Behind the confession: Relating false confession, interrogative compliance, personality traits, and psychopathy

The present study further supports the established notion that personality traits contribute to the phenomenon of false confessions and compliance in an interrogative setting. Furthermore, the study provides an investigation into the more recent interest in the potential effect of psychopathic traits in this context. A sample of university students (N = 607) completed questionnaires measuring psychopathic traits, interrogative compliance, and the big five personality factors. Of these, only 4.9% (n=30) claimed to have falsely confessed to an academic or criminal offense, with no participant taking the blame for both types of offense. Across measures the big five personality traits were the strongest predictors of compliance. The five personality traits accounted for 17.9 % of the total variance in compliance, with neuroticism being the strongest predictor, followed by openness and agreeableness. Psychopathy accounted for 3.3% of variance, with the lifestyle facet being the only significant predictor. After controlling for the big five personality factors, psychopathy only accounted for a small percentage of interrogative compliance, indicating that interrogators should take into account a person’s personality traits during the interrogation.N/

Protecting eyewitness evidence: Examining the efficacy of a self-administered interview tool

Given the crucial role of eyewitness evidence, statements should be obtained as soon as possible after an incident. This is not always achieved due to demands on police resources. Two studies trace the development of a new tool, the Self-Administered Interview (SAI), designed to elicit a comprehensive initial statement. In Study 1, SAI participants reported more correct details than participants who provided a free recall account, and performed at the same level as participants given a Cognitive Interview. In Study 2, participants viewed a simulated crime and half recorded their statement using the SAI. After a delay of 1 week, all participants completed a free recall test. SAI participants recalled more correct details in the delayed recall task than control participants

Police Strategies and Suspect Responses in Real-Life Serious Crime Interviews

This research focuses exclusively on real-life taped interviews with serious crime suspects and examines the strategies used and types of questions asked by police, and suspects’ responses to these. The information source was audio-tape-recorded interviews with 56 suspects. These recordings were obtained from 11 police services across England and Wales and were analysed using a specially designed coding frame. It was found that interviewers employed a range of strategies with presentation of evidence and challenge the most frequently observed. Closed questions were by far the most frequently used, and open questions, although less frequent, were found to occur more during the opening phases of the interviews. The frequency of ineffective question types (e.g. negative, repetitive, multiple) was low. A number of significant associations were observed between interviewer strategies and suspect responses. Rapport/empathy and open-type questions were associated with an increased likelihood of suspects admitting the offence whilst describing trauma, and negative questions were associated with a decreased likelihood

Where bias begins: a snapshot of police officers’ beliefs about factors that influence the investigative interview with suspects

The aim of the current study was to obtain a snapshot of police officer’s beliefs about factors that may influence the outcome of the investigative interview with suspects. We created a 26-item survey that contained statements around three specific themes: best interview practices, confessions and interviewee vulnerabilities. Police officers (N = 101) reported their beliefs on each topic by indicating the level of agreement or disagreement with each statement. The findings indicated that this sample of officers held beliefs that were mostly consistent with the literature. However, many officers also responded in the mid-range (neither agree nor disagree) which may indicate they are open to developing literature-consistent beliefs of the topics. Understanding what officers believe about factors within the investigative interview may have implications for future training. It may also help explain why some officers do not consistently apply best practices (i.e. strong counterfactual beliefs) versus officers who reliably apply literature-consistent practices to their interviews (i.e. knowledge-consistent beliefs).This research is supported by a fellowship awarded from the Erasmus Mundus Joint Doctorate Program, The House of Legal Psychology (EMJD-LP) with Framework Partnership Agreement (FPA) 2013-0036 and Specific Grant Agreement (SGA) 2015-1610 awarded to Nicole Adams.Published onlin

Using Bayesian networks to guide the assessment of new evidence in an appeal case.

When new forensic evidence becomes available after a conviction there is no systematic framework to help lawyers to determine whether it raises sufficient questions about the verdict in order to launch an appeal. This paper presents such a framework driven by a recent case, in which a defendant was convicted primarily on the basis of audio evidence, but where subsequent analysis of the evidence revealed additional sounds that were not considered during the trial. The framework is intended to overcome the gap between what is generally known from scientific analyses and what is hypothesized in a legal setting. It is based on Bayesian networks (BNs) which have the potential to be a structured and understandable way to evaluate the evidence in a specific case context. However, BN methods suffered a setback with regards to the use in court due to the confusing way they have been used in some legal cases in the past. To address this concern, we show the extent to which the reasoning and decisions within the particular case can be made explicit and transparent. The BN approach enables us to clearly define the relevant propositions and evidence, and uses sensitivity analysis to assess the impact of the evidence under different assumptions. The results show that such a framework is suitable to identify information that is currently missing, yet clearly crucial for a valid and complete reasoning process. Furthermore, a method is provided whereby BNs can serve as a guide to not only reason with incomplete evidence in forensic cases, but also identify very specific research questions that should be addressed to extend the evidence base and solve similar issues in the future.This research was funded by the Engineering and Physical Sciences Research Council of the UK through the Security Science Doctoral Research Training Centre (UCL SECReT) based at University College London (EP/G037264/1), and the European Research Council (ERC-2013-AdG339182-BAYES_KNOWLEDGE)

What People Believe about How Memory Works: A Representative Survey of the U.S. Population

Incorrect beliefs about the properties of memory have broad implications: The media conflate normal forgetting and inadvertent memory distortion with intentional deceit, juries issue verdicts based on flawed intuitions about the accuracy and confidence of testimony, and students misunderstand the role of memory in learning. We conducted a large representative telephone survey of the U.S. population to assess common beliefs about the properties of memory. Substantial numbers of respondents agreed with propositions that conflict with expert consensus: Amnesia results in the inability to remember one's own identity (83% of respondents agreed), unexpected objects generally grab attention (78%), memory works like a video camera (63%), memory can be enhanced through hypnosis (55%), memory is permanent (48%), and the testimony of a single confident eyewitness should be enough to convict a criminal defendant (37%). This discrepancy between popular belief and scientific consensus has implications from the classroom to the courtroom

GENN: A GEneral Neural Network for Learning Tabulated Data with Examples from Protein Structure Prediction

We present a GEneral Neural Network (GENN) for learning trends from existing data and making predictions of unknown information. The main novelty of GENN is in its generality, simplicity of use, and its specific handling of windowed input/output. Its main strength is its efficient handling of the input data, enabling learning from large datasets. GENN is built on a two-layered neural network and has the option to use separate inputs–output pairs or window-based data using data structures to efficiently represent input–output pairs. The program was tested on predicting the accessible surface area of globular proteins, scoring proteins according to similarity to native, predicting protein disorder, and has performed remarkably well. In this paper we describe the program and its use. Specifically, we give as an example the construction of a similarity to native protein scoring function that was constructed using GENN. The source code and Linux executables for GENN are available from Research and Information Systems at http://mamiris.com and from the Battelle Center for Mathematical Medicine at http://mathmed.org. Bugs and problems with the GENN program should be reported to EF