Understanding Animal Intelligence

Practical Behavior Theory for Biology, Psychology, and Cognitive Science
Erscheinungsjahr 2025
ISBN: 978-0-443-15730-1
Verlag: Elsevier Science

Buch, Englisch, 400 Seiten, Format (B × H): 191 mm x 235 mm

Understanding Animal Intelligence: Practical Behavior Theory for Biology, Psychology, and Cognitive Science synthesizes theories and practice to comprehend animal cognition. Using the framework of reinforcement learning, this book combines current knowledge of animal intelligence, focusing on decision-making, memory retrieval, associative learning, training, and more. It evaluates how evolutionary and environmental factors influence cognition and behavior. Additionally, the book explores motivational states and how these fulfill different goals, such as seeking food and water. Moreover, this book is a valuable reference and essential reading for courses in animal behavior, animal physiology, and ethology.

It provides a comprehensive analysis of animal intelligence by examining decision-making processes, memory retrieval, and associative learning. The book also delves into the interplay between evolutionary and environmental influences on cognition and behavior and demonstrates how learning can align with genetic predispositions.

Autoren/Hrsg.

Ghirlanda, Stefano

Enquist, Magnus

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Vorklinische Medizin: Grundlagenfächer Physiologie
• Naturwissenschaften Biowissenschaften Biowissenschaften
• Geowissenschaften Geographie | Raumplanung Humangeographie
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Naturwissenschaften Agrarwissenschaften Tierhaltung
• Naturwissenschaften Biowissenschaften Tierkunde / Zoologie
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Vorklinische Medizin: Grundlagenfächer Anatomie

Inhaltsverzeichnis

Part I: Fundamental questions
1. What does it mean to understand animal intelligence?
2. Different purposes of mechanistic, developmental, and evolutionary explanations
3. Describing behavior - responses to stimuli, individual history, evolutionary history
4. Modeling animal intelligence - decision-making, learning, and evolution

Part II: Decision-making
5. Using available information to select the best action - external stimuli, memories, and motivational states
6. Evaluation of current stimuli - perception, generalization, relationship with deep learning
7. Memory retrieval - selecting which information to use
8. Motivational systems - selecting which goal to pursue
9. Inborn contributions to decision-making strategies

Part III: Learning and development
10. Associative learning - a modern perspective on reinforcement learning
11. Specialized memory systems - purposes and algorithms
12. Genetic guidance of learning
13. Learned information
14. Maturation - changing behavioral mechanisms with age and experience
15. Training - teaching animals beyond their inherent scope

Part IV: Evolution of behavior
16. Evolution’s effect on learning and decision-making
17. Innate value landscapes
18. Co-evolution of animal intelligence with environmental demands

Über Autor(innen)

Enquist, Magnus
Dr. Magnus Enquist is a Professor of Ethology and the Director of the Centre for the Study of Cultural Evolution at Stockholm University. He received his PhD in Zoology from Stockholm University. His current research focuses on processes of cultural change - cultural evolution - and the differences between humans and other animals. He has published numerous articles, book chapters, and whole books on the evolution of behavior, including behavioral mechanisms such as recognition and learning.

Ghirlanda, Stefano
Dr. Stefano Ghirlanda is a Full Professor in the Department of Psychology at Brooklyn College. He received his PhD in zoology, with a concentration in ethology, from Stockholm University. He is also a founder and fellow of the Stockholm University Centre for the Study of Cultural Evolution. He considers himself a "behavior theorist" - he is interested in data-driven, theoretical accounts of all aspects of human and non-human behavior, including their neural underpinnings, and in particular learning and generalization, the architecture of behavior, and cultural evolution.