title: Taxometric analysis of fuzzy categories: a Monte Carlo study.
abstract: A small Monte Carlo study examined the performance of a form of taxometric analysis (the MAXCOV procedure) with fuzzy data sets. These combine taxonic (categorical) and nontaxonic (continuous) features, containing a subset of casts with intermediate degrees of category membership. Fuzzy data sets tended to yield taxonic findings on plot inspection and two popular consistency tests, even when the degree of fuzziness, i.e., the proportion of intermediate cases, was large. These results suggest that fuzzy categories represent a source of pseudotaxonic inferences, if on is understood in the usual binary, "either-or" fashion. This in turn implies that dichotomous causes cannot be confidently inferred when taxometric analyses yield apparently taxonic findings.
authors.affiliation: New School University.
authors.forename: Nick
authors.initials: N
authors.lastname: Haslam
authors.forename: Charles
authors.initials: C
authors.lastname: Cleland
mesh_terms: D017143:Fuzzy Logic; D006801:Humans