Linear separability in superordinate natural language concepts

Two experiments are reported in which linear separability was investigated in superordinate natural language concept pairs (e.g., toiletry-sewing gear). Representations of the exemplars of semantically related concept pairs were derived in two to five dimensions using multidimensional scaling (MDS) of similarities based on possession of the concept features. Next, category membership, obtained from an exemplar generation study (in Experiment 1) and from a forced-choice classification task (in Experiment 2) was predicted from the coordinates of the MDS representation using log linear analysis. The results showed that all natural kind concept pairs were perfectly linearly separable, whereas artifact concept pairs showed several violations. Clear linear separability of natural language concept pairs is in line with independent cue models. The violations in the artifact pairs, however, yield clear evidence against the independent cue models.     

Using response times to detect aberrant responses in computerized adaptive testing

A lognormal model for response times is used to check response times for aberrances in examinee behavior on computerized adaptive tests. Both classical procedures and Bayesian posterior predictive checks are presented. For a fixed examinee, responses and response times are independent; checks based on response times offer thus information independent of the results of checks on response patterns. Empirical examples of the use of classical and Bayesian checks for detecting two different types of aberrances in response times are presented. The detection rates for the Bayesian checks outperformed those for the classical checks, but at the cost of higher false-alarm rates. A guideline for the choice between the two types of checks is offered.This study received funding from the Law School Admission Council (LSAC). The opinions and conclusions contained in this paper are those of the authors and do not necessarily reflect the policy and position of LSAC. The authors are most indebted to Wim M. M. Tielen for his computational assistance and to the US Defense Manpower Data Center for the permission to use the ASVAB data set in the empirical examples.     

Estimating the mean effect size in meta-analysis: Bias,precision, and mean squared error of different weighting methods

Although use of the standardized mean difference in meta-analysis is appealing for several reasons, there are some drawbacks. In this article, we focus on the following problem: that a precision-weighted mean of the observed effect sizes results in a biased estimate of the mean standardized mean difference. This bias is due to the fact that the weight given to an observed effect size depends on this observed effect size. In order to eliminate the bias, Hedges and Olkin (1985) proposed using the mean effect size estimate to calculate the weights. In the article, we propose a third alternative for calculating the weights: using empirical Bayes estimates of the effect sizes. In a simulation study, these three approaches are compared. The mean squared error (MSE) is used as the criterion by which to evaluate the resulting estimates of the mean effect size. For a meta-analytic dataset with a small number of studies, theMSE is usually smallest when the ordinary procedure is used, whereas for a moderate or large number of studies, the procedures yielding the best results are the empirical Bayes procedure and the procedure of Hedges and Olkin, respectively.     

The human element in car following models

This commentary distinguishes between the engineering-inspired formalisms used by Brackstone, M., and McDonald, M. (Transportation Research Part F (1999)) and the psychological knowledge of, and assumptions about human behaviour when car following. It outlines a preliminary mathematical model, based on psychological knowledge about car following behaviour by human drivers, which could be applied in traffic engineering, as a means of bringing these two ways of studying car following closer together.     

Causal conditional reasoning and semantic memory retrieval: a test of the semantic memory framework

This study tested and refined a framework that proposes a mechanism for retrieving alternative causes and disabling conditions (Cummins, 1995) during reasoning. Experiment 1 examined the relation between different factors affecting retrieval. The test revealed high correlations between the number of possible alternative causes or disabling conditions and their strength of association and plausibility. Experiment 2 explored the hypothesis that due to a more extended search process, conditional inferences would last longer when many alternative causes or disabling conditions were available. Affirmation of the consequent (AC) and modus ponens (MP) latencies showed the hypothesized pattern. Denial of the antecedent (DA) and modus tollens (MT) inferences did not show latency effects. The experiment also identified an effect of the number of disabling conditions on AC and DA acceptance. Experiment 3 measured efficiency of disabler retrieval by a limited time, disabler generation task. As predicted, better disabler retrieval was related to lower acceptance of the MP and MT inferences.     

A randomised Monty Hall experiment: The positive effect of conditional frequency feedback

The Monty Hall dilemma (MHD) is a notorious probability problem with a counterintuitive solution. There is a strong tendency to stay with the initial choice, despite the fact that switching doubles the probability of winning. The current randomised experiment investigates whether feedback in a series of trials improves behavioural performance on the MHD and increases the level of understanding of the problem. Feedback was either conditional or non-conditional, and was given either in frequency format or in percentage format. Results show that people learn to switch most when receiving conditional feedback in frequency format. However, problem understanding does not improve as a consequence of receiving feedback. Our study confirms the dissociation between behavioural performance on the MHD, on one hand, and actual understanding of the MHD, on the other. We discuss how this dissociation can be understood.     

A Political Account of Corporate Moral Responsibility

Should we conceive of corporations as entities to which moral responsibility can be attributed? This contribution presents what we will call a political account of corporate moral responsibility. We argue that in modern, liberal democratic societies, there is an underlying political need to attribute greater levels of moral responsibility to corporations. Corporate moral responsibility is essential to the maintenance of social coordination that both advances social welfare and protects citizens’ moral entitlements. This political account posits a special capacity of self-governance that corporations can intelligibly be said to possess. Corporations can be said to be “administrators of duty” in that they can voluntarily incorporate moral principles into their decision-making processes about how to conduct business. This account supplements and partly transforms earlier pragmatic accounts of corporate moral responsibility by disentangling responsibility from its conventional linkages with accountability, blame and punishment. It thereby represents a distinctive way to defend corporate moral responsibility and shows how Kantian thinking can be helpful in disentangling the problems surrounding the concept.     

Evaluating expectations about negative emotional states of aggressive boys using Bayesian model selection

Researchers often have expectations about the research outcomes in regard to inequality constraints between, e.g., group means. Consider the example of researchers who investigated the effects of inducing a negative emotional state in aggressive boys. It was expected that highly aggressive boys would, on average, score higher on aggressive responses toward other peers than moderately aggressive boys, who would in turn score higher than nonaggressive boys. In most cases, null hypothesis testing is used to evaluate such hypotheses. We show, however, that hypotheses formulated using inequality constraints between the group means are generally not evaluated properly. The wrong hypotheses are tested, i.e.. the null hypothesis that group means are equal. In this article, we propose an innovative solution to these above-mentioned issues using Bayesian model selection, which we illustrate using a case study.