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Chapter Thirteen

This chapter explores the benefits of cognitive science, including its influence in various disciplines and practical applications in medicine, engineering, and robotics. It emphasizes the importance of cross-disciplinary integration in understanding working memory, with perspectives from philosophy, psychology, cognitive science, neuroscience, networks, evolution, linguistics, artificial intelligence, and robotics. The chapter also discusses the current issues faced by cognitive science, such as the lack of a unified theory, and the need for considering emotions, consciousness, physical and social environments, and individual and cultural differences. It concludes with the evaluation of cognitive science theories and the importance of integration across levels of description, disciplines, and methodologies.

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Chapter Thirteen

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  1. Chapter Thirteen Conclusion: Where We Go From Here

  2. The benefits of cognitive science • Brings together diverse theoretical perspectives. • Widespread influence of cognitive theory on other disciplines and in education. • Practical applications in medicine, engineering, and robotics. • Advances in diagnosis and treatment of disease and disorders.

  3. Working together on working memory Cross-disciplinary integration can help us to better understand the relationship between a given mental Process such as working memory and other mental processes.

  4. Working together on working memory • Philosophy. Asks critical questions about the process. • Restricted to visual & spatial representations? • How are numbers represented? • Do these representations have meaning? • Is there a single central executive?

  5. Working together on working memory • Psychology (non-cognitive). Application of different theoretical lenses to the process. • Does wm create Gestalts during problem solving?

  6. Working together on working memory • Cognitive. Formulation of information- processing models of the process. • Experimentally test process models. • How do models of different cognitive processes constrain each other?

  7. Working together on working memory • Neuroscience. Anatomy and physiology of process. • Do connections between psychological models correspond to neural connectivity between brain regions that implement them?

  8. Working together on working memory • Networks. Create ANNs to simulate the process and/or semantic networks to study how knowledge is represented in the process. • Implement ANN that rehearses verbal information. • Implement ANN that rotates mental images.

  9. Working together on working memory • Evolution. Focus on the problem the process is designed to solve and how it originated. Implement evolutionary algorithms. • What adaptive roles are served by different wm functions?

  10. Working together on working memory • Linguistics. What is the role of language in the process? How does the process interface with language? • What is the role of wm in language comprehension and production?

  11. Working together on working memory • Artificial Intelligence. Design and run computer algorithms that will perform the process. • How is wm used to search for a target among the items of a list? • Explain human weakness in retrieving long lists. (List the 50 states of the US.)

  12. Working together on working memory • Robotics. Design and test robots that will implement the process and use it in a real- world environment. • How is wm used as interface between perception and action? • Plot a path through a cluttered room.

  13. Issues in cognitive science • Lack of a unified theory. • The nature of mental representation and computation still debated. • Classic information processing vs. connectionism.

  14. Cognitive science and the real world • Cognitive science has a hard time accounting for processes that must interact with the complexity of the physical world. This is just one of the issues it must confront.

  15. Issues in cognitive science • Emotions. There is no adequate account of emotions or of how they interact with cognition. • Consciousness. A fundamental mystery. No agreement on what it is or how to understand it. • Physical environments. Most cognitive science is done in simple controlled scenarios instead of complex real-word situations. • Social environments. Most cognitive processes are individualized instead of in social or ecological environments.

  16. Issues in cognitive science • Individual and cultural differences. Greater consideration and study of these needed.

  17. Evaluating cognitive science theories • Five considerations (Thagard, 2000): • Representational power. • Computational power. • Psychological plausibility. • Neurological plausibility. • Practical applicability.

  18. Integration in cognitive science • Integration across levels of description. The need for theories and models that specify the implementation, algorithmic, and computational levels. • Integration across disciplines. Need for collaborative interdisciplinary work. • Integration across methodology. Need for synergistic use of multiple methods.

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