category that were either linearly separable or not linearly separable. With overall category structure equivalent across category types, the linearly separable categories were found to be easier to learn than the not linearly separable categories. An analysis of individual participants' data showed that there were more participants operating under a linear separability constraint when learning large categories than when learning small ones. Formal modeling showed that an exemplar model could not account for many of these data. These results are taken to support the existence of multiple processes in categorization.
AB - Formal models of categorization make different predictions about the theoretical importance of linear separability. Prior research, most of which has failed to find support for a linear separability constraint on category learning, has been conducted using tasks that involve learning two categories with a small number of members. The present experiment used four categories with three or nine patterns per category that were either linearly separable or not linearly separable. With overall category structure equivalent across category types, the linearly separable categories were found to be easier to learn than the not linearly separable categories. An analysis of individual participants' data showed that there were more participants operating under a linear separability constraint when learning large categories than when learning small ones. Formal modeling showed that an exemplar model could not account for many of these data. These results are taken to support the existence of multiple processes in categorization.
UR - http://www.scopus.com/inward/record.url?scp=0035717731&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0035717731&partnerID=8YFLogxK
M3 - Article
C2 - 11913752
AN - SCOPUS:0035717731
VL - 29
SP - 1153
EP - 1164
JO - Memory and Cognition
JF - Memory and Cognition
SN - 0090-502X
IS - 8
ER -