Measuring physical performance via self-report in healthy young adults

Discrepancies exist in literature as to what extent self-reporting can replace performance-based testing. To answer this question, self-reports and performance tests should measure identical constructs. Previous studies did not measure identical constructs. The objective of our study was to investigate to what extent self-reporting can replace performance-based testing. Seventy-two healthy subjects were tested. The constructs of the self-reports and the performance tests covered the same components to enable a comparison of self-reports and performance test results. Three different self-reports and a performance test were used to measure physical performance. Additionally, rating of perceived exertion was measured after the subjects lifted a reference weight to predict maximal lifting performance. The controls were age, gender, educational level, subject's participation in fitness, availability of reference data, motivation, attitude, general self-efficacy, and mood. Results showed that all lifting tasks could be predicted, though not solely via self-reporting. A prediction of the performance test results with a margin of +/-5 kg of error could be made for at least 79% of the subjects, via gender, self-reporting, and subject's participation in fitness. Self-reporting may not replace performance testing, although performance testing can be predicted with a margin of error of +/- 5 kg for at least 79% of the healthy subject

Measuring physical performance via self-report in healthy young adults

Discrepancies exist in literature as to what extent self-reporting can replace performance-based testing. To answer this question, self-reports and performance tests should measure identical constructs. Previous studies did not measure identical constructs. The objective of our study was to investigate to what extent self-reporting can replace performance-based testing. Seventy-two healthy subjects were tested. The constructs of the self-reports and the performance tests covered the same components to enable a comparison of self-reports and performance test results. Three different self-reports and a performance test were used to measure physical performance. Additionally, rating of perceived exertion was measured after the subjects lifted a reference weight to predict maximal lifting performance. The controls were age, gender, educational level, subject's participation in fitness, availability of reference data, motivation, attitude, general self-efficacy, and mood. Results showed that all lifting tasks could be predicted, though not solely via self-reporting. A prediction of the performance test results with a margin of +/-5 kg of error could be made for at least 79% of the subjects, via gender, self-reporting, and subject's participation in fitness. Self-reporting may not replace performance testing, although performance testing can be predicted with a margin of error of +/- 5 kg for at least 79% of the healthy subject

Second-order propositional modal logic: expressiveness and completeness results

In this paper we advance the state-of-the-art on the application of second-order propositional modal logic (SOPML) in the representation of individual and group knowledge, as well as temporal and spatial reasoning. The main theoretical contributions of the paper can be summarised as follows. Firstly, we introduce the language of (multi-modal) SOPML and interpret it on a variety of different classes of Kripke frames according to the features of the accessibility relations and of the algebraic structure of the quantification domain of propositions. We provide axiomatisations for some of these classes, and show that SOPML is unaxiomatisable on the remaining classes. Secondly, we introduce novel notions of (bi)simulations and prove that they indeed preserve the interpretation of formulas in (the universal fragment of) SOPML. Then, we apply this formal machinery to study the expressiveness of Second-order Propositional Epistemic Logic (SOPEL) in representing higher-order knowledge, i.e., the knowledge agents have about other agents’ knowledge, as well as graph-theoretic notions (e.g., 3-colorability, Hamiltonian paths, etc.). The final outcome is a rich formalism to represent and reason about relevant concepts in artificial intelligence, while still having a model checking problem that is no more computationally expensive than that of the less expressive quantified boolean logic

Logics of preference when there is no best

Well-behaved preferences (e.g., total pre-orders) are a cornerstone of several areas in artificial intelligence, from knowledge representation, where preferences typically encode likelihood comparisons, to both game and decision theories, where preferences typically encode utility comparisons. Yet weaker (e.g., cyclical) structures of comparison have proven important in a number of areas, from argumentation theory to tournaments and social choice theory. In this paper we provide logical foundations for reasoning about this type of preference structures where no obvious best elements may exist. Concretely, we compare and axiomatize a number of ways in which the concepts of maximality and optimality can be lifted to this general class of preferences. In doing so we expand the scope of the long-standing tradition of the logical analysis of preference