The effects of unconditional stimulus valence and conditioning paradigm on verbal, skeleto-motor, and autonomic indices of human Pavlovian conditioning

The effects of unconditional stimulus (US) valence (aversive electro-tactile stimulus vs. nonaversive imperative stimulus of a RT task) and conditioning paradigm (delay vs. trace) on affective learning as indexed by verbal ratings of conditional stimulus (CS) pleasantness and blink startle modulation and on relational learning as indexed by electrodermal responses were investigated. Affective learning was not affected by the conditioning paradigm; however, electrodermal responses and blink latency shortening indicated delayed learning in the trace procedure. Changes in rated CS pleasantness were found with the aversive US, but not with the non-aversive US. Differential conditioning as indexed by electrodermal responses and startle modulation was found regardless of US valence. The finding of significant differential blink modulation and electrodermal responding in the absence of a change in rated CS pleasantness as a result of conditioning with a non-aversive US was replicated in a second experiment. These results seem to indicate that startle modulation during conditioning is mediated by the arousal level of the anticipated US, rather than by the valence of the CS. (C) 2002 Elsevier Science (USA). All rights reserved.

An analysis of relational complexity in an air traffic control conflict detection task

Theoretical analyses of air traffic complexity were carried out using the Method for the Analysis of Relational Complexity. Twenty-two air traffic controllers examined static air traffic displays and were required to detect and resolve conflicts. Objective measures of performance included conflict detection time and accuracy. Subjective perceptions of mental workload were assessed by a complexity-sorting task and subjective ratings of the difficulty of different aspects of the task. A metric quantifying the complexity of pair-wise relations among aircraft was able to account for a substantial portion of the variance in the perceived complexity and difficulty of conflict detection problems, as well as reaction time. Other variables that influenced performance included the mean minimum separation between aircraft pairs and the amount of time that aircraft spent in conflict.

Evaluation of unsupervised semantic mapping of natural language with Leximancer concept mapping

The Leximancer system is a relatively new method for transforming lexical co-occurrence information from natural language into semantic patterns in an unsupervised manner. It employs two stages of co-occurrence information extraction-semantic and relational-using a different algorithm for each stage. The algorithms used are statistical, but they employ nonlinear dynamics and machine learning. This article is an attempt to validate the output of Leximancer, using a set of evaluation criteria taken from content analysis that are appropriate for knowledge discovery tasks.

A learning approach for performance evaluation of local network

In this paper, a novel approach is developed to evaluate the overall performance of a local area network as well as to monitor some possible intrusion detections. The data is obtained via system utility 'ping' and huge data is analyzed via statistical methods. Finally, an overall performance index is defined and simulation experiments in three months proved the effectiveness of the proposed performance index. A software package is developed based on these ideas.