Springer Science and Business Media LLC

Since its introduction in 1957, the Mooney test has continued to see active use in studies of visual perception, in studies using brain imaging, and in clinical research. Mooney’s original version is of limited length, however, and was designed to be administered by time-consuming personal interview. We have developed a new, extended version of the Mooney test that is suitable for online testing and for use in a test–retest paradigm. The Mooney–Verhallen Test (MVT) comprises 144 trials, takes on average less than 10 min to complete, and has a Spearman–Brown-corrected test–retest reliability of ρ = .89. We outline our methods for developing the stimuli and for selecting the final stimulus set, and we present the results from two rounds of testing on two independent samples of 374 participants and 505 participants, respectively. The test is freely available for scientific use.

Pavlovian conditioned inhibition training provides a method for investigating inhibitory dimensional stimulus control of the rabbit’s conditioned nictitating membrane response. The basic technique consists of reinforcing a burst of white noise while a compound made up of white noise and a tone is systematically not reinforced. Generalization tests to the white noise and a series of test tones result in a U-shaped gradient which remains relatively stable over a series of training-test phases.

An emerging area of research in engineering psychology is the evaluation of text entry for mobile devices using a small number of keys for the control of cursor direction and character selection from a matrix of characters (i.e., selection-based data entry). The present article describes a software tool designed to reduce time and effort in the development of prototypes of alternative selection-based text-entry schemes and their empirical evaluation. The tool, available for distribution to researchers, educators, and students, uses Action Script code compiled into an executable file that has an embedded Adobe Flash Player and is compatible with most operating systems (including Microsoft Windows, Apple OSX, and Linux).

People tend to like stimuli—ranging from human faces to text—that are prototypical, and thus easily processed. However, recent research has suggested that less typical stimuli may be preferred in creative contexts, such as fine art or music lyrics. In an archival sample of movie scripts, we tested whether genre-typicality predicted film ratings as a function of rater role (novice audience member or expert film critic). Genre-typicality was operationalized as the profile correlations between linguistic arcs (across five segments, or acts) for each script and within-genre averages. We predicted (1) that critics would prefer more disfluent (genre-atypical) films and general audiences would prefer fluent (genre-typical) films, and (2) that these differences would be most pronounced for genres expected to be more entertaining (e.g., action/adventure) than challenging (e.g., tragedy). Partly consistent with our hypotheses, the results showed that critics gave higher ratings to action/adventure films with less typical positive emotion arcs. However, regardless of audience-member or professional-critic status, higher ratings were attributed to films that were more genre-atypical (or disfluent), in terms of analytic thinking, narrative action, and emotional tone, across all genres except family/kids films. Such findings support the growing literature on the appeal of disfluency in the arts and have relevance for researchers in psychology and computer science who are interested in computational linguistic approaches to attitudes, film, and literature.

There seems to be confusion among researchers regarding whether it is good practice to center variables at their means prior to calculating a product term to estimate an interaction in a multiple regression model. Many researchers use mean centered variables because they believe it’s the thing to do or because reviewers ask them to, without quite understanding why. Adding to the confusion is the fact that there is also a perspective in the literature that mean centering does not reduce multicollinearity. In this article, we clarify the issues and reconcile the discrepancy. We distinguish between “micro” and “macro” definitions of multicollinearity and show how both sides of such a debate can be correct. To do so, we use proofs, an illustrative dataset, and a Monte Carlo simulation to show the precise effects of mean centering on both individual correlation coefficients as well as overall model indices. We hope to contribute to the literature by clarifying the issues, reconciling the two perspectives, and quelling the current confusion regarding whether and how mean centering can be a useful practice.