What’s Left Unsaid : How Nonverbal Influence Compares with Verbal Influence

Eyewitnesses' memory reports can be altered when ambiguous post-event information is presented verbally during interviews. While recent research has identified that gestures can also act a source of influence in eyewitness interviews, it is unknown whether nonverbal suggestions can exert an influence to the same magnitude as those made verbally. To investigate this, 92 adults were interviewed about a crime video and provided with either verbal (speech) or nonverbal (gesture) suggestions during questioning that provided either factual or misleading information about the scene. The results revealed that both differed from controls, and that gestures exerted a similar level of influence as speech: As with speech, gestures led participants to giving both correct and incorrect responses. These results highlight that misinformation conveyed covertly through gestures as a form of suggestion that is comparable to overt verbal influence despite differences in the way in which they convey information.Peer reviewe

International collaboration clusters in Africa

Recent discussion about the increase in international research collaboration suggests a comprehensive global network centred around a group of core countries and driven by generic socio-economic factors where the global system influences all national and institutional outcomes. In counterpoint, we demonstrate that the collaboration pattern for countries in Africa is far from universal. Instead, it exhibits layers of internal clusters and external links that are explained not by monotypic global influences but by regional geography and, perhaps even more strongly, by history, culture and language. Analysis of these bottom-up, subjective, human factors is required in order to provide the fuller explanation useful for policy and management purposes.Comment: 12 pp, 5 Figs including map links to viewe

A nod in the wrong direction : Does nonverbal feedback affect eyewitness confidence in interviews?

Eyewitnesses can be influenced by an interviewer's behaviour and report information with inflated confidence as a result. Previous research has shown that positive feedback administered verbally can affect the confidence attributed to testimony, but the effect of non-verbal influence in interviews has been given little attention. This study investigated whether positive or negative non-verbal feedback could affect the confidence witnesses attribute to their responses. Participants witnessed staged CCTV footage of a crime scene and answered 20 questions in a structured interview, during which they were given either positive feedback (a head nod), negative feedback (a head shake) or no feedback. Those presented with positive non-verbal feedback reported inflated confidence compared with those presented with negative non-verbal feedback regardless of accuracy, and this effect was most apparent when participants reported awareness of the feedback. These results provide further insight into the effects of interviewer behaviour in investigative interviewsPeer reviewedFinal Accepted Versio

Handmade memories : The robustness of the gestural misinformaton effect in children's eyewitness interviews

This is an Accepted Manuscript. The final publication is available at Springer via https://doi.org/10.1007/s10919-015-0210-z.An interviewer’s gestures can convey misleading information and subsequently cause inaccuracies in the reporting of an event by both adults and children. We investigated the robustness of the gestural misinformation effect, examining the extent to which an interviewer’s gestures mislead children under conditions that would normally buffer them against verbal suggestibility (strength of memory trace, age, and verbal ability). Children (a younger sample aged 2–4 years, n = 30; and an older sample aged 7–9 years, n = 26) were exposed to a videotaped event and questioned immediately, having been allocated randomly to either an accurate gesture condition (gestures consistent with observed events, e.g., “What was the lady wearing?” plus a ‘hat’ gesture) or a misleading gesture condition (“What was the lady wearing?” plus a ‘gloves’ gesture). Children were susceptible to the gestural misinformation effect even when questioned immediately after witnessing the event, regardless of age and verbal ability. These findings reveal new insights into the robustness of the gestural misinformation effect in children’s eyewitness interviews.Peer reviewedFinal Accepted Versio

Believe it or not:Exploring the relationship between dogmatism and openness within non-religious samples

Personality and dogmatic thinking within religious individuals have been examined by previous research, but neglected for non-religious individuals. In this experiment, we distinguish between two types of non-religious groups; those who ascribe themselves to an identity (atheists) and those who do not (no beliefs in particular). A total of 103 non-religious individuals (36% atheists and 64% with no particular beliefs) completed an online questionnaire measuring dogmatism and openness traits, with an additional Christian group (n = 91) serving as a control. After confirming a relationship between identity salience and dogmatism, and validating a measure of dogmatism (DOG) in both non-religious groups, we note key personality differences between the two. Those with no beliefs in particular demonstrated a traditional negative correlation between openness and dogmatism (along with Christians) while these variables correlated positively for atheists (in particular, on ‘unconventionality’). This study is the first to establish differences between the relationship of dogmatism and openness within non-religious populations and explain these differences through group identity. Thus, identity strength and group belief systems are suggested to be key contributors to observed group differences between non-religious individualsPeer reviewe

