This dissertation introduces a new measure of basic-level performance (Strategy Length & Internal Practicability, SLIP). SLIP implements two computational constraints on the organisation of categories in a taxonomy: the minimum number of feature tests required to place the input in a category (strategy length) and the ease with which these tests are performed (internal practicability). The predictive power of SLIP is compared with that of four other basic-level measures: context model (Medin & Schaffer, 1978; modified by Estes, 1994), category feature-possession (Jones, 1983), category utility (Corter & Gluck, 1992), and compression measure (Pothos & Chater, 1998a), drawing data from the empirical work of Rosch et al. (1976), Murphy and Smith (1982), Mervis and Crisafi (1982), Hoffmann and Ziessler (1983), Corter, Gluck and Bower (1988), Murphy (1991), Lassaline (1990), Tanaka and Taylor (1991), and Johnson and Mervis (1997). Nine experiments further test the validity of the computational constraints of SLIP using computer-synthesised 3-D artificial objects, artificial scenes, and letter strings. The first five experiments examine the two constraints of SLIP in verification. Experiment 1 isolates the effect of strategy length on basic-levelness, and Experiments 2a and 2b that of internal practicability. Experiment 3 examines the interactions between the two factors. Experiment 4 tests, whether, as predicted by SLIP, there is a linear relationship between strategy length and response times. The last four experiments study the two computational constraints in naming. Experiment 5a isolates the effect of strategy length, and Experiment 5b that of internal practicability. Experiment 6 examines the time-course of the effect of strategy length. Finally, Experiment 7 looks at the effect of robustness (i.e., the idea an approximate categorisation is better than none) on the order of feature tests in length 2 strategies.
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