Evaluating models of recognition memory using first- and second-choice responses

Swets, Tanner Jr., and Birdsall (1961) proposed a 4-alternative forced-choice task with two choices (4AFC-2R) for distinguishing between the Equal-Variance Signal Detection model and the One-High Threshold model. This task was recently implemented in the field of recognition memory (Parks & Yonelinas, 2009), a field in which several candidate models have been proposed. One advantage of the 4AFC-2R task is that it permits parameter estimation and goodness of fit testing, something which so far was only possible through the use of Receiver Operating Characteristic (ROC) functions for the more complex candidate models. The present article provides a thorough characterization and comparison of the main recognition memory models in the context of this task. Results are illustrated by a reanalysis of Parks and Yonelinas’ original data, revealing a preference for hybrid approaches to recognition memory, more specifically for the dual-process model (Yonelinas, 1997), whereas pure signal detection models performed poorly. The present analysis provides an assessment of the merits and limitations of this task, highlighting future research applications.     

Causal reasoning

The main focus of this paper is the question as to what it is for an individual to think of her environment in terms of a concept of causation, or causal concepts, in contrast to some more primitive ways in which an individual might pick out or register what are in fact causal phenomena. I show how versions of this question arise in the context of two strands of work on causation, represented by Elizabeth Anscombe and Christopher Hitchcock, respectively. I then describe a central type of reasoning that, I suggest, a subject has to be able to engage in, if we are to credit her with causal concepts. I also point out that this type of reasoning turns on the idea of a physical connection between cause and effect, as articulated in recent singularist approaches of causation.     

Of virtual victims and victimized virtues: differential effects of experienced aggression in video games on social cooperation

Two experimental studies were used to investigate how interacting with aggressive virtual characters in video games affects trust and cooperation of players. Study 1 demonstrates that experiencing virtual aggression from a victim's perspective can impair players' investments in a subsequent common goods dilemma situation. This effect is mediated by reduced expectations of trust in the cooperativeness of interaction partners. In Study 2 the same effect was replicated by using a different cooperation task and by investigating the moderating role of justice sensitivity from a victim's perspective as a dispositional factor. Participants transferred less money to an unknown partner in a trust game after exposure to aggressive nonplayer characters in a video game. This effect was stronger for people high in victim sensitivity. Results of both studies can be interpreted in line with the sensitivity to mean intentions model and add to the body of research on violent media effects.     

Separating response‐execution bias from decision bias: Arguments for an additional parameter in Ratcliff's diffusion model

Diffusion model data analysis permits the disentangling of different processes underlying the effects of experimental manipulations. Estimates can be provided for the speed of information accumulation, for the amount of information used to draw conclusions, and for a decision bias. One parameter describes the duration of non‐decisional processes including the duration of motor‐response execution. In the default diffusion model, it is implicitly assumed that both responses are executed with the same speed. In some applications of the diffusion model, this assumption will be violated. This will lead to biased parameter estimates. Consequently, we suggest accounting explicitly for differences in the speed of response execution for both responses. Results from a simulation study illustrate that parameter estimates from the default model are biased if the speed of response execution differs between responses. A second simulation study shows that large trial numbers (N>1,000) are needed to detect whether differences in response‐execution times are based on different execution times.     

Implicit and explicit self-esteem in the context of internet addiction

Previous research has repeatedly found that people suffering from some clinical disorders (e.g., bulimia nervosa, depression) possess low explicit (i.e., conscious, deliberate) self-esteem while at the same time displaying high implicit (i.e., unconscious, automatic) self-esteem. This phenomenon has been termed damaged self-esteem and was proposed to be an indicator of psychological distress. Although Internet addiction has been found to be associated with low levels of explicit self-esteem, as well as with high levels of psychological distress, its relation to implicit self-esteem has, to our knowledge, not been investigated thus far. We therefore hypothesized that the phenomenon of damaged self-esteem could also be found amongst people suffering from Internet addiction, and conducted two studies using the Initial Preference Task as a measure of implicit self-esteem. As expected, we found that individuals scoring high on Internet addiction possess low explicit and high implicit self-esteem. This effect was, however, only found for the first name initial of the Initial Preference Task, leading to the conclusion that first and last name initials might tap into different parts of implicit self-esteem.     

Hierarchical Multinomial Processing Tree Models: A Latent-Class Approach

Multinomial processing tree models are widely used in many areas of psychology. Their application relies on the assumption of parameter homogeneity, that is, on the assumption that participants do not differ in their parameter values. Tests for parameter homogeneity are proposed that can be routinely used as part of multinomial model analyses to defend the assumption. If parameter homogeneity is found to be violated, a new family of models, termed latent-class multinomial processing tree models, can be applied that accommodates parameter heterogeneity and correlated parameters, yet preserves most of the advantages of the traditional multinomial method. Estimation, goodness-of-fit tests, and tests of other hypotheses of interest are considered for the new family of models. The author thanks Bill Batchelder, Edgar Erdfelder, Thorsten Meiser, and Christoph Stahl for helpful comments on a previous version of this paper. The author is also grateful to Edgar Erdfelder for making available the data set analyzed in this paper.