The impact of stimulus and response variability on S-R correspondence effects

Six experiments investigated how variability on irrelevant stimulus dimensions and variability on response dimensions contribute to spatial and nonspatial stimulus-response (S-R) correspondence effects. Experiments 1-3 showed that, when stimuli varied in location and number, S-R correspondence effects for location or numerosity occurred when responses varied on these dimensions but not when responses were invariant on these dimensions. These results are consistent with the response-discrimination account, according to which S-R correspondence effects should only arise for a dimension that is used for discriminating between responses in working memory. Experiments 4-6 showed that, when responses varied in location and number, both invariant and variable stimulus number produced correspondence effects in S-R numerosity. In summary, the present results indicate that the usefulness of a particular dimension for response discrimination can be sufficient for producing S-R correspondence effects, whereas variability of a stimulus dimension is not sufficient for producing such effects.     

An integrated perspective on the relation between response speed and intelligence

Research in the field of mental chronometry and individual differences has revealed several robust regularities (Jensen, 2006). These include right-skewed response time (RT) distributions, the worst performance rule, correlations with general intelligence (g) that are more pronounced for RT standard deviations (RTSD) than they are for RT means (RTm), an almost perfect linear relation between individual differences in RTSD and RTm, linear Brinley plots, and stronger correlations between g and inspection time (IT) than between g and RTm. Here we show how all these regularities are manifestations of a single underlying relationship, when viewed through the lens of Ratcliff’s diffusion model ( [Ratcliff, 1978] and [Ratcliff et al., 2008] ). The single underlying relationship is between individual differences in general intelligence and individual differences in “drift rate”, which is just the speed of information processing in Ratcliff’s model. We also test and confirm a strong prediction of the diffusion model, namely that the worst performance rule generalizes to phenomena outside of the field of intelligence. Our approach provides an integrative perspective on intelligence findings.     

The role of ethnocultural variables in response to terrorism

The threat of terrorism is having a profound impact on Americans. This study examined ethnocultural variables of ethnic background, gender, age, and educational background to better understand first reactions to, explanations for, and responses to what happened on September 11, 2001. Data were obtained from a sample of university students, church and civic group members, and people of the general community. Results suggest that ethnic background, gender, and age influence reactions to terrorism. Ethnic background and gender influenced causal explanations about the attacks. Strong gender differences were noted in how participants were affected by the attacks; strongest similarities were observed in reports of first reactions to news of the events, with most people experiencing shock and disbelief. Conclusions stress the importance of future mental health interventions and research giving strong consideration to ethnocultural variables when dealing with victims of terrorism.     

On the relationship between item response theory and factor analysis of discretized variables

Equivalence of marginal likelihood of the two-parameter normal ogive model in item response theory (IRT) and factor analysis of dichotomized variables (FA) was formally proved. The basic result on the dichotomous variables was extended to multicategory cases, both ordered and unordered categorical data. Pair comparison data arising from multiple-judgment sampling were discussed as a special case of the unordered categorical data. A taxonomy of data for the IRT and FA models was also attempted.The work reported in this paper has been supported by Grant A6394 to the first author from the Natural Sciences and Engineering Research Council of Canada.     

The commonsense conception and its relation to scientific theory

Abstract

In this paper I discern two concepts of meaning: meaning O ‐ which is assigned by us on the basis of our commonsense conception in order to constitute our own daily reality — and meaning I, which we assign when we interpret reality scientifically. Authors who contend that the commonsense conception is nothing but a kind of scientific theory, do not see that the two fields of life have their own concept of meaning. Commonsense and science are not separate from each other, however: though both have their own practices, the way we interpret reality scientifically presupposes our commonsense conception.     

Ramsay-curve item response theory (RC-IRT) to detect and correct for nonnormal latent variables

Popular methods for fitting unidimensional item response theory (IRT) models to data assume that the latent variable is normally distributed in the population of respondents, but this can be unreasonable for some variables. Ramsay-curve IRT (RC-IRT) was developed to detect and correct for this nonnormality. The primary aims of this article are to introduce RC-IRT less technically than it has been described elsewhere; to evaluate RC-IRT for ordinal data via simulation, including new approaches for model selection; and to illustrate RC-IRT with empirical examples. The empirical examples demonstrate the utility of RC-IRT for real data, and the simulation study indicates that when the latent distribution is skewed, RC-IRT results can be more accurate than those based on the normal model. Along with a plot of candidate curves, the Hannan-Quinn criterion is recommended for model selection.