The sibling familiarity effect: Is within?person facial variability shared across siblings?

Humans are experts at familiar face recognition, but poor at unfamiliar face recognition. Familiarity is created when a face is encountered across varied conditions, but the way in which a person's appearance varies is identity?specific, so familiarity with one identity does not benefit recognition of other individuals. However, the faces of biological siblings share structural similarities, so we explored whether the benefits of familiarity are shared across siblings. Results show that familiarity with one half of a sibling pair improves kin detection (experiment 1), and that unfamiliar face matching is more accurate when targets are the siblings of familiar versus unfamiliar individuals (experiment 2). PCA applied to facial images of celebrities and their siblings demonstrates that faces are generally better reconstructed in the principal components of a same?sex sibling than those of an unrelated individual. When we encounter the unfamiliar sibling of someone we already know, our pre?existing representation of their familiar relation may usefully inform processing of the unfamiliar face. This can benefit both kin detection and identity processing, but the benefits are constrained by the degree to which facial variability is shared.

An international survey of applied face-matching training courses.

Face matching decisions in applied settings are typically carried out by trained face-matching professionals, known as facial reviewers and facial examiners. Recent research has demonstrated that short professional face-matching training courses are limited in improving face-matching accuracy, however, despite the existence of high-level training guidelines produced by the practitioner community the content, duration and delivery of training for facial reviewers and facial examiners are not widely understood in the academic research community. The current study aimed to address this gap in the scientific literature to better understand how different agencies train facial reviewers and facial examiners, using results collected from an international survey. The survey included questions on the duration, delivery and content of an agencies face-matching training. 24 face-matching agencies from five continents took part in the survey. A quantitative analysis of the survey results revealed that at the group level facial examiners received longer durations of training, covering more topics than reviewers and more frequently included one-to-one mentoring. However, the survey revealed large differences in the duration, delivery methods and content of training by individual agencies at both the facial review and facial examiner level and low inclusion of evidence-based training practices. These results should help researchers to better understand the diversity in training practices and durations of training between different agencies and may help explain the individual differences observed in the performance of face matching professionals in the literature.

Digital Detectives: Websleuthing Reduces Eyewitness Identification Accuracy in Police Lineups.

Eyewitnesses to crimes sometimes search for a culprit on social media before viewing a police lineup, but it is not known whether this affects subsequent lineup identification accuracy. The present online study was conducted to address this. Two hundred and eighty-five participants viewed a mock crime video, and after a 15-20 min delay either (i) viewed a mock social media site including the culprit, (ii) viewed a mock social media site including a lookalike, or (iii) completed a filler task. A week later, participants made an identification from a photo lineup. It was predicted that searching for a culprit on social media containing the lookalike (rather than the culprit) would reduce lineup identification accuracy. There was a significant association between social media exposure and lineup accuracy for the Target Present lineup (30% more of the participants who saw the lookalike on social media failed to positively identify the culprit than participants in the other conditions), but for the Target Absent lineup (which also included the lookalike) there was no significant association with lineup identification accuracy. The results suggest that if an eyewitness sees a lookalike (where they are expecting to see the culprit) when conducting a self-directed search on social media, they are less likely to subsequently identify the culprit in the formal ID procedure.

Recognising Familiar Faces Out of Context.

Expertise in familiar face recognition has been well-documented in several studies. Here, we examined the role of context using a surprise lecturer recognition test. Across two experiments, we found few students recognised their lecturer when they were unexpected, but accuracy was higher when the lecturer was preceded by a prompt. Our findings suggest that familiar face recognition can be poor in unexpected contexts.

Entitlement to Tell on Police Facebook Sites.

Despite the increased use of social media by the police, little qualitative exploration has been carried out regarding how online spaces such as Facebook might be important sites for Police Force identity. This study qualitatively analyzes a popular story told on the official Facebook site of a rural police force. It analyzes the story which is co-created by both the police and the public, and looks at how identity is created for the police through this online activity. The research finds that entitlement to tell particular stories is hotly challenged by the community and raises important questions regarding the perceived ownership of experiences in the criminal justice system. It concludes that Facebook posts might be important sites of feedback for police forces. Additionally, such sites may play an important role in trust-building and community engagement, but to realize this opportunity may require significant investment in terms of resources.

Big data and ergonomics methods: A new paradigm for tackling strategic transport safety risks.

Big data collected from On-Train Data Recorders (OTDR) has the potential to address the most important strategic risks currently faced by rail operators and authorities worldwide. These risk issues are increasingly orientated around human performance and have proven resistant to existing approaches. This paper presents a number of proof of concept demonstrations to show that long standing ergonomics methods can be driven from big data, and succeed in providing insight into human performance in a novel way. Over 300 ergonomics methods were reviewed and a smaller sub-set selected for proof-of-concept development using real on-train recorder data. From this are derived nine candidate Human Factors Leading Indicators which map on to all of the psychological precursors of the identified risks. This approach has the potential to make use of a significantly underused source of data, and enable rail industry stakeholders to intervene sooner to address human performance issues that, via the methods presented in this paper, are clearly manifest in on-train data recordings. The intersection of psychological knowledge, ergonomics methods and big data creates an important new framework for driving new insights.

Can Link Analysis Be Applied to Identify Behavioral Patterns in Train Recorder Data?

OBJECTIVE: A proof-of-concept analysis was conducted to establish whether link analysis could be applied to data from on-train recorders to detect patterns of behavior that could act as leading indicators of potential safety issues. BACKGROUND: On-train data recorders capture data about driving behavior on thousands of routine journeys every day and offer a source of untapped data that could be used to offer insights into human behavior. METHOD: Data from 17 journeys undertaken by six drivers on the same route over a 16-hr period were analyzed using link analysis, and four key metrics were examined: number of links, network density, diameter, and sociometric status. RESULTS: The results established that link analysis can be usefully applied to data captured from on-vehicle recorders. The four metrics revealed key differences in normal driver behavior. These differences have promising construct validity as leading indicators. CONCLUSION: Link analysis is one method that could be usefully applied to exploit data routinely gathered by on-vehicle data recorders. It facilitates a proactive approach to safety based on leading indicators, offers a clearer understanding of what constitutes normal driving behavior, and identifies trends at the interface of people and systems, which is currently a key area of strategic risk. APPLICATION: These research findings have direct applications in the field of transport data monitoring. They offer a means of automatically detecting patterns in driver behavior that could act as leading indicators of problems during operation and that could be used in the proactive monitoring of driver competence, risk management, and even infrastructure design.