Monitoring Cognitive and Emotional Processes Through Pupil and Cardiac Response During Dynamic Versus Logical Task

The paper deals with the links between physiological measurements and cognitive and emotional functioning. As long as the operator is a key agent in charge of complex systems, the definition of metrics able to predict his performance is a great challenge. The measurement of the physiological state is a very promising way but a very acute comprehension is required; in particular few studies compare autonomous nervous system reactivity according to specific cognitive processes during task performance and task related psychological stress is often ignored. We compared physiological parameters recorded on 24 healthy subjects facing two neuropsychological tasks: a dynamic task that require problem solving in a world that continually evolves over time and a logical task representative of cognitive processes performed by operators facing everyday problem solving. Results showed that the mean pupil diameter change was higher during the dynamic task; conversely, the heart rate was more elevated during the logical task. Finally, the systolic blood pressure seemed to be strongly sensitive to psychological stress. A better taking into account of the precise influence of a given cognitive activity and both workload and related task-induced psychological stress during task performance is a promising way to better monitor operators in complex working situations to detect mental overload or pejorative stress factor of error

The Potency Of Metacognitive Learning To Foster Mathematical Logical Thinking

The ability of thinking logically needs to be developed due to the fact that it is an essential basic skill. Logical thinking affects that giving reason must be true, and that a sequence of assumptions is based on the high truth value. Mathematics is a subject that functions to train students to think logically. The understanding of logic will help students to arrange the proof that support through process to finally arrive at a conclusion. Currently, metacognition is viewed as an essential element of learning. It refers to someone knowledge of processes and the result itself or of that connected to the process. Metacognition is needed when student solves the task that needs argumentation and logical understanding. In order to help student to skillful think logically, mathematics learning must be designed as such so that the condition will raise the skill of metacognitive acts. Key words: metacognitive learning, mathematical logical thinkin

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Unraveling the influence of domain knowledge during simulation-based inquiry learning

This study investigated whether the mere knowledge of the meaning of variables can facilitate inquiry learning processes and outcomes. Fifty-seven college freshmen were randomly allocated to one of three inquiry tasks. The concrete task had familiar variables from which hypotheses about their underlying relations could be inferred. The intermediate task used familiar variables that did not invoke underlying relations, whereas the abstract task contained unfamiliar variables that did not allow for inference of hypotheses about relations. Results showed that concrete participants performed more successfully and efficiently than intermediate participants, who in turn were equally successful and efficient as abstract participants. From these findings it was concluded that students learning by inquiry benefit little from knowledge of the meaning of variables per se. Some additional understanding of the way these variables are interrelated seems required to enhance inquiry learning processes and outcomes

Using fuzzy logic to integrate neural networks and knowledge-based systems

Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems

LOGICAL AND PSYCHOLOGICAL PARTITIONING OF MIND: DEPICTING THE SAME MAP?

The aim of this paper is to demonstrate that empirically delimited structures of mind are also differentiable by means of systematic logical analysis. In the sake of this aim, the paper first summarizes Demetriou's theory of cognitive organization and growth. This theory assumes that the mind is a multistructural entity that develops across three fronts: the processing system that constrains processing potentials, a set of specialized structural systems (SSSs) that guide processing within different reality and knowledge domains, and a hypecognitive system that monitors and controls the functioning of all other systems. In the second part the paper focuses on the SSSs, which are the target of our logical analysis, and it summarizes a series of empirical studies demonstrating their autonomous operation. The third part develops the logical proof showing that each SSS involves a kernel element that cannot be reduced to standard logic or to any other SSS. The implications of this analysis for the general theory of knowledge and cognitive development are discussed in the concluding part of the paper

Metacognitive Activities Performed by Pre-Service Science Teachers in Scientific Reasoning Skills Teaching with the POE Technique

The aim of this study was to investigate the pre-service teachers’ metacognitive activities occurring in the teaching scientific reasoning skills with the POE technique. The participants of the research included six pre-service science teachers who were seniors in the science education department of at a university in the west of Turkey. The holistic single-case design was used as the research method in this study. The POE Activity Report, an Activity Journal and a Semi-structured Metacognition Observation Form were used to examine the participants’ metacognitive activities. Inductive and comparative analysis was used to. It was found that (i)  the pre-service teachers performed various monitoring activities (f = 13) and evaluating activities (f = 4) in the teaching of six different scientific reasoning skills (control of variables, proportional reasoning, correlation reasoning, probability reasoning, combinational reasoning, hypothetical-deductive reasoning) with the POE technique; (ii) there was more variety in metacognitive activities performed by pre-service teachers in teaching of control of variables (f = 15), there was least diversity in the teaching of hypothetical-deductive reasoning skill (f = 10). The results were discussed in line with the related literature, and suggestions were presented regarding the teaching of scientific reasoning skills