Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

The validity of self-report psychopathy assessment has been questioned, especially in forensic settings where clinical evaluations influence critical decision-making (e.g., institutional placement, parole eligibility). Informant-based assessment offers a potentially valuable supplement to self-report but is challenging to acquire in under-resourced forensic contexts. The current study evaluated, within an incarcerated sample (n = 322), the extent to which brief prototype-based informant ratings of psychopathic traits as described by the triarchic model (boldness, meanness, disinhibition; Patrick et al., 2009) converge with self-report trait scores and show incremental validity in predicting criterion measures. Self/informant convergence was robust for traits of boldness and disinhibition, but weaker for meanness. Informant-rated traits showed incremental predictive validity over self-report traits, both within and across assessment domains. These findings indicate that simple prototype-based informant ratings of the triarchic traits can provide a useful supplement to self-report in assessing psychopathy within forensic-clinical settings.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9297945PMC
http://dx.doi.org/10.1002/bsl.2542DOI Listing

Publication Analysis

Top Keywords

prototype-based informant
12
informant ratings
12
ratings triarchic
8
validity self-report
8
supplement self-report
8
traits
6
self-report
5
evaluating validity
4
validity prototype-based
4
triarchic psychopathy
4

Similar Publications

To address the challenging problem of multi-scale inshore-offshore ship detection in synthetic aperture radar (SAR) remote sensing images, we propose a novel deep learning-based automatic ship detection method within the framework of compositional learning. The proposed method is supported by three pillars: context-guided region proposal, prototype-based model-pretraining, and multi-model ensemble learning. To reduce the false alarms induced by the discrete ground clutters, the prior knowledge of the harbour's layout is exploited to generate land masks for terrain delimitation.

View Article and Find Full Text PDF

Deep learning systems excel in closed-set environments but face challenges in open-set settings due to mismatched label spaces between training and test data. Generalized category discovery (GCD) is one of such real-world open-set learning problems. In GCD, given a dataset, only a subset of samples is labeled.

View Article and Find Full Text PDF

Lensless on-chip microscopy imaging draws widespread attention owing to its evident advantages in simple optical structure, aberration-free imaging, wide field-of-view, and low-cost hardware budget, providing a fertile opportunity for disruptive reductions in cost and revolutionary improvements in portability for biomedical imaging applications. Here, we report a high-throughput pixel-super-resolved coded ptychographic microscopy implemented using a color image sensor. However, the color filtering array (CFA) introduces inherent modulation in the diffraction patterns acquired under monochromatic illumination, leading to spectral crosstalk in the data processing for lensless on-chip imaging.

View Article and Find Full Text PDF

Lifelong person re-identification (LReID) suffers from the catastrophic forgetting problem when learning from non-stationary data streams. Existing exemplar-based and knowledge distillation-based LReID methods encounter data privacy and limited acquisition capacity, respectively. In this paper, we introduce the prototype, which is under-investigated in LReID, to better balance knowledge retention and acquisition.

View Article and Find Full Text PDF

Monitoring species' presence in an ecosystem is crucial for conservation and understanding habitat diversity, but can be expensive and time consuming. As a result, ecologists have begun using the DNA that animals naturally leave behind in water or soil (called environmental DNA, or eDNA) to identify the species present in an environment. Recent work has shown that when used to identify species, convolutional neural networks (CNNs) can be as much as 150 times faster than ObiTools, a traditional method that does not use deep learning.

View Article and Find Full Text PDF