Face Cognition: A Set of Distinct Mental Abilities

Perceiving, learning, and recognizing faces swiftly and accurately is of paramount importance to humans as a social species. Though established functional models of face cognition<sup>1,2</sup> suggest the existence of multiple abilities in face cognition, the number of such abilities and the relationships among them and to other cognitive abilities can only be determined by studying individual differences. Here we investigated individual differences in a broad variety of indicators of face cognition and identified for the first time three component abilities: face perception, face memory, and the speed of face cognition. These component abilities were replicated in an independent study and were found to be robustly separable from established cognitive abilities, specifically immediate and delayed memory, mental speed, general cognitive ability, and object cognition. The analysis of individual differences goes beyond functional and neurological models of face cognition by demonstrating the difference between face perception and face learning, and by making evident the distinction between speed and accuracy of face cognition. Our indicators also provide a means to develop tests and training programs for face cognition that are broader and more precise than those currently available).<sup>3,4</sup&#x3e

Förderung der Mulchsaat im Ökolandbau - das Auflaufverhalten von Zwischenfrüchten

The several advantages of conservation tillage systems are interesting for organic farmers too. The positive effects concerning the reduction of soil erosion are well known and documented. The more residues are left on the soil surface the higher reduction of soil erosion is achieved. Nevertheless the plough is still the main tillage equipment in organic agriculture systems. A first step towards conservation tillage might be the tillage before intercrops. In a three years project the relationship of emergence rate from straw quantity, working depth and residue cover is investigated. Thick mulch layers have negative effects on the emergence rate of the followed crop. Modern machinery for conservation tillage improve the incorporation of crop residues which effects the residue cover after tillage. Especially at deep working depth (above 13 cm) the benchmark of 30% residues cover can’t be achieved any more. The results of the trials support the conservation tillage system before intercrops

Weed control in organic farming through mechanical solutions

Based on the experience from science and practice, the handbook aims to present and discuss direct and indirect mechanical weed control. In the Introductory section the reader gets an overview of different machinery, mechanical, thermal and system-based methods to control weed. The farmer learns which factors contribute to soil compaction and gets advice how to avoid it. Furthermore, direct measures during seedbed preparation and tillage to control weeds as the creeping thistle and vetches on arable crops and vegetable crops are proposed. Finally, the development and the potential of future machinery is presented

Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning

With electric power systems becoming more compact and increasingly powerful, the relevance of thermal stress especially during overload operation is expected to increase ceaselessly. Whenever critical temperatures cannot be measured economically on a sensor base, a thermal model lends itself to estimate those unknown quantities. Thermal models for electric power systems are usually required to be both, real-time capable and of high estimation accuracy. Moreover, ease of implementation and time to production play an increasingly important role. In this work, the thermal neural network (TNN) is introduced, which unifies both, consolidated knowledge in the form of heat-transfer-based lumped-parameter models, and data-driven nonlinear function approximation with supervised machine learning. A quasi-linear parameter-varying system is identified solely from empirical data, where relationships between scheduling variables and system matrices are inferred statistically and automatically. At the same time, a TNN has physically interpretable states through its state-space representation, is end-to-end trainable -- similar to deep learning models -- with automatic differentiation, and requires no material, geometry, nor expert knowledge for its design. Experiments on an electric motor data set show that a TNN achieves higher temperature estimation accuracies than previous white-/grey- or black-box models with a mean squared error of $3.18~\text{K}^2$ and a worst-case error of $5.84~\text{K}$ at 64 model parameters.Comment: Preprint; Fix typos, streamline math. notation; 10 page

Complementary and competing factor analytic approaches for the investigation of measurement invariance

Sample-related invariance is an important topic in psychometric research. The generalizability of findings in a broad range of application samples requires equivalence of interpretations based on the measurement outcomes across respective samples. Contextual factors like gender, age, culture, ethnicity, socio-economical status etc. may affect the meaning and interpretation of psychological measures. Sample-related invariance is frequently investigated using Multiple-Group Mean and Covariance Structure (MGMCS) analyses. This method builds upon natural or artifical categories of contextual variables. Many contextual variables are continuous variables and their categorization is associated with an information loss and potentially overly simplistic data analyses. We present and discuss two complementary analytical approaches – Latent Moderated Structural (LMS) Equations and Local Structural Equation Models (LSEM). Both approaches allow treating contextual factors as continuous variables and are appropriate to detect non-linear relations. The use of these methods is exemplified based on real data. We investigated measurement equivalence of a battery of cognitive tests across age (N = 448; age range 18-82 years). Based on a higher-order factor model of cognitive abilities factorial equivalence could be established – contradicting the age-dedifferentiation hypothesis. Advantages and disadvantages of MGMCS, LMS, and LSEM and further implementations beyond aging-research are discussed

Examining age-related shared variance between face cognition, vision, and self-reported physical health: a test of the common cause hypothesis for social cognition

The shared decline in cognitive abilities, sensory functions (e.g., vision and hearing), and physical health with increasing age is well documented with some research attributing this shared age-related decline to a single common cause (e.g., aging brain). We evaluate the extent to which the common cause hypothesis predicts associations between vision and physical health with social cognition abilities specifically face perception and face memory. Based on a sample of 443 adults (17–88 years old), we test a series of structural equation models, including Multiple Indicator Multiple Cause (MIMIC) models, and estimate the extent to which vision and self-reported physical health are related to face perception and face memory through a common factor, before and after controlling for their fluid cognitive component and the linear effects of age. Results suggest significant shared variance amongst these constructs, with a common factor explaining some, but not all, of the shared age-related variance. Also, we found that the relations of face perception, but not face memory, with vision and physical health could be completely explained by fluid cognition. Overall, results suggest that a single common cause explains most, but not all age-related shared variance with domain specific aging mechanisms evident

Process-oriented intelligence research: A review from the cognitive perspective

Despite over a century of research on intelligence, the cognitive processes underlying intelligent behavior are still unclear. In this review, we summarize empirical results investigating the contribution of cognitive processes associated with working memory capacity, processing speed, and executive processes to intelligence differences. Specifically, we (a) evaluate how cognitive processes associated with the three different cognitive domains have been measured, and (b) how these processes are related to individual differences in intelligence. Consistently, this review illustrates that isolating single cognitive processes using average performance in cognitive tasks is hardly possible. Instead, formal models that implement theories of cognitive processes underlying performance in different cognitive tasks may provide more adequate indicators of single cognitive processes. Therefore, we outlined which models for working memory capacity, processing speed, and executive processes may provide more specific insights into cognitive processes associated with individual differences in intelligence. Finally, we discuss implications of a process-oriented intelligence research using cognitive measurement models for psy- chometric theories of intelligence and argue that a model-based approach might overcome validity problems of traditional intelligence theories