The Reverend Dr. James Curtis Hepburn was one of its founders and served as the first president. The novelist and poet Shimazaki Toson graduated from this college and wrote the lyrics of its college song..
OBJECTIVE:The strategic use of evidence (SUE) is a technique that aims to improve the ability to differentiate between liars and truth-tellers. However, while theoretical training provides guidance on interview techniques, it lacks opportunities for practical application. HYPOTHESES:We created two large language model driven artificial intelligence (AI) suspects with whom participants could simulate interviews and hypothesized that these simulations would enhance the transfer of training to later interactions with human mock suspects. METHOD:The study included 156 Chinese laypersons (78 interviewers and 78 human mock suspects). The two AI suspects followed response rules representing simplified and prototypical examples of liars' and truth-tellers' behaviors under the SUE model. Interviewers were randomly allocated to one of three types of training: (a) instruction and AI exercise, (b) instruction, and (c) control. After the training, the participants interacted with either a lying or truthful human mock suspect. RESULTS:Receiving interventions made interviewers use evidence-framing matrix (an important tactic within the SUE framework) more frequently, thereby eliciting more inconsistencies between the lying human mock suspects' statements and the evidence (i.e., evidence-statement inconsistencies) as well as more inconsistencies within their own statements (i.e., within-statement inconsistencies). Both instruction and instruction and AI exercise groups used evidence-statement (in)consistencies more to make their judgments about whether human mock suspects were lying or truthful compared to those in the control group. In addition, the instruction and AI exercise group was better at accurately judging whether the human mock suspects were lying or truthful compared to the control group. CONCLUSIONS:Overall, this study provided preliminary evidence that simulated SUE with AI suspects transferred to interactions with human mock suspects in a controllable experimental setting, but that the advantage over instruction-only was not particularly robust. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Objective Discrimination between EMG activity such as fibrillation potentials/positive sharp waves (Fib/PSW), end plate spikes (EPS), fasciculation potentials (FP), and contaminating voluntary motor unit potentials (MUP) is mandatory for EMG diagnosis. Discharge rhythm is the key for discrimination. We devised a new parameter, Random Index (RI), which quantifies the rhythm and takes a value from 0 to 1, smaller for regular trains of discharges. This study evaluated the utility of RI as well as modified versions of the regularity indices proposed in past reports. Methods EMG records of patients with amyotrophic lateral sclerosis were retrospectively reviewed. EPS were collected also from a healthy volunteer. The EMG activity was classified by an expert. RI and other regularity indices as well as the median instantaneous firing rate (IFRm) were calculated. Results Analyzed sequences were 73 Fib/PSW, 27 EPS, 24 FP, and 36 MUP. The four types were clearly separated over the 2-dimensional plots of regularity indices vs. IFRm. Especially, Fib/PSW and EPS were far separated in these plots. RI achieved significantly better discrimination between Fib/PSW and MUP than other indices. Conclusion RI is a robust tool for discriminating EMG activity. Significance RI and other regularity indices would be useful for educational purpose.
ABSTRACT We examined the simultaneous training of questioning skills and supportive statements through simulated child sexual abuse (CSA) interviews paired with feedback. Eighty inexperienced participants were divided into four groups: no feedback, feedback on question types and case outcomes, feedback on supportive statements, and the combination of all feedback types. Each participant conducted four online simulated CSA interviews with child avatars. Results showed that combined feedback improved both questioning skills and supportive statements, demonstrating the potential for simultaneous multi‐skill training. The proportion of recommended questions increased by 20%–30% on average, while supportive statements increased two‐ to four‐fold. However, combined training showed slightly lower improvements compared with single‐skill training suggesting the presence of a trade‐off. These findings highlight the importance of personalized feedback and suggest that initial separate training for single skills or additional interventions may enhance multi‐skill training effectiveness, contributing to more effective interviewer training programs.
Background and aimsInternet addiction (IA) and depression commonly co-occur in adolescents, yet the mechanisms underlying their comorbidity remain unclear. This study aims to elucidate the comorbidity mechanism through network analysis, identifying bridge symptoms linking IA and depression, and exploring sex differences. Additionally, the study examines the association between effortful control (EC) and bridge symptoms, providing insights for interventions.MethodsA school-based survey was conducted among 7th to 9th-grade students in Japan. Participants completed questionnaires assessing IA (measured by the Young Diagnostic Questionnaire), depression (measured by the Patient Health Questionnaire for Adolescents), and EC (measured by the Early Adolescent Temperament Questionnaire). Network analysis was employed to identify bridge symptoms and examine their association with EC. Bootstrapping for network analysis was conducted to assess network accuracy and stability as well as sex differences in the network structures.ResultsAmong the 4,111 students approached, 3,909 (1,904 male and 2,005 female) students filled out the survey. Bridge symptoms such as “Escape” (from the IA cluster) and “Concentration” (from the depression cluster) were found important in both male and female students. Our analysis also revealed differences in the importance of the bridge symptoms across males and females with “Psychomotor” symptoms (from the depression cluster) predominantly in males and “Feeling Guilty” (from the depression cluster) and “Functional impairment” (from the IA cluster) predominantly in females. EC showed a notable negative association with “Concentration”, suggesting important relationships between the transdiagnostic factor and bridge symptoms in understanding the comorbid conditions. The network comparison test did not reveal significant differences in the network structures across sexes.Discussion and conclusionsThe study revealed differences in bridge symptoms linking IA and depression between male and female students. Our findings provide valuable insights for understanding the comorbidity mechanisms of IA and depression in adolescents. Further research using a longitudinal study design is warranted to identify the directionality between EC and bridge symptoms.
PurposeThe present study compared the performance of a Large Language Model (LLM; ChatGPT) and human interviewers in interviewing children about a mock-event they witnessed.MethodsChildren aged 6-8 (N = 78) were randomly assigned to the LLM (n = 40) or the human interviewer condition (n = 38). In the experiment, the children were asked to watch a video filmed by the researchers that depicted behavior including elements that could be misinterpreted as abusive in other contexts, and then answer questions posed by either an LLM (presented by a human researcher) or a human interviewer.ResultsIrrespective of condition, recommended (vs. not recommended) questions elicited more correct information. The LLM posed fewer questions overall, but no difference in the proportion of the questions recommended by the literature. There were no differences between the LLM and human interviewers in unique correct information elicited but questions posed by LLM (vs. humans) elicited more unique correct information per question. LLM (vs. humans) also elicited less false information overall, but there was no difference in false information elicited per question.ConclusionsThe findings show that the LLM was competent in formulating questions that adhere to best practice guidelines while human interviewers asked more questions following up on the child responses in trying to find out what the children had witnessed. The results indicate LLMs could possibly be used to support child investigative interviewers. However, substantial further investigation is warranted to ascertain the utility of LLMs in more realistic investigative interview settings.
