The 6 nonoverlapping primary scales of the Structured Interview of Reported Symptoms (SIRS) were subjected to taxometric analysis in a group of 1,211 criminal and civil examinees in order to investigate the latent structure of feigned psychopathology. Both taxometric procedures used in this study, mean above minus below a cut (MAMBAC) and maximum covariance (MAXCOV), produced dimensional results. A subgroup of participants (n = 711) with valid Minnesota Multiphasic Personality Inventory-2 (MMPI-2) protocols were included in a second round of analyses in which the 6 nonoverlapping primary scales of the SIRS and the Infrequency (F), Infrequency-Psychopathology (Fp), and Dissimulation (Ds) scales of the MMPI-2 served as indicators. Again, the results were more consistent with dimensional latent structure than with taxonic latent structure. On the basis of these findings, it is concluded that feigned psychopathology forms a dimension (levels of fabrication or exaggeration) rather than a taxon (malingering-honest dichotomy) and that malingering is a quantitative distinction rather than a qualitative one. The theoretical and clinical practice implications of these findings are discussed.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1037/1040-3590.20.3.238 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Evolutionary Novelties in Bacteria and the Missing Backdrop of the Environment.
Environ Microbiol
January 2025
Trivedi School of BioSciences and Koita Centre for Digital Health, Ashoka University, Sonipat, India.
Evolutionary novelty has been one of the central themes in the field of evolutionary biology for many years. Structural and functional innovations such as scales in the reptiles, fins in the fishes and mammary glands in the mammals have been the focus of the studies. Insights obtained from these studies have shaped the criterion for the identification of novelty as well as provide the framework for studying novelty.
View Article and Find Full Text PDFGiven the same external input, one's understanding of that input can differ based on internal contextual knowledge. Where and how does the brain represent latent belief frameworks that interact with incoming sensory information to shape subjective interpretations? In this study, participants listened to the same auditory narrative twice, with a plot twist in the middle that dramatically shifted their interpretations of the story. Using a robust within-subject whole-brain approach, we leveraged shifts in neural activity between the two listens to identify where latent interpretations are represented in the brain.
View Article and Find Full Text PDFProteins drive biochemical transformations by transitioning through distinct conformational states. Understanding these states is essential for modulating protein function. Although X-ray crystallography has enabled revolutionary advances in protein structure prediction by machine learning, this connection was made at the level of atomic models, not the underlying data.
View Article and Find Full Text PDFDeveloping and validating a scale to measure perceived barriers to prosthodontics treatments among partially edentulous patients.
Front Oral Health
January 2025
Oral and Maxillofacial Surgery Department, Faculty of Dental Medicine, Umm Al-Qura University, Makkah, Saudi Arabia.
Background: People experiencing tooth loss need dental prostheses to preserve the integrity of their oral structures and replace the missing teeth. Patient-related outcome measures (PROMs) for perceived barriers to prosthodontics treatment are scarce in the literature.
Aims: The aim of this study was to develop a comprehensive scale to identify and measure barriers to prosthodontic treatment as perceived by partially edentulous patients.
Emotion analysis of EEG signals using proximity-conserving auto-encoder (PCAE) and ensemble techniques.
Cogn Neurodyn
December 2025
Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, TamilNadu India.
Emotion recognition plays a crucial role in brain-computer interfaces (BCI) which helps to identify and classify human emotions as positive, negative, and neutral. Emotion analysis in BCI maintains a substantial perspective in distinct fields such as healthcare, education, gaming, and human-computer interaction. In healthcare, emotion analysis based on electroencephalography (EEG) signals is deployed to provide personalized support for patients with autism or mood disorders.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!