Multinomial processing tree (MPT) models have become popular in cognitive psychology in the past two decades. In contrast to general-purpose data analysis techniques, such as log-linear models or other generalized linear models, MPT models are substantively motivated stochastic models for categorical data. They are best described as tools (a) for measuring the cognitive processes that underlie human behavior in various tasks and (b) for testing the psychological assumptions on which these models are based. The present article provides a review of MPT models and their applications in psychology, focusing on recent trends and developments in the past 10 years. Our review is nontechnical in nature and primarily aims at informing readers about the scope and utility of MPT models in different branches of cognitive psychology.

Typically, people are more likely to consider a previously seen or heard statement as true compared to a novel statement. This repetition-based "truth effect" is thought to rely on fluency-truth attributions as the underlying cognitive mechanism. In two experiments, we tested the nature of the fluency-attribution mechanism by means of warning instructions, which informed participants about the truth effect and asked them to prevent it. In Experiment 1, we instructed warned participants to consider whether a statement had already been presented in the experiment to avoid the truth effect. However, warnings did not significantly reduce the truth effect. In Experiment 2, we introduced control questions and reminders to ensure that participants understood the warning instruction. This time, warning reduced, but did not eliminate the truth effect. Assuming that the truth effect relies on fluency-truth attributions, this finding suggests that warned participants could control their attributions but did not disregard fluency altogether when making truth judgments. Further, we found no evidence that participants overdiscount the influence of fluency on their truth judgments.

No abstract

Eyewitnesses often report details of the witnessed crime incorrectly. However, there is usually more than 1 eyewitness observing a crime scene. If this is the case, one approach to reconstruct the details of a crime more accurately is aggregating across individual reports. Although aggregation likely improves accuracy, the degree of improvement largely depends on the method of aggregation. The most straightforward method is the majority rule. This method ignores individual differences between eyewitnesses and selects the answer shared by most eyewitnesses as being correct. We employ an alternative method based on cultural consensus theory (CCT) that accounts for differences in the eyewitnesses' knowledge. To test the validity of this approach, we showed 30 students 1 of 2 versions of a video depicting a heated quarrel between 2 people. The videos differed in the amount of information pertaining to the critical event. Participants then answered questions about the critical event. Analyses based on CCT rendered highly accurate eyewitness competence estimates that mirrored the amount of information available in the video. Moreover, CCT estimates resulted in a more precise reconstruction of the video content than the majority rule did. This was true for group sizes ranging from 4 to 15 eyewitnesses, with the difference being more pronounced for larger groups. Thus, through simultaneous consideration of multiple witness statements, CCT provides a new approach to the assessment of eyewitness accuracy that outperforms standard methods of information aggregation.