The Saliency of Gestural Misinformation in the Perception of a Violent Crime

This is the accepted manuscript version of the following article: Daniel J. gurney, Louise R. Ellis & Emily Vardn-Hynard, ‘The saliencey of gestural misinformation in the perception of a violent crime’, Psychology, Crime & Law, Vol. 22(7): 651-665, first published online 18 April 2016. The version of record is available online via doi: http://dx.doi.org/10.1080/1068316X.2016.1174860 Published by Taylor & Francis Online.Recent research has revealed that misinformation from gestures can influence eyewitness memory. However, while the effects of verbal influence have been shown to have major impacts on prosecution, gestural misinformation is yet to demonstrate misinformation effects to this extent. To investigate the salience of suggestions provided nonverbally, and how these compare to those made verbally, two experiments were conducted. In Experiment 1, participants watched footage of a crime scene and were presented with one of two types of gestures during questioning that suggested different interpretations of the crime. The results confirmed that the gestures influenced responses with participants altering their interpretation of the crime according to the information gestured to them. Experiment 2 built on this to investigate how comparable this gestural influence was to verbal influence. The results revealed that gestural misinformation caused participants to alter their interpretation of the crime and elicited the same effects as verbal misinformation. Additionally, participants were less likely to have felt misled from gestures as they were from speech. These results reveal new insights into the strength of gestural misinformation and show that, despite their subtle nature in communication, gestures can exert a powerful influence in eyewitness interviews.Peer reviewedFinal Accepted Versio

The Stroke Neuro-Imaging Phenotype Repository: An open data science platform for stroke research

Stroke is one of the leading causes of death and disability worldwide. Reducing this disease burden through drug discovery and evaluation of stroke patient outcomes requires broader characterization of stroke pathophysiology, yet the underlying biologic and genetic factors contributing to outcomes are largely unknown. Remedying this critical knowledge gap requires deeper phenotyping, including large-scale integration of demographic, clinical, genomic, and imaging features. Such big data approaches will be facilitated by developing and running processing pipelines to extract stroke-related phenotypes at large scale. Millions of stroke patients undergo routine brain imaging each year, capturing a rich set of data on stroke-related injury and outcomes. The Stroke Neuroimaging Phenotype Repository (SNIPR) was developed as a multi-center centralized imaging repository of clinical computed tomography (CT) and magnetic resonance imaging (MRI) scans from stroke patients worldwide, based on the open source XNAT imaging informatics platform. The aims of this repository are to: (i) store, manage, process, and facilitate sharing of high-value stroke imaging data sets, (ii) implement containerized automated computational methods to extract image characteristics and disease-specific features from contributed images, (iii) facilitate integration of imaging, genomic, and clinical data to perform large-scale analysis of complications after stroke; and (iv) develop SNIPR as a collaborative platform aimed at both data scientists and clinical investigators. Currently, SNIPR hosts research projects encompassing ischemic and hemorrhagic stroke, with data from 2,246 subjects, and 6,149 imaging sessions from Washington University\u27s clinical image archive as well as contributions from collaborators in different countries, including Finland, Poland, and Spain. Moreover, we have extended the XNAT data model to include relevant clinical features, including subject demographics, stroke severity (NIH Stroke Scale), stroke subtype (using TOAST classification), and outcome [modified Rankin Scale (mRS)]. Image processing pipelines are deployed on SNIPR using containerized modules, which facilitate replicability at a large scale. The first such pipeline identifies axial brain CT scans from DICOM header data and image data using a meta deep learning scan classifier, registers serial scans to an atlas, segments tissue compartments, and calculates CSF volume. The resulting volume can be used to quantify the progression of cerebral edema after ischemic stroke. SNIPR thus enables the development and validation of pipelines to automatically extract imaging phenotypes and couple them with clinical data with the overarching aim of enabling a broad understanding of stroke progression and outcomes