Bias and learning in temporal binding: intervals between actions and outcomes are compressed by prior bias

It has consistently been shown that agents judge the intervals between their actions and outcomes as compressed in time, an effect named intentional binding. In the present work, we investigated whether this effect is result of prior bias volunteers have about the timing of the consequences of their actions, or if it is due to learning that occurs during the experimental session. Volunteers made temporal estimates of the interval between their action and target onset (Action conditions), or between two events (No-Action conditions). Our results show that temporal estimates become shorter throughout each experimental block in both conditions. Moreover, we found that observers judged intervals between action and outcomes as shorter even in very early trials of each block. To quantify the decrease of temporal judgments in experimental blocks, exponential functions were fitted to participants’ temporal judgments. The fitted parameters suggest that observers had different prior biases as to intervals between events in which action was involved. These findings suggest that prior bias might play a more important role in this effect than calibration-type learning processes.

Word sense disambiguation via high order of learning in complex networks

Complex networks have been employed to model many real systems and as a modeling tool in a myriad of applications. In this paper, we use the framework of complex networks to the problem of supervised classification in the word disambiguation task, which consists in deriving a function from the supervised (or labeled) training data of ambiguous words. Traditional supervised data classification takes into account only topological or physical features of the input data. On the other hand, the human (animal) brain performs both low- and high-level orders of learning and it has facility to identify patterns according to the semantic meaning of the input data. In this paper, we apply a hybrid technique which encompasses both types of learning in the field of word sense disambiguation and show that the high-level order of learning can really improve the accuracy rate of the model. This evidence serves to demonstrate that the internal structures formed by the words do present patterns that, generally, cannot be correctly unveiled by only traditional techniques. Finally, we exhibit the behavior of the model for different weights of the low- and high-level classifiers by plotting decision boundaries. This study helps one to better understand the effectiveness of the model. Copyright (C) EPLA, 2012

A Systematic Mapping on the Role-Permission Relationship in Role Based Access Control Models

Access control is a key component of security in any computer system. In the last two decades, the research on Role Basead Access Control Models was intense. One of the most important components of a Role Based Model is the Role-Permission Relationship. In this paper, the technique of systematic mapping is used to identify, extract and analyze many approaches applied to establish the Role-Permission Relationship. The main goal of this mapping is pointing directions of significant research in the area of Role Based Access Control Models.

Two-dimensional semimetal-insulator transition in HgTe-based quantum wells induced by a longitudinal magnetic field

A metal-insulator transition in a two-dimensional semimetal based on HgTe quantum wells is discovered. The transition is induced by a magnetic field applied parallel to the plane of the quantum well. The threshold behavior of the activation energy as a function of the magnetic-field strength and an abrupt reduction of the Hall resistance at the onset of the transition suggest that the observed effect originates from the formation of an excitonic insulator.

The influence of different co-catalysts in Pt-based ternary and quaternary electro-catalysts on the electro-oxidation of methanol and ethanol in acid media

One of the key objectives in fuel cell technology is to reduce Pt loading by the improvement of its catalytic activity towards alcohol oxidation. Here, a sol-gel based method was used to prepare ternary and quaternary carbon supported nanoparticles by combining Pt-Ru with Mo, Ta, Pb, Rh or Ir, which were used as electro-catalysts for the methanol and ethanol oxidation reactions in acid medium. Structural characterization performed by XRD measurements revealed that crystalline structures with crystallites ranging from 2.8 to 4.1 nm in size and with different alloy degrees were produced. Tantalum and lead deposited as a heterogeneous mixture of oxides with different valences resulting in materials with complex structures. The catalysts activities were evaluated by cyclic voltammetry and by Tafel plots and the results showed that the activity towards methanol oxidation was highly dependent of the alloy degree, while for ethanol the presence of a metal capable to promote the break of C-C bond, such as Rh, was necessary for a good performance. Additionally, the catalysts containing of TaOx or PbOx resulted in the best materials due to different effects: the hi-functional mechanism promoted by TaOx and a better dispersion of the catalysts constituents promoted by PbOx. (C) 2012 Elsevier B.V. All rights reserved.