Problem solving : cognitive mechanisms and formal models / Zygmunt Pizlo, University of California, Irvine.

Format:

Book

Author/Creator:

Pizlo, Zygmunt, author.

Language:

English

Subjects (All):

Problem solving.

Cognitive psychology.

Neurosciences.

Physical Description:

1 online resource (xvi, 191 pages) : digital, PDF file(s).

Edition:

1st ed.

Place of Publication:

Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2022.

Summary:

Intelligent mental representations of physical, cognitive and social environments allow humans to navigate enormous search spaces, whose sizes vastly exceed the number of neurons in the human brain. This allows us to solve a wide range of problems, such as the Traveling Salesperson Problem, insight problems, as well as mathematics and physics problems. As an area of research, problem solving has steadily grown over time. Researchers in Artificial Intelligence have been formulating theories of problem solving for the last 70 years. Psychologists, on the other hand, have focused their efforts on documenting the observed behavior of subjects solving problems. This book represents the first effort to merge the behavioral results of human subjects with formal models of the causative cognitive mechanisms. The first coursebook to deal exclusively with the topic, it provides a main text for elective courses and a supplementary text for courses such as cognitive psychology and neuroscience.

Contents:

Cover

Half-title

Title page

Copyright information

Dedication

Contents

List of Figures

List of Tables

Preface

Chapter 1 Problem Solving: Definition of the Main Concepts

1.1 Gestalt Influence

1.2 Insight Problems: The Status of the ''Aha!'' Criterion

1.3 Search Problems

1.4 The Scientific Status of Goal-Directed Behavior

1.5 Forming Mental Representations

1.6 Problems to Solve

Chapter 2 Animal Problem Solving: Innovative Use of Tools

2.1 Early Research with Chimpanzees

2.2 The Role of Brain Size: How Carnivores Solve the Puzzle Box Problem

2.3 Self-Recognition in a Mirror

2.3.1 A New Test for the Presence of Consciousness

2.3.2 Why Does the Mirror Reflect Left and Right But Not Top and Bottom?

2.4 Chimpanzees' Visuomotor Coordination Using Camera Images

2.5 Innovative Problem Solving in Crows, Parrots, and Hyenas

2.5.1 Innovative Tool Use in Crows

2.5.2 Tool Use by Kea and New Caledonian Crows

2.5.3 Wild and Captive Spotted Hyenas

2.6 Visual Navigation: Chimps and Monkeys Solve the Traveling Salesman Problem

2.7 Problems to Solve

Chapter 3 Modern Research on the Human Ability to Solve Problems that Have Large Search Spaces

3.1 Permutations and Combinations

Polynomial and Exponential Numbers of Computations

3.2 Nearest Neighbor Algorithm for the TSP

3.3 Something Was In the Air: How the Cognitive Science Community Actually Discovered the TSP

3.3.1 The Role of Global Perceptual Factors: The Convex Hull and Clustering

3.3.2 Cognitive Challenge to AI

3.3.3 Perceptual vs. Analytical Processing

3.4 Problems to Solve

Chapter 4 The Exponential Pyramid Representation that Compensates for Exponentially Large Problem Spaces

4.1 Classification of Computational Complexity: P, NP, NP-Hard, NP-Complete.

4.2 The Exponential Pyramid as a Model of the Human Visual System

4.2.1 Speed-Accuracy Tradeoff

4.2.2 Mental Size Transformation

4.3 Pyramid Model for the TSP

4.3.1 Hierarchical Clustering: Self-Similar Operations

4.3.2 Coarse-to-Fine Solution Process Using a Pyramid Algorithm for the TSP

4.4 Solving the 2D and 3D TSP in Real and Virtual Environments: Perception Meets Problem Solving

4.5 Problems to Solve

Chapter 5 Heuristic Function, Distance, and Direction in Solving Problems

5.1 Heuristic Function and an A* Algorithm

5.2 Human Performance: The Concept of Direction

5.3 Continuous and Discrete Geometry of Direction and Distance

5.4 Pyramid Model for Solving the 15-Puzzle

5.5 Problems to Solve

Chapter 6 Insight and Creative Thinking

6.1 Scientific Discovery

6.1.1 Galileo's Law of Free-Fall

6.1.2 Archimedes's Law of the Lever

6.1.3 Symmetry of the Natural Laws

6.1.4 Einstein's Theories of Relativity

6.2 A Few More Brain Teasers Called Insight Problems

6.3 Broader Context for Insight

6.4 Problems to Solve

Chapter 7 Inference in Perception: Perceptual Representation: A Rejoinder to Insight

7.1 Gestalt Tradition: Solving Ill-Posed Problems and Their Relationship to Insight

7.2 Figure-Ground Organization and Curve Integration: Examples of Visual Inferences

7.3 Formalism of Forward and Inverse Problems

7.4 More on Implicit and Explicit Constraints in 3D Shape Recovery

7.5 Physics Connection: The Least-Action Principle

7.6 Data Mining and Knowledge Discovery

7.7 Problems to Solve

Chapter 8 Cognitive Inferences, Mental Representations

8.1 Multidimensional Scaling as a Tool for Data Visualization

8.2 Clustering Methods

8.3 Using Clusters to Explain Memory Organization

8.4 TSP with Obstacles

8.5 Problems to Solve

Chapter 9 Theory of Mind.

9.1 Visual Perspective Taking

9.2 Strategic Reasoning in Matrix Games

9.3 Problems to Solve

Chapter 10 Solving Problems in Physics and Mathematics

10.1 Physics Education

10.2 Intuitive Physics and Causal Reasoning

10.3 Solving Problems in Mathematics: Polya's Contributions

10.4 Problems to Solve

Chapter 11 Summary and Conclusions

11.1 Mental Representations

11.2 Scientific Discovery as Creative (Insightful) Problem Solving

11.3 Optimization Problems

11.4 Intuitive Physics

11.5 The Concept of Direction

11.6 Problems to Solve

References

Index.

Notes:

Title from publisher's bibliographic system (viewed on 23 Jun 2022).

ISBN:

1-009-20558-7

1-009-20557-9

1-009-20560-9

OCLC:

1338839942