Recursive Gauss-Helmert model with equality constraints applied to the efficient system calibration of a 3D laser scanner

Sensors for environmental perception are nowadays applied in numerous vehicles and are expected to be used in even higher quantities for future autonomous driving. This leads to an increasing amount of observation data that must be processed reliably and accurately very quickly. For this purpose, recursive approaches are particularly suitable in terms of their efficiency when powerful CPUs and GPUs are uneconomical, too large, or too heavy for certain applications. If explicit functional relationships between the available observations and the requested parameters are used to process and adjust the observation data, complementary approaches exist. The situation is different for implicit relationships, which could not be considered recursively for a long time but only in the context of batch adjustments. In this contribution, a recursive Gauss-Helmert model is presented that can handle explicit and implicit equations and thus allows high flexibility. This recursive estimator is based on a Kalman filter for implicit measurement equations, which has already been used for georeferencing kinematic multi-sensor systems (MSS) in urban environments. Furthermore, different methods for introducing additional information using constraints and the resulting added value are shown. Practical application of the methodology is given by an example for the calibration of a laser scanner for a MSS.

Explicit and implicit markers of fairness preeminence in criminal judges

Achieving justice could be considered a complex social decision-making scenario. Despite the relevance of social decisions for legal contexts, these processes have still not been explored for individuals who work as criminal judges dispensing justice. To bridge the gap, we used a complex social decision-making task (Ultimatum game) and tracked a heart rate variability measurement: the square root of the mean squared differences of successive NN intervals (RMSSD) at their baseline (as an implicit measurement that tracks emotion regulation behavior) for criminal judges (n = 24) and a control group (n = 27). Our results revealed that, compared to controls, judges were slower and rejected a bigger proportion of unfair offers. Moreover, the rate of rejections and the reaction times were predicted by higher RMSSD scores for the judges. This study provides evidence about the impact of legal background and expertise in complex social decision-making. Our results contribute to understanding how expertise can shape criminal judges’ social behaviors and pave the way for promising new research into the cognitive and physiological factors associated with social decision-making.

Information-Based Georeferencing of an Unmanned Aerial Vehicle by Dual State Kalman Filter with Implicit Measurement Equations

Georeferencing a kinematic Multi-Sensor-System (MSS) within crowded areas, such as inner-cities, is a challenging task that should be conducted in the most reliable way possible. In such areas, the Global Navigation Satellite System (GNSS) data either contain inevitable errors or are not continuously available. Regardless of the environmental conditions, an Inertial Measurement Unit (IMU) is always subject to drifting, and therefore it cannot be fully trusted over time. Consequently, suitable filtering techniques are required that can compensate for such possible deficits and subsequently improve the georeferencing results. Sometimes it is also possible to improve the filter quality by engaging additional complementary information. This information could be taken from the surrounding environment of the MSS, which usually appears in the form of geometrical constraints. Since it is possible to have a high amount of such information in an environment of interest, their consideration could lead to an inefficient filtering procedure. Hence, suitable methodologies are necessary to be extended to the filtering framework to increase the efficiency while preserving the filter quality. In the current paper, we propose a Dual State Iterated Extended Kalman Filter (DSIEKF) that can efficiently georeference a MSS by taking into account additional geometrical information. The proposed methodology is based on implicit measurement equations and nonlinear geometrical constraints, which are applied to a real case scenario to further evaluate its performance.

The Implicit Component of Moral Disengagement: Applying the Relational Responding Task to Investigate Its Relationship With Cheating Behavior

This article aims to conceptualize, for the first time, an implicit form of moral disengagement and investigate its role in relation to cheating behavior. In line with the implicit social-cognition models, we argue that the implicit moral disengagement would represent an unintentional, automatic, and less accessible form of the mechanisms bypassing the moral self-regulatory system. We anticipate that in situations implying on-the-spot decisions and where individuals might suffer no consequences for the misconduct, the implicit moral disengagement would predict the actual behavior while the explicit moral disengagement would predict self-reported conduct. The results of three empirical studies provide support for the theorization of an implicit moral disengagement and its assessment through a newly developed implicit measurement procedure using the relational responding task. Results of the structural equation models, including both implicit and explicit moral disengagement, demonstrated that only the implicit one was associated with the actual misconduct.

