Improvements and future challenges for the research infrastructure in the field “Measuring cognitive ability”

The assessment of cognitive abilities is critical in large-scale survey studies that aim at elucidating the longitudinal interplay between the individual’s cognitive potential and socio-economic variables. The format of such studies calls for assessment methods which can not only be administered economically but also display a high (psychometric) measurement quality. In consideration of recent theoretical and empirical advances in intelligence research, we recommend the implementation of tests drawing on working memory in large-scale survey studies. Working memory is a limited-capacity system for temporary storage and processing of information and currently discussed to be the cognitive key system underlying intellectual abilities. Four types of working memory tests are exemplarily described and critically evaluated with regard to their psychometric quality and the need for further evaluation.cognitive abilities, intelligence, knowledge, information processing, mental speed, working memory

Improvements and future challenges for the research infrastructure in the field "Measuring cognitive ability"

The assessment of cognitive abilities is critical in large-scale survey studies that aim at elucidating the longitudinal interplay between the individual's cognitive potential and socio-economic variables. The format of such studies calls for assessment methods which can not only be administered economically but also display a high (psychometric) measurement quality. In consideration of recent theoretical and empirical advances in intelligence research, we recommend the implementation of tests drawing on working memory in large-scale survey studies. Working memory is a limited-capacity system for temporary storage and processing of information and currently discussed to be the cognitive key system underlying intellectual abilities. Four types of working memory tests are exemplarily described and critically evaluated with regard to their psychometric quality and the need for further evaluation

Promises and potential pitfalls of a \u27cognitive neuroscience of mathematics learning\u27

The present commentary discusses the papers of the special issue on \u27cognitive neuroscience and mathematics learning\u27 with respect to methodological and theoretical constraints of using neuroscientific methods to study educationally relevant processes associated with mathematics learning. A special focus is laid on the relevance of subject populations, methodological limitations of current neuroimaging methods and theoretical questions concerning the relationship between the well-studied neural correlates of numerical magnitude processing and the less-investigated neural processes underlying higher level mathematical skills, such as algebraic reasoning. © FIZ Karlsruhe 2010

Expertise in symbol-referent mapping

Much evidence cited by Cohen Kadosh & Walsh (CK&W) in support of their notation-specific representation hypothesis is based on tasks requiring automatic number processing. Several of these findings can be alternatively explained by differential expertise in mapping numerical symbols onto semantic magnitude representations. The importance of considering symbol-referent mapping expertise in theories on numerical representations is highlighte

Visual on-line learning in distributed camera networks

Automatic detection of persons is an important application in visual surveillance. In general, state-of-the-art systems have two main disadvantages: First, usually a general detector has to be learned that is applicable to a wide range of scenes. Thus, the training is time-consuming and requires a huge amount of labeled data. Second, the data is usually processed centralized, which leads to a huge network traffic. Thus, the goal of this paper is to overcome these problems, which is realized by a person detection system, that is based on distributed smart cameras (DSCs). Assuming that we have a large number of cameras with partly overlapping views, the main idea is to reduce the model complexity of the detector by training a specific detector for each camera. These detectors are initialized by a pre-trained classifier, that is then adapted for a specific camera by co-training. In particular, for co-training we apply an on-line learning method (i.e., boosting for feature selection), where the information exchange is realized via mapping the overlapping views onto each other by using a homography. Thus, we have a compact scenedependent representation, which allows to train and to evaluate the classifiers on an embedded device. Moreover, since the information transfer is reduced to exchanging positions the required network-traffic is minimal. The power of the approach is demonstrated in various experiments on different publicly available data sets. In fact, we show that on-line learning and applying DSCs can benefit from each other. Index Terms — visual on-line learning, object detection, multi-camera networks 1

Brain Correlates of Mathematical Competence in Processing Mathematical Representations

The ability to extract numerical information from different representation formats (e.g., equations, tables, or diagrams) is a key component of mathematical competence but little is known about its neural correlate. Previous studies comparing mathematically less and more competent adults have focused on mental arithmetic and reported differences in left angular gyrus (AG) activity which were interpreted to reflect differential reliance on arithmetic fact retrieval during problem solving. The aim of the present functional magnetic resonance imaging study was to investigate the brain correlates of mathematical competence in a task requiring the processing of typical mathematical representations. Twenty-eight adults of lower and higher mathematical competence worked on a representation matching task in which they had to evaluate whether the numerical information of a symbolic equation matches that of a bar chart. Two task conditions without and one condition with arithmetic demands were administered. Both competence groups performed equally well in the non-arithmetic conditions and only differed in accuracy in the condition requiring calculation. Activation contrasts between the groups revealed consistently stronger left AG activation in the more competent individuals across all three task conditions. The finding of competence-related activation differences independently of arithmetic demands suggests that more and less competent individuals differ in a cognitive process other than arithmetic fact retrieval. Specifically, it is argued that the stronger left AG activity in the more competent adults may reflect their higher proficiency in processing mathematical symbols. Moreover, the study demonstrates competence-related parietal activation differences that were not accompanied by differential experimental performance

The function of the left angular gyrus in mental arithmetic: Evidence from the associative confusion effect

While the left angular gyrus (lAG) has been repeatedly implicated in mental arithmetic, its precise functional role has not been established. On the one hand, it has been speculated that the lAG is involved in task-specific processes. On the other hand, the observation of relative deactivation during arithmetic has led to the contention that differential lAG activation reflects task-unrelated difficulty effects associated with the default mode network (DMN). Using functional magnetic resonance imaging, we investigated the neural correlates of the associative confusion effect that allowed us to dissociate effects of task difficulty and task-related arithmetic processes on lAG activation. The associative confusion effect is characterized by poorer performance while verifying addition and multiplication equations whose solutions are associated with the other operation (confusion equations: e.g., 9 × 6 = 15 ) compared with solutions unrelated to both operations (non-confusion equations: e.g., 9 × 6 = 52 ). Comparing these two conditions revealed higher activation of the anterior lAG (areas PGa, PFm, and PF) and the left dorsolateral prefrontal cortex for the confusion problems. This effect displayed only slight anatomical overlap with the well-established reverse problem-size effect (small minus large problems) and task-related deactivation in the parietal cortex. The finding of greater lAG activity (less deactivation) in the more difficult task condition is inconsistent with the hypothesis that lAG activation during mental arithmetic reflects task difficulty related modulations of the DMN. Instead, the present findings provide further support for the symbol-referent mapping hypothesis, suggesting that the lAG mediates the automatic mapping of arithmetic problems onto solutions stored in memory. © 2011 Wiley Periodicals, Inc

Photogenerated Carriers in SrTiO3 Probed by Mid-Infrared Absorption

Infrared absorption spectra of SrTiO$_3$ have been measured under above-band-gap photoexcitations to study the properties of photogenerated carriers, which should play important roles in previously reported photoinduced phenomena in SrTiO$_3$. A broad absorption band appears over the entire mid-infrared region under photoexcitation. Detailed energy, temperature, and excitation power dependences of the photoinduced absorption are reported. This photo-induced absorption is attributed to the intragap excitations of the photogenerated carriers. The data show the existence of a high density of in-gap states for the photocarriers, which extends over a wide energy range starting from the conduction and valence band edges.Comment: 5 pages, 5 figures, submitted to J. Phys. Soc. Jp