Behavioral Sciences & the Law最新文献

This is a single-case study of Mohammed Atta that utilizes Theodore Millon's model of personality disorders to explore Atta's transformation from a markedly shy, sensitive, and gentle person who abhorred fanaticism and violence into one of the leaders of the 9/11 terrorist attacks. First, a brief review of Atta's life and personality is presented. This is followed by a delineation of three possible pathways that could explain his transformation. These pathways include what Millon identified as a puritanical compulsive personality, a selfless dependent personality, and a spineless denigrating personality. Because personality is complex and multifaceted, it is noted that Atta's transformation could have been a result of some combination of these three personality dynamics. The role of personality factors, in combination with life circumstances, in expanding our understanding of the radicalization process is discussed.

A series of pioneering studies from the late 1970s and early 1980s evidenced that the general public held grossly inaccurate estimations of the frequency and success of the insanity plea, as well as significantly negative attitudes towards the plea. Since the 1980s replications of these findings are nonexistent and, perhaps consequently, contemporary scholars continue to rely on the findings of these studies when discussing the general population’s understanding of the insanity defence. We replicated several major research findings from this body of literature by conducting a cross-sectional, online-administered, self-report study of 257 adults aged 18–65 within the general US population by administering the same questions as found in previous studies, and additional questions. Results support each of our hypotheses; compared to the two past representative studies, participants in this study believed that the insanity plea was used less frequently, was less successful when employed, was not as abused or overused, and was more acceptable as a defence. Throughout the paper we conjecture why attitudes and estimations may have shifted over the past 40 years. We also explicate how our results can be useful to lawyers or psychologists; primarily by elucidating attitudes and knowledge of the plea among prospective jurors.

A seminal BSL article in 1984 provided a conceptual framework for systematically applying detection strategies to feigned mental disorders. Over the decades, gradual advances have emerged for delineating and operationalizing these strategies. The first major section follows the developments in feigning strategies for the MMPI family and the SIRS/SIRS-2 as leading measures for multiscale inventories and structured interviews, respectively. The second major section critically reviews two unlikely detection strategies (i.e., rare symptoms and symptom combinations) and two amplified detection strategies (i.e., symptom severity and symptom selectivity). The article concludes with recommendations for future research on potential strategies.

A handwritten signature is one of the forms of a biometric measure that creates an individual identity of the persons to mark their approval related to any document. The manual examination for determination of the authenticity of the handwritten signatures is a common practice amongst forensic document examiners. This process involves a detailed and thorough analysis of handwriting characteristics of an individual ensuring a comprehensive assessment of the each and every important feature. However, the use of artificial intelligence tools can reduce this manual work of experts for identifying forgery in signatures. The main objective of the present study was to classify the handwritten signatures as forged and genuine, manually as well as using tools of artificial intelligence, especially machine learning (ML) methods. A total of 1400 signatures, consisting of 700 forged and 700 real signatures were obtained. The signatures were obtained from 71 participants; one writer executed 700 signatures (real/genuine signatures) and 70 participants were asked to forge 10 signatures each by observing one genuine signature selected from a pool of 700 real signatures. The study employed two methods to examine the signatures: manual examination and by using machine learning-based models. In the manual examination, thorough comparison between real and forged signatures revealed that all the forged signatures were imitated and falsified that is not created by the original creator. In contrast, the machine learning-based models that is support vector machine (SVM) and random forest classifier (RFC) were utilized for classifying the signatures as either forged or genuine. The RFC and SVM achieved accuracies of 92% and 89.64% respectively for classification of the signatures as real or forged. Accuracy of both the models of the machine learning approach revealed that the approach may be used to reduce the manual work of forensic handwriting experts and allow this examination to be performed more quickly. However, the admissibility of AI-based examination of signatures is still challenged due to the lack of universal standards and a regulatory framework.