Validity and temporal stability of the chimeric face technique for studying hemispheric processing asymmetries: Data from 6-through 14-year-old children

The validity and temporal stability of the chimeric face technique as a measure of cerebral hemispheric functioning in children were investigated. The method is based upon the observation that when a subject encounters a representation of the human face, the left side (from the observer's point of view) of the face dominates his or her perception of it. This bias was shown to be present in a majority of children ranging from 6 through 14 years old, and it was stronger for right-handers than for left-handers. The 1-month temporal stability of the bias was 67%, which is comparable to that of other surface methods. As in other split-message techniques, the typical left visual field bias was more reliable than the unusual right visual field bias. The implications of these data are discussed.     

Analytic practice: Convergences and divergences1

The author discusses current convergences and divergences concerning analytic practice. After presenting a clinical vignette that can be understood differently according to different theoretical approaches, he discusses Wallerstein’s proposal of a common ground in psychoanalysis and suggests that the present state of the art indicates that psychoanalysis is a pluralistic discipline, with different ways of training and practising it, and that the main challenge is to improve our ability to listen to and to learn from different approaches.     

Keyboard dynamics discrepancies between baseline and deceptive eyewitness narratives

Information manipulation and cognitive load imposition make the production of deceptive narratives difficult. But little is known about the production of deception, and how its mechanisms may help distinguish truthful from deceitful narratives. This study focuses on the measurement of keystroke dynamics while typing truthful and deceptive eyewitness testimonies after a baseline assessment. While typing their narrative, some participants would undergo an auditory cognitive load. Results show that liars typed their story slower, and in less time than the truthful participants when compared to their respective baselines. The imposition of the auditory cognitive load showed adverse results, enhancing the amount of keystrokes and the time necessary to type the narrative. Classification shows better results for deceptive narrative when no auditory cognitive load is imposed. These results are discussed in terms of expanding current models to include the cognition of linguistic production and writing strategies.     

Adults' difficulties in identifying concealment among children interviewed with the putative confession instructions

The putative confession (PC) instructions (“[suspect] told me everything that happened and wants you to tell the truth”) increases children's honesty. However, research has shown that children who maintain secrecy despite the PC are more convincing. We examined whether (a) the PC undermines adults' deception detection abilities or (b) children who conceal despite the PC are better deceivers. Adults evaluated truthful and deceptive children interviewed with the PC where the PC portion of the interview was either present or absent. Adults' deception detection was no worse when the PC was present; in fact, it was slightly better. Rather than negatively affecting adults' ability to detect deception, the PC selects an unusually convincing group of concealers.     

Adaptations in driver deceleration behaviour with automatic incident detection: A naturalistic driving study

Traffic congestion and crash rates can be reduced by introducing variable speed limits (VSLs) and automatic incident detection (AID) systems. Previous findings based on loop detector measurements have revealed that drivers reduce their speeds while approaching traffic congestion when the AID system is active. Notwithstanding these behavioural effects, most microscopic traffic flow models assessing the impact of VSLs do not describe driver response accurately.This study analyses the main factors that influence driver deceleration behaviour while approaching traffic congestion with and without VSLs. The Dutch VSL database was linked to the driver behaviour data collected in the UDRIVE naturalistic driving study. Driver engagement in secondary tasks and glance behaviour were extracted from the video data. Linear mixed-effects models predicting the characteristics of deceleration events were estimated.The results show that the maximum deceleration is high when approaching a slower leader, when driving at high speeds and short distance headways, and close to the beginning of traffic congestion. The minimum time headway is short when driving at high speeds and changing lanes. Certain drivers showed higher decelerations and shorter time headways than others. Controlled for these main factors, smaller maximum decelerations were found when the VSLs were present and visible, and when the gantries were within close proximity. These factors could be incorporated into microscopic traffic simulations to evaluate the impact of AID systems on traffic congestion more realistically. Further research is needed to clarify the link between engagement in secondary tasks, glance behaviour and deceleration behaviour.     

