Cognitive coherence in the evluation of a novel single item

Article published in Judgement and Decision-Makin

Using artificial bat sonar neural networks for complex pattern recognition: recognizing faces and the speed of a moving target

Two sets of studies examined the viability of using bat-like sonar input for artificial neural networks in complex pattern recognition tasks. In the first set of studies, a sonar neural network was required to perform two face recognition tasks. In the first task, the network was trained to recognize different faces regardless of facial expressions. Following training, the network was tested on its ability to generalize and correctly recognize faces using echoes of novel facial expressions that were not included in the training set. The neural network was able to recognize novel echoes of faces almost perfectly (above 96% accuracy) when it was required to recognize up to five faces. In the second face recognition task, a sonar neural network was trained to recognize the sex of 16 faces (eight males and eight females). After training, the network was able to correctly recognize novel echoes of those faces as 'male' or as 'female' faces with accuracy levels of 88%. However, the network was not able to recognize novel faces as 'male' or 'female' faces. In the second set of studies, a sonar neural network was required to learn to recognize the speed of a target that was moving towards the viewer. During training, the target was presented in a variety of orientations, and the network's performance was evaluated when the target was presented in novel orientations that were not included in the training set. The different orientations dramatically affected the amplitude and the frequency composition of the echoes. The neural network was able to learn and recognize the speed of a moving target, and to generalize to new orientations of the target. However, the network was not able to generalize to new speeds that were not included in the training set. The potential and limitations of using bat-like sonar as input for artifical neural networks are discussed

Legal psychologists as experts: guidelines for minimizing bias

Legal psychologists’ assessments can have a major impact on the fact finder’s evaluation of evidence and, consequently, perceptions of guilt. Yet, in the few studies about legal psychologists’ assessments and reports, great variability was found. As is the case with other forensic expert domains, legal psychologists are prone to cognitive biases, such as being adversely affected by irrelevant contextual information, confirmation bias, and allegiance bias. Based on the scientific literature, we propose several ways in which legal psychologists can minimize cognitive biases in their assessments, most notably the alternative scenario method. Furthermore, we propose guidelines for expert witnesses in the legal psychological domain, designed to make reports as scientifically grounded, applicable, readable, transparent, and bias-free as possible. We hope that the guidelines will enhance the quality of expert witness testimony provided by legal psychologists around the world