Search Results (2)

Personality Systems Interactions (PSI) theory explains differences in personality based on the properties of four cognitive systems—object recognition (OR), intuitive behaviour (IB), intention memory (IM) and extension memory (EM). Each system is associated with characteristic modes of perception and behaviour, so personality is determined by which systems are primarily active. According to PSI theory, the activities of the cognitive systems are regulated by positive and negative affect (reward and punishment). Thus, differences in personality ultimately emerge from four parameters—the sensitivities of up- or downregulating positive and negative affect. The complex interactions of affect and cognitive systems have been represented in a mathematical model based on a system of differential equations. In this study, the environment of a person represented by the mathematical model is modelled by a time series of perturbations with positive and negative affect that are generated by a stochastic process. Comparing the average activities of the cognitive systems for different parameter sets exposed to the same time series of affect perturbations, we observe that different dominant cognitive systems emerge. This demonstrates that different sensitivities for positive and negative affect lead to different modes of cognition and, thus, to different personality types such as agreeable, conscientious, self-determined or independent. Varying the relative frequencies of negative and positive affect perturbations reveals that the average activities of all cognitive systems respond linearly. This observation enables us to predict that conscientious and independent personalities benefit from increased exposure to positive affect, whereas agreeable and self-determined personalities achieve a better balance of their cognitive systems by increased negative affect. Full article

Optimization problems become increasingly complicated in the era of big data and Internet of Things, which significantly challenges the effectiveness and efficiency of existing optimization methods. To effectively solve this kind of problems, this paper puts forward a stochastic cognitive dominance leading particle swarm optimization algorithm (SCDLPSO). Specifically, for each particle, two personal cognitive best positions are first randomly selected from those of all particles. Then, only when the cognitive best position of the particle is dominated by at least one of the two selected ones, this particle is updated by cognitively learning from the better personal positions; otherwise, this particle is not updated and directly enters the next generation. With this stochastic cognitive dominance leading mechanism, it is expected that the learning diversity and the learning efficiency of particles in the proposed optimizer could be promoted, and thus the optimizer is expected to explore and exploit the solution space properly. At last, extensive experiments are conducted on a widely acknowledged benchmark problem set with different dimension sizes to evaluate the effectiveness of the proposed SCDLPSO. Experimental results demonstrate that the devised optimizer achieves highly competitive or even much better performance than several state-of-the-art PSO variants. Full article