Can bottom-up processes of attention be a source of ‘interference’ in situations where top-down control of attention is crucial?

In this study, we investigate whether emotionally engaged bottom-up processes of attention can be a source of ‘interference’ in situations where top-down control of attention is necessary. Participants were asked to monitor and report on a video of a war scenario showing a developing battle in two conditions: emotionally positive and emotionally negative. Half of the participants (n = 15) were exposed to task-irrelevant pictures of positive emotional valence embedded within the scenario; the other half were exposed to task-irrelevant pictures of negative emotional valence. Sensitivity and Bias scores were calculated using signal detection theory. Overall, task accuracy scores were dependent upon the valence; negative pictures had an adverse effect on performance, whereas positive pictures improved performance. We concluded that negative emotional pictures interfered with top-down control of attention by attracting competing bottom-up processes of attention. We found the opposite effect for positive emotional stimuli.     

Drawing what lies ahead: False intentions are more abstractly depicted than true intentions

The aim of this study was to examine how people mentally represent and depict true and false statements about claimed future actions—so‐called true and false intentions. On the basis of construal level theory, which proposes that subjectively unlikely events are more abstractly represented than likely ones, we hypothesized that false intentions should be represented at a more abstract level than true intentions. Fifty‐six hand drawings, produced by participants to describe mental images accompanying either true or false intentions, were rated on level of abstractness by a second set of participants (N = 117) blind to the veracity of the intentions. As predicted, drawings of false intentions were rated as more abstract than drawings of true intentions. This result advances the use of drawing‐based deception detection techniques to the field of true and false intentions and highlights the potential for abstractness as a novel cue to deceit.     

Facilitating memory‐based lie detection in immediate and delayed interviewing: The role of mnemonics

We experimentally investigated how different mnemonic techniques employed in an interview conducted immediately after an event affected truth tellers' and liars' responses when they were interviewed again after a 2‐week delay. We also compared how verbal accounts changed over time within truth tellers and liars, and how consistent both groups were. Participants (n = 143) were shown a mock intelligence operation video and instructed either to tell the truth or lie about its contents in two interviews, one of which was immediately after watching the video and the other after a 2‐week delay. In the immediate interview, they were asked to provide a free recall and then asked to provide further information via one of three mnemonics: context reinstatement, sketch, or event‐line. In the delayed interview, they were asked to provide only a free recall. Truth tellers reported more visual, spatial, temporal, and action details than did liars both immediately and after a delay. Truth tellers experienced more of a decline in reporting details after a delay than did liars, and this decline was affected by the mnemonic used. Truth tellers thus showed, more than liars, patterns of reporting indicative of genuine memory decay. Liars produced patterns of a “stability bias” instead. Truth tellers and liars were equally consistent between their immediate and delayed statements.     

To see dynamic change: continuous focused attention facilitates change detection,but the effect persists briefly

What is the role of continuously focused attention on an object in change detection? To ensure focused attention on one object, we conducted a single object change detection task, manipulating an object’s location between pre-change and post-change displays (same or different location), and also manipulating a blank duration (the FOD task) and a pre-change object presentation duration (the FBD task). If attention is continuously focused at the spatial location of the pre-change object, a location shift of the post-change object should interrupt change detection due to a cognitive cost of attentional shift. Results suggest attention is focused continuously for a brief blank duration, and attention can facilitate the detection of change occurring at the location of attentional focus. Additionally, although attention is focused continuously for a long time if a target is visible, the effect of attention declines with time. The results clarify the new temporal characteristics of focused attention.     

Wild facial expression recognition based on incremental active learning

Facial expression recognition in a wild situation is a challenging problem in computer vision research due to different circumstances, such as pose dissimilarity, age, lighting conditions, occlusions, etc. Numerous methods, such as point tracking, piecewise affine transformation, compact Euclidean space, modified local directional pattern, and dictionary-based component separation have been applied to solve this problem. In this paper, we have proposed a deep learning–based automatic wild facial expression recognition system where we have implemented an incremental active learning framework using the VGG16 model developed by the Visual Geometry Group. We have gathered a large amount of unlabeled facial expression data from Intelligent Technology Lab (ITLab) members at Inha University, Republic of Korea, to train our incremental active learning framework. We have collected these data under five different lighting conditions: good lighting, average lighting, close to the camera, far from the camera, and natural lighting and with seven facial expressions: happy, disgusted, sad, angry, surprised, fear, and neutral. Our facial recognition framework has been adapted from a multi-task cascaded convolutional network detector. Repeating the entire process helps obtain better performance. Our experimental results have demonstrated that incremental active learning improves the starting baseline accuracy from 63% to average 88% on ITLab dataset on wild environment. We also present extensive results on face expression benchmark such as Extended Cohn-Kanade Dataset, as well as ITLab face dataset captured in wild environment and obtained better performance than state-of-the-art approaches.     