Detecting anonymous attacks in wireless communication medium using adaptive grasshopper optimization algorithm

Intrusion Detection Systems (IDSs) is a system that monitors network traffic for suspicious activity and issues alert when such activity is revealed. Moreover, the existing IDSs-based methods are based on outdated attacks that unable to identify modern attacks or malicious trends. For this reason, in this study we developed a new multi-swarm adaptive grasshopper optimization algorithm to utilize adaptation mechanism in a group of swarms based on fuzzy logic to protect against sophisticated attacks. The proposed (MSAGOA) technique has the capability of global optimization and rapid convergence that are used to attain optimal feature subsets to identify attack types on IDS datasets. In the MSAGOA technique, learning engine as Extreme learning Machine, Naive Bayes, Random Forest and Decision Tree is applied as a fitness function to select the highly discriminating features and to maximize classification performance. Afterward, select the best classifier which works as a fitness function in our approach to measure the performance in terms of accuracy, detection rate, and false alarm rate. The simulations are performed on three IDS datasets such as NSL-KDD, AWID-ATK-R, and NGIDS-DS. The experimental results demonstrated that MSAGOA method has performed better and obtained high detection rate of 99.86%, accuracy of 99.89% in NSL-KDD and high detection rate of 98.73%, accuracy of 99.67% in AWID-ATK-R and detection rate of 89.50%, accuracy of 90.23% in NGIDS-DS. In addition, the performance is compared with several other existing techniques to show the efficacy of the proposed approach.     

阴谋论信念的产生机制——社会认知视角的三种解释

社会心理学将阴谋论视作一种意识形态上的信念,并定义为人们将重大的政治或社会事件归因为有权力的群体或个人暗中预谋以达成其目的的解释倾向。社会认知视角为个体这种阴谋论信念的产生提供了三种解释。错觉模式感知导致人们认知上倾向于在不相关的事件之间建立联系;敏感性动因觉察影响人们过度感知并假定事件背后的动因、目的和意图等;投射是将自己愿意参与阴谋的意图归因于事件中的他人。     

Are errors detected before they occur? Early error sensations revealed by metacognitive judgments on the timing of error awareness

Errors in choice tasks are not only detected fast and reliably, participants often report that they knew that an error occurred already before a response was produced. These early error sensations stand in contrast with evidence suggesting that the earliest neural correlates of error awareness emerge around 300 ms after erroneous responses. The present study aimed to investigate whether anecdotal evidence for early error sensations can be corroborated in a controlled study in which participants provide metacognitive judgments on the subjective timing of error awareness. In Experiment 1, participants had to report whether they became aware of their errors before or after the response. In Experiment 2, we measured confidence in these metacognitive judgments. Our data show that participants report early error sensations with high confidence in the majority of error trials across paradigms and experiments. These results provide first evidence for early error sensations, informing theories of error awareness.     

ELM-HTM guided bio-inspired unsupervised learning for anomalous trajectory classification

Artificial intelligent systems often model the solutions of typical machine learning problems, inspired by biological processes, because of the biological system is faster and much adaptive than deep learning. The utility of bio-inspired learning methods lie in its ability to discover unknown patterns, and its less dependence on mathematical modeling or exhaustive training. In this paper, we propose a new bio-inspired learning model for a single-class classifier to detect abnormality in video object trajectories. The method uses a simple but dynamic extreme learning machine (ELM) and hierarchical temporal memory (HTM) together referred to as ELM-HTM in an unsupervised way to learn and classify time series patterns. The method has been tested on trajectory sequences in traffic surveillance to find abnormal behaviors such as high-speed, unusual stops, driving in wrong directions, loitering, etc. Experiments have also been performed with 3D air signatures captured using sensors and used for biometric authentication(forged/genuine). The results indicate a significant gain over training time and classification accuracy. The proposed method outperforms in predicting long-time patterns by observing small steps with an average accuracy gain of 15% as compared to the state-of-the-art HTM. The method has applications in detecting abnormal activities in videos by learning the movement patterns as well as in biometric authentication.