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Tribhuvan university CSIT 2074 Syllabus Cognitive Science

Cognitive Science - Syllabus

Course Overview and Structure
Syllabus Notes Old Questions Unit Wise Questions Question Bank

Embark on a profound academic exploration as you delve into the Cognitive Science course () within the distinguished Tribhuvan university's CSIT department. Aligned with the 2074 Syllabus, this course (CSC374) seamlessly merges theoretical frameworks with practical sessions, ensuring a comprehensive understanding of the subject. Rigorous assessment based on a 60 + 20 + 20 marks system, coupled with a challenging passing threshold of , propels students to strive for excellence, fostering a deeper grasp of the course content.

This 3 credit-hour journey unfolds as a holistic learning experience, bridging theory and application. Beyond theoretical comprehension, students actively engage in practical sessions, acquiring valuable skills for real-world scenarios. Immerse yourself in this well-structured course, where each element, from the course description to interactive sessions, is meticulously crafted to shape a well-rounded and insightful academic experience.


Course Description:

This course covers the fundamental concepts of cognitive science and brain computation.

Course Objectives:

The main objective of this course is to provide basic knowledge of web cognition process,

mind theory, physical symbol systems, cognitive systems, concepts of brain mappings and

neural network structures.

Units

Cognition Process, Cognitive Psychology, Cognitive Science; Foundations of Cognitive Science, Cognitive Science and Multi-disciplinary; Machines and Minds; Laws thoughts to binary logic; Classical Cognitive Science; Connectionist Cognitive Science; Mind body Problem; Turing Response to Mind Body Problem; Pinker, Penerose and Searle‟s Responses to Mind Body Problem; Representational Theory of Mind; Theories of Mental Representation: Minimal Analysis of mental representation, Resemblance theories of mental representation, Casual covariation theories of mental representation, internal roles theories of mental representation

Key Topics

  • Introduction to Programming Language
    PR-01

    This topic introduces the concept of programming languages, their importance, and brief history. It sets the foundation for understanding the basics of programming.

  • Types of Programming Language
    PR-02

    This topic covers the different types of programming languages, including procedural, object-oriented, functional, and scripting languages. It explains the characteristics and uses of each type.

  • Language Processor
    PR-03

    This topic explains the role of a language processor, including compilers, interpreters, and assemblers. It discusses how they translate source code into machine code.

  • Program Errors
    PR-04

    This topic discusses the different types of program errors, including syntax, runtime, and logical errors. It explains how to identify, debug, and fix errors.

  • Features of Good Program
    PR-05

    This topic outlines the characteristics of a good program, including readability, maintainability, efficiency, and reliability. It provides guidelines for writing good programs.

  • Different Programming Paradigm
    PR-06

    This topic explores different programming paradigms, including procedural, object-oriented, functional, and declarative programming. It explains the principles and applications of each paradigm.

Cognitive Models of Memory, Atkinson-Shiffrin‟s Model, Tulving‟s Model, Mental Imagery, Kosslyn‟s View, Moyer‟s View, Peterson‟s View, Cognitive Maps, Problem Understanding, States of Cognition, Cognition in AI

Physical Symbol System Hypothesis; Symbol and Symbol Systems; Problem Solving by Symbol Structure; Physical Symbol System to Language of Thoughts; The Computer Model of the Mind; Syntax and the Language of Thought: Fodor‟s Argument for the Language of Thought Hypothesis; The Chinese Room Argument; Chinese Room and Turing Test; The Symbol Ground Problem

Cognitive System; Architecture for intelligent agents; Modularity of Mind; Modularity Hypothesis; The ACT-R/PM architecture

Structure and Function in Brain; Anatomical Connectivity; Cognitive Functioning Techniques from Neuroscience; Mapping the brain‟s electrical activity: EEG and MEG; Mapping the brain‟s blood flow and blood oxygen levels: PET and fMRI; Attention; Visuospatial attention

Metarepresentation; Metarepresentation, autism, and theory of mind; Mind Reading System; Understanding False Belief; Mind Reading as Simulation

Key Topics

  • Neurally Inspired Models of Information Processing
    NE-1

    This topic explores models of information processing inspired by the structure and function of the brain. It introduces the concept of neural networks and their application to information processing.

  • Single-Layer Networks and Boolean Functions
    NE-2

    This topic delves into the basics of single-layer neural networks and their relationship with Boolean functions. It covers the fundamentals of neural network architecture and its application to simple logical operations.

  • Multilayer Networks
    NE-3

    This topic builds upon the basics of single-layer networks and explores the architecture and functionality of multilayer neural networks. It discusses the advantages and limitations of multilayer networks in information processing.

  • Information Processing in Neural Networks
    NE-4

    This topic examines the process of information processing within neural networks. It covers how neural networks receive, process, and transmit information, and the implications for cognitive science.

  • Language Learning in Neural Networks
    NE-5

    This topic investigates the application of neural networks to language learning and processing. It explores how neural networks can be used to model language acquisition and understanding.

  • Neural Network Models of Children's Physical Reasoning
    NE-6

    This topic applies neural network models to the study of children's physical reasoning and cognitive development. It examines how neural networks can be used to understand and simulate children's problem-solving abilities.

Lab works

Laboratory Works:

The laboratory work includes implementing and simulating the concepts of cognition process,

intelligent agents, neural networks. In addition, laboratory work can be extended to use the

tools like PSY Toolkit, PsyNeuLink etc.