The Gender Violence - Implicit Association Test to measure attitudes toward intimate partner violence against women

Attitudes play a central role in intimate partner violence against women and are related to its origin, to the responses of women who suffer violence, and to the settings where it occurs. In fact, these attitudes are recognized as one of the risk factors linked to violent perpetration and to public, professional, and victim responses to this type of violence. However, even though available research generally shows a broad rejection of this violence, it remains a serious social and health problem that has reached epidemic proportions. This suggests that the information available about these attitudes (obtained through explicit and direct measures, i.e., self-reports) may be distorted or influenced by factors such as social desirability. In this context, the overall objective of our research project is to provide multi-method measures (explicit and implicit) of attitudes toward intimate partner violence against women, and the main goal of this paper is to propose an instrument for the implicit measurement of these attitudes. In this regard, the Implicit Association Test (IAT) is the most common procedure used, providing a superior predictive validity compared to explicit measures for socially sensitive topics. We will present an exploratory study that describes its adaptation for our purposes, and the development of the Gender Violence - Implicit Association Test (GV-IAT) to use among Spanish-speaking populations, and discuss the strengths and limitations of this proposal.

Lions, and Tigers, and Implicit Measures, Oh My! Implicit Assessment and the Valence vs. Threat Distinction

Physically threatening objects are negative, but negative objects are not necessarily threatening. Moreover, responses elicited by threats to physical harm are distinct from those elicited by other negatively (and positively) valenced stimuli. We discuss the importance of the threat versus valence distinction for implicit measurement both in terms of the activated evaluation and the design of the measure employed to assess that evaluation. We suggest that accounting for the distinct evaluations of threat and valence better enables implicit measures to provide understanding and prediction of subsequent judgment, emotion, and behavior.

A passive, objective and implicit measure of recognition memory in Alzheimer’s Disease using Fastball memory assessment.

Earlier diagnosis of Alzheimer’s disease (AD) requires biomarkers sensitive to the structural and functional changes associated with the disease. While considerable progress has been made in the development of structural biomarkers, functional biomarkers of early cognitive change are still needed. We present Fastball, a new EEG method for the passive, objective and implicit measurement of recognition memory, that requires no behavioural response or comprehension of the task. Younger adults (aged M=24 SD=6), older adults (aged M=74 SD=4) and AD patients aged M=79 SD=10), (n=20 per group) completed the Fastball task, lasting just under three minutes. The task required the passive viewing of rapidly presented images and assessed their spontaneous ability to differentiate between images on the basis of previous exposure, i.e. old/new. Participants were not instructed to attend to previously seen images and provided no behavioural response. Following the Fastball task, they completed a cued recall task to measure their explicit recognition of previously seen stimuli. AD patients showed significantly impaired recognition memory compared to healthy older adult controls in the Fastball task (p<0.001, Cohen’s D = 1.52). AD patients’ behavioural recognition memory performance, however, was not significantly different from healthy older adult controls. AD patients could be discriminated with high accuracy from healthy older adult controls using the Fastball measure of recognition memory (AUC=0.86, p<0.001), whereas discrimination performance was poor using behavioural cued-recall accuracy, (AUC=0.63, p=0.148). There were no significant effects of healthy ageing, with older and younger adult controls performing equivalently in both the Fastball task and behavioural cued-recall task. Fastball is sensitive to changes in recognition memory processes in AD that would be missed by behavioural testing alone. It is passive, non-invasive, quick to administer and uses cheap, scalable EEG technology. Fastball provides a new powerful method for the assessment of cognition in dementia and opens a new door in the development of early diagnosis tools.

Research on Image Acquisition Method Model Based on Implicit Association Test

In the process of the traditional quantitative method is easily interfered with by subjective and external environment, and cannot reflect the real emotion of users. The implicit measurement method can better reflect the cognitive of users and has good reliability in perceptual evaluation. In this paper, the implicit cognitive processing process in users’ perceptual evaluation of products is quantitatively analyzed. The correlation between product image attribute values and implicit measurement data is obtained. Thus, an image extraction model based on implicit measurement data is obtained. The implicit association test is introduced into the image extraction process, and the relationship between the implicit association test data of users and the data of product image attribute values is analyzed. Taking UAV as the analysis prototype, the image extraction model is obtained. After verification and analysis, the image extraction results are consistent with the image attribute values.