Perceptual speed does not cause intelligence, and intelligence does not cause perceptual speed

There is ongoing debate whether the efficiency of local cognitive processes leads to global cognitive ability or whether global ability feeds the efficiency of basic processes. A prominent example is the well-replicated association between inspection time (IT), a measure of perceptual discrimination speed, and intelligence (IQ), where it is not known whether increased speed is a cause or consequence of high IQ. We investigated the direction of causation between IT and IQ in 2012 genetically related subjects from Australia and The Netherlands. Models in which the reliable variance of each observed variable was specified as a latent trait showed IT correlations of -0.44 and -0.33 with respective Performance and Verbal IQ; heritabilities were 57% (IT), 83% (PIQ) and 77% (VIQ). Directional causation models provided poor fits to the data, with covariation best explained by pleiotropic genes (influencing variation in both IT and IQ). This finding of a common genetic factor provides a better target for identifying genes involved in cognition than genes which are unique to specific traits.

Off-line signature verification and forgery detection using fuzzy modeling

Automatic signature verification is a well-established and an active area of research with numerous applications such as bank check verification, ATM access, etc. This paper proposes a novel approach to the problem of automatic off-line signature verification and forgery detection. The proposed approach is based on fuzzy modeling that employs the Takagi-Sugeno (TS) model. Signature verification and forgery detection are carried out using angle features extracted from box approach. Each feature corresponds to a fuzzy set. The features are fuzzified by an exponential membership function involved in the TS model, which is modified to include structural parameters. The structural parameters are devised to take account of possible variations due to handwriting styles and to reflect moods. The membership functions constitute weights in the TS model. The optimization of the output of the TS model with respect to the structural parameters yields the solution for the parameters. We have also derived two TS models by considering a rule for each input feature in the first formulation (Multiple rules) and by considering a single rule for all input features in the second formulation. In this work, we have found that TS model with multiple rules is better than TS model with single rule for detecting three types of forgeries; random, skilled and unskilled from a large database of sample signatures in addition to verifying genuine signatures. We have also devised three approaches, viz., an innovative approach and two intuitive approaches using the TS model with multiple rules for improved performance. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

A general-purpose tunable landscape generator

The research literature on metalieuristic and evolutionary computation has proposed a large number of algorithms for the solution of challenging real-world optimization problems. It is often not possible to study theoretically the performance of these algorithms unless significant assumptions are made on either the algorithm itself or the problems to which it is applied, or both. As a consequence, metalieuristics are typically evaluated empirically using a set of test problems. Unfortunately, relatively little attention has been given to the development of methodologies and tools for the large-scale empirical evaluation and/or comparison of metaheuristics. In this paper, we propose a landscape (test-problem) generator that can be used to generate optimization problem instances for continuous, bound-constrained optimization problems. The landscape generator is parameterized by a small number of parameters, and the values of these parameters have a direct and intuitive interpretation in terms of the geometric features of the landscapes that they produce. An experimental space is defined over algorithms and problems, via a tuple of parameters for any specified algorithm and problem class (here determined by the landscape generator). An experiment is then clearly specified as a point in this space, in a way that is analogous to other areas of experimental algorithmics, and more generally in experimental design. Experimental results are presented, demonstrating the use of the landscape generator. In particular, we analyze some simple, continuous estimation of distribution algorithms, and gain new insights into the behavior of these algorithms using the landscape generator.

Training emotional intelligence: Presenting the results of an experimental study

This presentation outlines the results of an eighteen month study examining the effect of an emotions focused training intervention on the emotional intelligence of employees from a large public sector organisation. Utilising an experimental methodology, 280 staff attended a two-day program focused on training emotional intelligence skills and abilities. These interventions were created around Mayer and Salovey’s four-branch model of emotional intelligence (awareness, understanding, facilitation and management of emotions). The experimental group’s emotional intelligence was tested pre and post training using the Workgroup Emotional Intelligence Profile (WEIP). In addition, a control group from the same organisation also completed the same measure at three points during the same eighteen month period. Analysis of the control and experimental group data were conducted, and whilst no changes were found in the control group, the experimental group’s overall emotional intelligence significantly improved post training. To further strengthen these findings, a measure of effect size using Cohen’s d was also conducted to assess the magnitude of the training intervention’s overall effect. Full results will be presented during the presentation, with feedback on the study and methods utilised encouraged from participants.