The Link between Cognition and the Complexity of Engineering Systems Design

This paper focuses on the role of human cognition in the design of large complex systems. It contrasts the physical system that is the product of the design with the cognitive model that is used by the designer to “understand” the system. The complexity of the system relevant to the designer is a function not only of the physical system, but also of the cognitive model that the designer holds in his mind. Furthermore, the level of cognitive model available to an experienced designer depends on the state of domain knowledge. To be useful in answering the question, “How complex is this system to design?” the state of the domain knowledge available to the designer must be assessed with respect to the level at which the design problem is posed. The concept of conceptual distance is introduced that depends on the disparity between the present level of integrated knowledge and the conceptual level of the design problem. This “distance” is a measure of the complexity of the design task and is called the cognitive complexity of the design. To investigate the concept of cognitive complexity a model of human knowledge is proposed along with a set of graphical abstractions. It is concluded that the cognitive complexity of the design task is neither wholly intrinsic (a property of the system) nor wholly subjective (a property of the mind) but requires an objective evaluation of the engineering problem with respect to present knowledge. It is noted that the structure of knowledge in a specific domain can be mapped and therefore a research program can be launched to systematically determine the difficulty of various engineering endeavors

A Model for Measuring Cognitive Complexity of Software

This paper proposes a model for calculating cognitive complexity of a code. This model considers all major factors responsible for (cognitive) complexity. The practical applicability of the measure is evaluated through experimentation, test cases and comparative study

WEAK MEASUREMENT THEORY AND MODIFIED COGNITIVE COMPLEXITY MEASURE

Measurement is one of the problems in the area of software engineering. Since traditional measurement theory has a major problem in defining empirical observations on software entities in terms of their measured quantities, Morasca has tried to solve this problem by proposing Weak Measurement theory. In this paper, we tried to evaluate the applicability of weak measurement theory by applying it on a newly proposed Modified Cognitive Complexity Measure (MCCM). We also investigated the applicability of Weak Extensive Structure for deciding on the type of scale for MCCM. It is observed that the MCCM is on weak ratio scale

An Approach for the Empirical Validation of Software Complexity Measures

Software metrics are widely accepted tools to control and assure software quality. A large number of software metrics with a variety of content can be found in the literature; however most of them are not adopted in industry as they are seen as irrelevant to needs, as they are unsupported, and the major reason behind this is due to improper empirical validation. This paper tries to identify possible root causes for the improper empirical validation of the software metrics. A practical model for the empirical validation of software metrics is proposed along with root causes. The model is validated by applying it to recently proposed and well known metrics

A Complexity Measure Based on Cognitive Weights

Cognitive Informatics plays an important role in understanding the fundamental characteristics of software. This paper proposes a model of the fundamental characteristics of software, complexity in terms of cognitive weights of basic control structures. Cognitive weights are degree of difficulty or relative time and effort required for comprehending a given piece of software, which satisfy the definition of complexity. An attempt has also been made to prove the robustness of proposed complexity measure by comparing it with the other measures based on cognitive informatics

Weighted Class Complexity: A Measure of Complexity for Object Oriented System

Software complexity metrics are used to predict critical information about reliability and maintainability of software systems. Object oriented software development requires a different approach to software complexity metrics. In this paper, we propose a metric to compute the structural and cognitive complexity of class by associating a weight to the class, called as Weighted Class Complexity (WCC). On the contrary, of the other metrics used for object oriented systems, proposed metric calculates the complexity of a class due to methods and attributes in terms of cognitive weight. The proposed metric has been demonstrated with OO examples. The theoretical and practical evaluations based on the information theory have shown that the proposed metric is on ratio scale and satisfies most of the parameters required by the measurement theor

ANALYZING PHYSICS ITEMS OF UN, TIMSS, AND PISA BASED ON HIGHER-ORDER THINKING AND SCIENTIFIC LITERACY

his study aims to analyzing physics items of The National Examination (Ujian Nasional/UN), Trends in Mathematics and Science Study (TIMSS), and Program for International Student Assessment (PISA) based on higher-order thinking and scientific literacy. The higher-order thinking and scientific literacy has criteria: a) on the thinking process taxonomy of analyzing, evaluating, and creating, and also they are in the knowledge dimension of conceptual, procedural and metacognitive; b) having a divergent construct of item; not only to measure the competency in cognitive, but also to measure science process skills and affective; to facilitate several science process skills; the stem of item use the stimulus in the student s daily life or the phenomenon near the students; and not only to measure the science cognitive but also to measure the student affective and how to use the science knowledge in their daily life. Sample of researched-items were 17 items of UN SMP/MTs 2013, 24 items of TIMSS 2007 (released items), and 17 items of PISA 2006 (released items). Results of this study showed: 1) the percentage of higherorder thinking and scientific literacy in physics items of TIMSS, PISA, and UN were 0.58; 0.53; and 0.35 respectively. The physics items of TIMSS, PISA, and UN also revealed that they are already related to the student daily life, but the i tems of PISA are higher in the aspect of contextual, complexity, and reality in the daily life than the others

Algorithmic Complexity for Short Binary Strings Applied to Psychology: A Primer

Since human randomness production has been studied and widely used to assess executive functions (especially inhibition), many measures have been suggested to assess the degree to which a sequence is random-like. However, each of them focuses on one feature of randomness, leading authors to have to use multiple measures. Here we describe and advocate for the use of the accepted universal measure for randomness based on algorithmic complexity, by means of a novel previously presented technique using the the definition of algorithmic probability. A re-analysis of the classical Radio Zenith data in the light of the proposed measure and methodology is provided as a study case of an application.Comment: To appear in Behavior Research Method

Approximations of Algorithmic and Structural Complexity Validate Cognitive-behavioural Experimental Results

We apply methods for estimating the algorithmic complexity of sequences to behavioural sequences of three landmark studies of animal behavior each of increasing sophistication, including foraging communication by ants, flight patterns of fruit flies, and tactical deception and competition strategies in rodents. In each case, we demonstrate that approximations of Logical Depth and Kolmogorv-Chaitin complexity capture and validate previously reported results, in contrast to other measures such as Shannon Entropy, compression or ad hoc. Our method is practically useful when dealing with short sequences, such as those often encountered in cognitive-behavioural research. Our analysis supports and reveals non-random behavior (LD and K complexity) in flies even in the absence of external stimuli, and confirms the "stochastic" behaviour of transgenic rats when faced that they cannot defeat by counter prediction. The method constitutes a formal approach for testing hypotheses about the mechanisms underlying animal behaviour.Comment: 28 pages, 7 figures and 2 table