Contextual Interference in Movements of the Same Class: Differential Effects on Program and Parameter Learning

Contextual interference effects in motor learning usually were not found when the tasks to be learned presumably required the same generalized motor program (GMP) and differed only with regard to the movement parameters (see Lee, Wulf, & Schmidt, 1992; Magill & Hall, 1990). Thus, tasks requiring different motor programs (e.g., different relative timings) seemed to be a prerequisite for random practice to be more effective than blocked practice. However, the previous studies (that did not find random/blocked differences) used global error measures that confounded errors in relative timing and errors in absolute timing. In the present study, subjects practiced three movement patterns that had the same relative timing (requiring the same GMP) but different overall durations (requiring different parameters). Errors in relative timing and in absolute timing were assessed separately. The results indicate that random practice is more effective for the learning of relative timing (GMP learning) and less effective for the learning of absolute timing (parameter learning) than blocked practice. Preliminary ideas as to the reasons for this effect are discussed.     

Truth and Generalized Quantification*

Kripke [1975] gives a formal theory of truth based on Kleene's strong evaluation scheme. It is probably the most important and influential that has yet been given—at least since Tarski. However, it has been argued that this theory has a problem with generalized quantifiers such as All(?, ψ)—that is, All ?s are ψ—or Most(?, ψ). Specifically, it has been argued that such quantifiers preclude the existence of just the sort of language that Kripke aims to deliver—one that contains its own truth predicate. In this paper I solve the problem by showing how Kleene's strong scheme, and Kripke's theory based on it, can in a natural way be extended to accommodate the full range of generalized quantifiers.     

The effect of variable delays on self-control

Five pigeons served as subjects in an experiment that examined the effects of variable as opposed to fixed delays on preference in a self-control paradigm (choice between larger, more delayed and smaller, less delayed reinforcers). Nonindependent concurrent variable-interval schedules were used to measure choice. When delays to the larger, more delayed reinforcers were variable as opposed to fixed, the subjects showed an increased preference for that alternative (the self-control alternative). A series of regressions revealed that the hyperbolic decay model and incentive theory provided poor fits to the data, but a modified version of the generalized matching law provided an adequate fit. Together, consistent with a general prediction made by discounting models, the data supported the conclusion that variable delays can increase self-control. However, specific discounting models were not able to explain the present data well.     

The unique correspondence of the item response function and item category response functions in polytomously scored item response models

The item response function (IRF) for a polytomously scored item is defined as a weighted sum of the item category response functions (ICRF, the probability of getting a particular score for a randomly sampled examinee of ability ). This paper establishes the correspondence between an IRF and a unique set of ICRFs for two of the most commonly used polytomous IRT models (the partial credit models and the graded response model). Specifically, a proof of the following assertion is provided for these models: If two items have the same IRF, then they must have the same number of categories; moreover, they must consist of the same ICRFs. As a corollary, for the Rasch dichotomous model, if two tests have the same test characteristic function (TCF), then they must have the same number of items. Moreover, for each item in one of the tests, an item in the other test with an identical IRF must exist. Theoretical as well as practical implications of these results are discussed.This research was supported by Educational Testing Service Allocation Projects No. 79409 and No. 79413. The authors wish to thank John Donoghue, Ming-Mei Wang, Rebecca Zwick, and Zhiliang Ying for their useful comments and discussions. The authors also wish to thank three anonymous reviewers for their comments.     

Question answering and database querying: Bridging the gap with generalized quantification

Even though Questions Answering and Database Querying have very different goals and frameworks, collaboration between the two fields could be mutually beneficial. However, the different assumptions in each field makes such collaboration difficult. In this paper, we introduce a query language with generalized quantifiers (QLGQ) and show how it could be used to help bridge the gap between the two fields.     

The second order approximation to sample influence curve in canonical correlation analysis

A second order approximation to the sample influence curve (SIC) in canonical correlation analysis has been derived in the literature. However, it does not seem satisfactory for some cases. In this paper, we present a more accurate second order approximation. As a particular case, the proposed method is exact for the SIC of the squared multiple correlation coefficient. An example is given. The authors are most grateful to the associate editor and three reviewers for valuable comments and suggestions which improved the presentation of the paper considerably. The first author was partly supported by a RGC earmarked research grant of Hong Kong.