An illumination robust road detection method based on color names and geometric information

Road detection is a basic task in automated driving field. In existing methods, detecting lane marks is a frequently used approach. However, road marks could fade with time, or even do not exist on some roads. Considering these factors, road region segmentation from background is much more reliable. One of the difficulties in this area is dealing with variant illumination conditions. Existing methods make some progress on reducing the influence of shadows, but they are still not satisfactory. Taking this into account, we put forward an improved shadow-free road detecting method based on color names, whose performance exceeds the existing methods. In addition, to further improving our performance on application, we adopt vanishing point detection and fuse two confidence maps to reduce the interference of sidewalk regions. Experiments on KITTI dataset depict that the proposed method is efficient. Moreover, the improved road detection method has low-complexity, which meets the requirement of practical usage.     

Novel object detection and recognition system based on points of interest selection and SVM classification

Due to the progression in computer vision technology, object recognition systems have gained considerable research interest. Though there are numerous object recognition systems in the literature, there is always a constant demand for better object recognition systems. Taking this as a challenge, this work proposes a novel object recognition system based on points of interest and feature extraction. Initially, the points of interest of the image are selected by means of Derivative Kadir-Brady (DKB) detector and the neighbourhood pixels of a particular window size are selected for further processing. The gabor and curvelet features are extracted from the area of interest, followed by the Support Vector Machine (SVM) classification. The performance of the proposed object recognition system is evaluated against three analogous techniques in terms of accuracy, precision, recall and F-measure. On experimental analysis, it is proven that the proposed approach outperforms the existing approaches and the performance of the proposed work is satisfactory.     

When white collar criminals turn to fatal violence: The impact of narcissism and psychopathy

The present article takes as its point of departure Perri's view, according to whom the impact of personality traits such as psychopathy and narcissism has unduly been omitted in research on white collar criminality. However, this article argues that although such factors have an important role to play in some cases when white collar criminals turn violent in order to prevent the detection of their felonies, they constitute neither the necessary nor the sufficient conditions for white collar criminals to commit murders. This means on the one hand that, although potentially increasing the risk for violence, these traits are not by themselves sufficient as the triggers of violent acts and can function so only under certain social‐contextual circumstances. However, such personality traits are not by themselves necessary, as there can be empirical cases of fraud detection murder committed by white collar criminals lacking them. This position is supported by both a logical analysis of Perri's main arguments and by empirical evidence, consisting of a few real‐life cases of Scandinavian white collar criminals who have “turned red.”     

Empirically Defining Treatment Response and Remission in Body Dysmorphic Disorder Using a Short Self-Report Instrument

Determining response or remission status in body dysmorphic disorder (BDD) usually requires a lengthy interview with a trained clinician. This study sought to establish empirically derived cutoffs to define treatment response and remission in BDD using a brief self-report instrument, the Appearance Anxiety Inventory (AAI). Results from three clinical trials of BDD were pooled to create a sample of 123 individuals who had received cognitive-behavioral therapy for BDD, delivered via the Internet. The AAI was compared to gold-standard criteria for response and remission in BDD, based on the clinician-administered Yale–Brown Obsessive Compulsive Scale, modified for BDD (BDD-YBOCS), and evaluated using signal detection analysis. The results showed that a ≥ 40% reduction on the AAI best corresponded to treatment response, with a sensitivity of 0.71 and a specificity of 0.84. A score ≤ 13 at posttreatment was the optimal cutoff in determining full or partial remission from BDD, with a sensitivity of 0.75 and a specificity of 0.88. These findings provide benchmarks for using the AAI in BDD treatment evaluation when resource-intensive measures administered by clinicians are not feasible.     

The verifiability approach to deception detection: A preregistered direct replication of the information protocol condition of Nahari,Vrij, and Fisher (2014b)

Nahari, Vrij, and Fischer [(2014b), Applied Cognitive Psychology, 28, 122–128] found that, when participants were forewarned that their statements would be checked for verifiable details, truth tellers gave much more verifiable details than liars. In this direct replication (n = 72), participants wrote a statement claiming they had carried out their regular campus activities, whereas liars had actually stolen an exam. Statements were coded for verifiable details. Our primary prediction was confirmed: Truth tellers provided significantly more verifiable details than liars. Of note, the replication effect size (d = 0.49) was less than half that of the original (d = 1.14), and – like in the original study – was smaller than the lie‐truth effect size for total details (verifiable and unverifiable details combined; d = 0.80). We hope this will stimulate other independent investigations of VA to tell whether or not coding for verifiability will pass Ockham's razor test.