Identification of literary movements using complex networks to represent texts

The use of statistical methods to analyze large databases of text has been useful in unveiling patterns of human behavior and establishing historical links between cultures and languages. In this study, we identified literary movements by treating books published from 1590 to 1922 as complex networks, whose metrics were analyzed with multivariate techniques to generate six clusters of books. The latter correspond to time periods coinciding with relevant literary movements over the last five centuries. The most important factor contributing to the distinctions between different literary styles was the average shortest path length, in particular the asymmetry of its distribution. Furthermore, over time there has emerged a trend toward larger average shortest path lengths, which is correlated with increased syntactic complexity, and a more uniform use of the words reflected in a smaller power-law coefficient for the distribution of word frequency. Changes in literary style were also found to be driven by opposition to earlier writing styles, as revealed by the analysis performed with geometrical concepts. The approaches adopted here are generic and may be extended to analyze a number of features of languages and cultures.

Influence of cueing on the preparation and execution of untrained and trained complex motor responses

This study investigated the influence of cueing on the performance of untrained and trained complex motor responses. Healthy adults responded to a visual target by performing four sequential movements (complex response) or a single movement (simple response) of their middle finger. A visual cue preceded the target by an interval of 300, 1000, or 2000 ms. In Experiment 1, the complex and simple responses were not previously trained. During the testing session, the complex response pattern varied on a trial-by-trial basis following the indication provided by the visual cue. In Experiment 2, the complex response and the simple response were extensively trained beforehand. During the testing session, the trained complex response pattern was performed in all trials. The latency of the untrained and trained complex responses decreased from the short to the medium and long cue-target intervals. The latency of the complex response was longer than that of the simple response, except in the case of the trained responses and the long cue-target interval. These results suggest that the preparation of untrained complex responses cannot be completed in advance, this being possible, however, for trained complex responses when enough time is available. The duration of the 1st submovement, 1st pause and 2nd submovement of the untrained and the trained complex responses increased from the short to the long cue-target interval, suggesting that there is an increase of online programming of the response possibly related to the degree of certainty about the moment of target appearance.

Stochastic competitive learning in complex networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning.