The use of contextual information in forensic toxicology: An international survey of toxicologists' experiences

Law and order effects: on cognitive dissonance and belief perseverance

Order of evidence presentation affects the evaluation and the integration of evidence in mock criminal cases. In this study, we aimed to determine whether the order in which incriminating and exonerating evidence is presented influences cognitive dissonance and subsequent display of confirmation bias. Law students (N = 407) were presented with a murder case vignette, followed by incriminating and exonerating evidence in various orders. Contrary to a predicted primacy effect (i.e. early evidence being most influential), a recency effect (i.e. late evidence being most influential) was observed in ratings of likelihood of the suspect's guilt. The cognitive dissonance ratings and conviction rates were not affected by the order of evidence presentation. The effects of evidence presentation order may be limited to specific aspects of legal decisions. However, there is a need to replicate the results using procedures and samples that are more representative of real-life criminal law trials.

What do radiologists look for? Advances and limitations of perceptual learning in radiologic search

Supported by guidance from training during residency programs, radiologists learn clinically relevant visual features by viewing thousands of medical images. Yet the precise visual features that expert radiologists use in their clinical practice remain unknown. Identifying such features would allow the development of perceptual learning training methods targeted to the optimization of radiology training and the reduction of medical error. Here we review attempts to bridge current gaps in understanding with a focus on computational saliency models that characterize and predict gaze behavior in radiologists. There have been great strides toward the accurate prediction of relevant medical information within images, thereby facilitating the development of novel computer-aided detection and diagnostic tools. In some cases, computational models have achieved equivalent sensitivity to that of radiologists, suggesting that we may be close to identifying the underlying visual representations that radiologists use. However, because the relevant bottom-up features vary across task context and imaging modalities, it will also be necessary to identify relevant top-down factors before perceptual expertise in radiology can be fully understood. Progress along these dimensions will improve the tools available for educating new generations of radiologists, and aid in the detection of medically relevant information, ultimately improving patient health.

Organizational and Human Factors Affecting Forensic Decision-Making: Workplace Stress and Feedback

Although forensic examiners operate in a stressful environment, there is a lack of understanding about workplace stress and feedback. These organizational and human factors can potentially impact forensic science judgments. In this study, 150 practicing forensic examiners from one laboratory were surveyed about their experiences of workplace stress, and the explicit and implicit feedback they receive. Forensic examiners reported that their high stress levels originated more from workplace-related factors (management and/or supervision, backlogs, and the pressure to do many cases) than from personal related factors (family, medical, and/or financial). The findings showed that a few (8%) of the forensic examiners sometimes felt strong implicit feedback about what conclusions were expected from them and that some (14%) also strongly felt that they were more appreciated when they helped to solve a case (e.g., by reaching a "match" as opposed to an "inconclusive" conclusion). Differences were found when comparing workplace stress and feedback levels across three core forensic science fields (forensic biology, chemistry, and latent prints) and across career stages (early, mid, and late). Gaining insights into the stress factors within a workplace and explicit and implicit feedback has implications for developing policies to improve the well-being, motivation, and performance of forensic examiners.

The effect of contextual information on decision-making in forensic toxicology

The impact of cognitive bias on decisions in forensic science has been demonstrated in numerous disciplines such as DNA and fingerprints, but has not been empirically investigated in the more objective domains, such as forensic toxicology. In the first experiment, participants (n = 58) were affected by irrelevant case information when analysing data from an immunoassay test for opiate-type drugs. In the second experiment, participants (n = 53) were biased in their choice of tests, for example, the age of the deceased impacted testing strategy: for older people, medicinal drugs were commonly chosen, whereas for younger people drugs of abuse were selected. Based on the results that examiners analyzing case data may have biases if they are given access to case context, we propose that examiners analysing presumptive test data are blind to irrelevant contextual information. Furthermore, that forensic toxicology laboratories use a consistent protocol for selecting tests, and that any deviations are documented and justified.

Cognitive and Human Factors in Expert Decision Making: Six Fallacies and the Eight Sources of Bias

Fallacies about the nature of biases have shadowed a proper cognitive understanding of biases and their sources, which in turn lead to ways that minimize their impact. Six such fallacies are presented: it is an ethical issue, only applies to "bad apples", experts are impartial and immune, technology eliminates bias, blind spot, and the illusion of control. Then, eight sources of bias are discussed and conceptualized within three categories: (A) factors that relate to the specific case and analysis, which include the data, reference materials, and contextual information, (B) factors that relate to the specific person doing the analysis, which include past experience base rates, organizational factors, education and training, and personal factors, and lastly, (C) cognitive architecture and human nature that impacts all of us. These factors can impact what the data are (e.g., how data are sampled and collected, or what is considered as noise and therefore disregarded), the actual results (e.g., decisions on testing strategies, how analysis is conducted, and when to stop testing), and the conclusions (e.g., interpretation of the results). The paper concludes with specific measures that can minimize these biases.