Good Athlete – Bad Athlete? on the ‘Role-Model Argument’ for Banning Performance-Enhancing Drugs

The paper critically discusses a role-model argument (RMA) in favour of banning performance-enhancing drugs in sport. The argument concludes that athletes should be banned from using performance-enhancing drugs because if they are allowed to use such drugs they will encourage, or cause, youngsters who look up to them to use drugs in a way that would be harmful. In Section 2 the structure of the argument and some versions of it are presented. In Section 3 a critical discussion of RMA is presented. It is argued that we should be reluctant to accept the argument as it stands for at least three reasons: (i) it rests on an unsupported empirical claim; (ii) it also makes a false empirical claim; and (iii) the normative premise of the argument is too demanding morally. Further objections to the RMA are also discussed, but argued to be beside the point     

Negation: A theory of its meaning,representation, and use

This article presents a model-based theory of what negation means, how it is mentally represented, and how it is understood. The theory postulates that negation takes a single argument that refers to a set of possibilities and returns the complement of that set. Individuals therefore tend to assign a small scope to negation in order to minimize the number of models of possibilities that they have to consider. Individuals untrained in logic do not know the possibilities corresponding to the negation of compound assertions formed with if, or, and and, and have to infer the possibilities one by one. It follows that negations are easier to understand, and to formulate, when individuals already have in mind the possibilities to be negated. The paper shows that the evidence, including the results of recent studies, corroborates the theory.     

Grounding Bayesian accounts of numerosity and variability effects in a similarity-based framework: the case of self-organising maps

ABSTRACT

In this paper, we propose self-organising maps as possible candidates to explain the psychological mechanisms underlying category generalisation. Self-organising maps are psychologically and biologically plausible neural network models that can learn after limited exposure to positive category examples, without any need of contrastive information. They reproduce human behaviour in category generalisation, in particular, the Numerosity and Variability effects, which are usually explained with Bayesian tools. Where category generalisation is concerned, self-organising maps deserve attention to bridge the gap between the computational level of analysis in Marr's hierarchy (where Bayesian models are often situated) and the algorithmic level of analysis in which plausible mechanisms are described.     

The role of body image in the prevention of eating disorders

This article reviews theory and research pertaining to prevention of negative body image and eating disorders. Research derived from the social cognitive model (SCM) and non-specific vulnerability-stressor (NSVS) model indicates that sustained prevention effects for attitudes and behaviors are possible, but not easy to achieve or explain. These limitations are considered in the context of promising research derived from a third model, critical social perspectives (CSP). We conclude that (1) research on practice should aim beyond the examination of efficacy in order to clarify the active ingredients contributing to prevention; and (2) research informed by each of the divergent perspectives can be used to enrich theory and practice in the field of eating disorders prevention.     

Heterogeneous factor analysis models: A bayesian approach

Multilevel factor analysis models are widely used in the social sciences to account for heterogeneity in mean structures. In this paper we extend previous work on multilevel models to account for general forms of heterogeneity in confirmatory factor analysis models. We specify various models of mean and covariance heterogeneity in confirmatory factor analysis and develop Markov Chain Monte Carlo (MCMC) procedures to perform Bayesian inference, model checking, and model comparison.We test our methodology using synthetic data and data from a consumption emotion study. The results from synthetic data show that our Bayesian model perform well in recovering the true parameters and selecting the appropriate model. More importantly, the results clearly illustrate the consequences of ignoring heterogeneity. Specifically, we find that ignoring heterogeneity can lead to sign reversals of the factor covariances, inflation of factor variances and underappreciation of uncertainty in parameter estimates. The results from the emotion study show that subjects vary both in means and covariances. Thus traditional psychometric methods cannot fully capture the heterogeneity in our data.     

The First-Order Theories of Dedekind Algebras

A Dedekind Algebra is an ordered pair (B,h) where B is a non-empty set and h is an injective unary function on B. Each Dedekind algebra can be decomposed into a family of disjoint, countable subalgebras called configurations of the Dedekind algebra. There are N₀ isomorphism types of configurations. Each Dedekind algebra is associated with a cardinal-valued function on omega called its configuration signature. The configuration signature of a Dedekind algebra counts the number of configurations in the decomposition of the algebra in each isomorphism type.The configuration signature of a Dedekind algebra encodes the structure of that algebra in the sense that two Dedekind algebras are isomorphic iff their configuration signatures are identical. Configuration signatures are used to establish various results in the first-order model theory of Dedekind algebras. These include categoricity results for the first-order theories of Dedekind algebras and existence and uniqueness results for homogeneous, universal and saturated Dedekind algebras. Fundamental to these results is a condition on configuration signatures that is necessary and sufficient for elementary equivalence.     

Simple matrix methods for analyzing diffusion models of choice probability, choice response time, and simple response time

Diffusion processes (e.g., Wiener process, Ornstein-Uhlenbeck process) are powerful approaches to model human information processes in a variety of psychological tasks. Lack of mathematical tractability, however, has prevented broad applications of these models to empirical data. This tutorial explains step by step, using a matrix approach, how to construct these models, how to implement them on a computer, and how to calculate the predictions made by these models. In particular, we present models for binaries choices for unidimensional and multiattribute choice alternatives; for simple reaction time tasks; and for three alternatives choice problems.