Automatic Generation of Cognitive Theories using Genetic Programming

Cognitive neuroscience is the branch of neuroscience that studies the neural mechanisms underpinning cognition and develops theories explaining them. Within cognitive neuroscience, computational neuroscience focuses on modeling behavior, using theories expressed as computer programs. Up to now, computational theories have been formulated by neuroscientists. In this paper, we present a new approach to theory development in neuroscience: the automatic generation and testing of cognitive theories using genetic programming. Our approach evolves from experimental data cognitive theories that explain “the mental program” that subjects use to solve a specific task. As an example, we have focused on a typical neuroscience experiment, the delayed-match-to-sample (DMTS) task. The main goal of our approach is to develop a tool that neuroscientists can use to develop better cognitive theories

Working memory and working attention: What could possibly evolve?

The concept of “working” memory is traceable back to nineteenth century theorists (Baldwin, 1894; James 1890) but the term itself was not used until the mid-twentieth century (Miller, Galanter & Pribram, 1960). A variety of different explanatory constructs have since evolved which all make use of the working memory label (Miyake & Shah, 1999). This history is briefly reviewed and alternative formulations of working memory (as language-processor, executive attention, and global workspace) are considered as potential mechanisms for cognitive change within and between individuals and between species. A means, derived from the literature on human problem-solving (Newell & Simon, 1972), of tracing memory and computational demands across a single task is described and applied to two specific examples of tool-use by chimpanzees and early hominids. The examples show how specific proposals for necessary and/or sufficient computational and memory requirements can be more rigorously assessed on a task by task basis. General difficulties in connecting cognitive theories (arising from the observed capabilities of individuals deprived of material support) with archaeological data (primarily remnants of material culture) are discussed

Integrating Data Mining and Social Network Techniques into the Development of a Web-based Adaptive Play-based Assessment tool for School Readiness.

A major challenge that faces most families is effectively anticipating how ready to start school a given child is. Traditional tests are not very effective as they depend on the skills of the expert conducting the test. It is argued that automated tools are more attractive especially when they are extended with games capabilities that would be the most attractive for the children to be seriously involved in the test. The first part of this thesis reviews the school readiness approaches applied in various countries. This motivated the development of the sophisticated system described in the thesis. Extensive research was conducted to enrich the system with features that consider machine learning and social network aspects. A modified genetic algorithm was integrated into a web-based stealth assessment tool for school readiness. The research goal is to create a web-based stealth assessment tool that can learn the user's skills and adjust the assessment tests accordingly. The user plays various sessions from various games, while the Genetic Algorithm (GA) selects the upcoming session or group of sessions to be presented to the user according to his/her skills and status. The modified GA and the learning procedure were described. A penalizing system and a fitness heuristic for best choice selection were integrated into the GA. Two methods for learning were presented, namely a memory system and a no-memory system. Several methods were presented for the improvement of the speed of learning. In addition, learning mechanisms were introduced in the social network aspect to address further usage of stealth assessment automation. The effect of the relatives and friends on the readiness of the child was studied by investigating the social communities to which the child belongs and how the trend in these communities will reflect on to the child under investigation. The plan is to develop this framework further by incorporating more information related to social network construction and analysis. Also, it is planned to turn the framework into a self adaptive one by utilizing the feedback from the usage patterns to learn and adjust the evaluation process accordingly

Learning Disabilities

Learning disabilities are a heterogeneous group of disorders characterized by failure to acquire, retrieve, or use information competently. They are the most severe and chronic form of learning difficulty in children. They can be present at birth or acquired as a result of illness, exposure to toxins, poor nutrition, medical treatment, sociocultural deprivation, or injury. Learning problems typically consist in failure to acquire reading, writing, or math skills, which are traditionally considered core domains. This book explores the epidemiology, neurobiological bases, and diagnostic tools necessary for a comprehensive assessment of children with learning disabilities. It also presents examples of children with specific learning disabilities and explains possible intervention strategies

European Research in Mathematics Education I, volume 2

International audienc

INQUIRIES IN INTELLIGENT INFORMATION SYSTEMS: NEW TRAJECTORIES AND PARADIGMS

Rapid Digital transformation drives organizations to continually revitalize their business models so organizations can excel in such aggressive global competition. Intelligent Information Systems (IIS) have enabled organizations to achieve many strategic and market leverages. Despite the increasing intelligence competencies offered by IIS, they are still limited in many cognitive functions. Elevating the cognitive competencies offered by IIS would impact the organizational strategic positions. With the advent of Deep Learning (DL), IoT, and Edge Computing, IISs has witnessed a leap in their intelligence competencies. DL has been applied to many business areas and many industries such as real estate and manufacturing. Moreover, despite the complexity of DL models, many research dedicated efforts to apply DL to limited computational devices, such as IoTs. Applying deep learning for IoTs will turn everyday devices into intelligent interactive assistants. IISs suffer from many challenges that affect their service quality, process quality, and information quality. These challenges affected, in turn, user acceptance in terms of satisfaction, use, and trust. Moreover, Information Systems (IS) has conducted very little research on IIS development and the foreseeable contribution for the new paradigms to address IIS challenges. Therefore, this research aims to investigate how the employment of new AI paradigms would enhance the overall quality and consequently user acceptance of IIS. This research employs different AI paradigms to develop two different IIS. The first system uses deep learning, edge computing, and IoT to develop scene-aware ridesharing mentoring. The first developed system enhances the efficiency, privacy, and responsiveness of current ridesharing monitoring solutions. The second system aims to enhance the real estate searching process by formulating the search problem as a Multi-criteria decision. The system also allows users to filter properties based on their degree of damage, where a deep learning network allocates damages in 12 each real estate image. The system enhances real-estate website service quality by enhancing flexibility, relevancy, and efficiency. The research contributes to the Information Systems research by developing two Design Science artifacts. Both artifacts are adding to the IS knowledge base in terms of integrating different components, measurements, and techniques coherently and logically to effectively address important issues in IIS. The research also adds to the IS environment by addressing important business requirements that current methodologies and paradigms are not fulfilled. The research also highlights that most IIS overlook important design guidelines due to the lack of relevant evaluation metrics for different business problems

Modelling the Developing Mind: From Structure to Change

This paper presents a theory of cognitive change. The theory assumes that the fundamental causes of cognitive change reside in the architecture of mind. Thus, the architecture of mind as specified by the theory is described first. It is assumed that the mind is a three-level universe involving (1) a processing system that constrains processing potentials, (2) a set of specialized capacity systems that guide understanding of different reality and knowledge domains, and (3) a hypecognitive system that monitors and controls the functioning of all other systems. The paper then specifies the types of change that may occur in cognitive development (changes within the levels of mind, changes in the relations between structures across levels, changes in the efficiency of a structure) and a series of general (e.g., metarepresentation) and more specific mechanisms (e.g., bridging, interweaving, and fusion) that bring the changes about. It is argued that different types of change require different mechanisms. Finally, a general model of the nature of cognitive development is offered. The relations between the theory proposed in the paper and other theories and research in cognitive development and cognitive neuroscience is discussed throughout the paper