In This Article Expand or collapse the "in this article" section Artificial Intelligence, Machine Learning, and Psychology

  • Introduction
  • General Overviews

Psychology Artificial Intelligence, Machine Learning, and Psychology
Chong Ho Yu
  • LAST MODIFIED: 26 October 2023
  • DOI: 10.1093/obo/9780199828340-0323


In 2019, the United States established a national task force for coordinating AI strategies across the federal government, industry, and academia in order to promote scientific discovery, economic competitiveness, and national security. In addition, the 2023 Organisation for Economic Co-operation and Development’s report indicates that AI has become a global competition. Hence, AI is here to stay and this trend is too important to ignore or downplay. From the incubation and development of AI, the relationship between AI and psychology is symbiotic. As cognitive psychologists and neuroscientists gain more insight into how the brain works, AI has been developed by mimicking human neural pathways. On the other hand, insights derived from AI research can be applied to a variety of subfields in psychology, whereas new social issues emerged from AI applications, such as AI bias, echo chambers, and misuse of AI tools (e.g., ChatGPT and Midjourney), led to new research topics in psychology. Further, several schools of thought of AI, such as the connectionist, symbolic, and analogist approaches, heavily borrowed ideas from psychological research. For example, in an attempt to build structural intelligence, connectionists draw an analogy between human neural networks and artificial neural networks. Moreover, AI symbolists subscribe to the notion that the human mental process is a logical production system. Furthermore, prior psychological research indicates that analogical thinking is commonly employed for problem-solving, and this notion became the foundation of example-based machine learning. Similarly, reinforcement learning in AI was inspired by behavioral psychology. Additionally, in line with the findings of developmental psychology that a child learns best through spontaneous discovery, AI researchers believe that setting the deep learning system free will be the most promising research direction. A vast number of pioneers of AI research who devoted efforts to the preceding research agendas are psychologists or received training in psychology, such as Frank Rosenblatt, Allen Newell, John Anderson, David Rumelhart, and Geoffrey Hinton, whereas some were inspired by cognitive science or neuroscience, such as Fei-Fei Li, Demis Hassabis, and Yann LeCun. In addition, in their work Artificial Psychology: Psychological Modeling and Testing of AI Systems (cited under Artificial Psychology) Crowder and his colleagues examined the bidirectional relationship between artificial intelligence and psychology, focusing on cognitive architectures and artificial emotions. The authors proposed comprehensive mathematical models for cognitive architectures, envisioning an AI system capable of reasoning about emotions, adapting to humans, and constructing knowledge representations based on experiences.

General Overviews

There are many books that illustrate the history and concepts of artificial intelligence and machine learning (AI/ML). Russell and Norvig 2021 is considered one of the most comprehensive introductions to AI/ML. Another book that explains AI through a historical perspective is Metz 2021, which is written using a storytelling and accessible approach. The book shows how psychologists and scholars in other disciplines jointly contributed to the development of AI. For a more concise introduction to the history of AI, Barbet 2020 is recommended. Besides introducing the internal development of AI schools, Barbet also discussed external factors contributing to the movement. Domingos 2015 compares and contrasts the five schools of AI; namely, symbolism, connectionism, Bayesianism, analogists, and evolutionists. These five schools of thought can be further explored by checking out the sources listed in the book. Today the terms “data science” (DS) and “machine learning” (ML) are often used in tandem. Although originally DS and ML were two separate movements, in the early twenty-first century they merged together as an integrated entity because both methodologies emphasized how to extract patterns and trends from the data by exploration. To understand the conceptual relationships between DS and ML, consult Yu 2022. Most DSML books are tied to specific platforms or languages (e.g., Python, R, SAS®, etc.), but it is important not to equate DSML with any particular software tool, or to confuse computing programming with DSML-based analytics. As an example, LISP was one of the first programming languages that has traditionally been associated with AI computing. LISP, however, lost popularity over time to other programming languages. It is advised that readers learn DSML conceptually and keep an open mind when dealing with various AI tools. While in DSML, prediction, recommendation, and optimizing search for patterns and associations from big data is more central than explanation. Pearl and Mackenzie 2018 drew the attention of researchers to causal inferences by introducing the Bayesian probabilistic inference network. A number of books about the relationship between AI and psychology have been published. Schneider 2019 is both psychological and philosophical. Specifically, the book discusses the meanings of the self, the mind, and consciousness in the context of AI. Brian Christian works closely with cognitive scientists to study technological impacts on human lives. Christian 2020 discusses the challenge that the goal of AI machines might not fully align with human values.

  • Barbet, Jacques. 2020. The maturation of artificial intelligence. In Therapeutic progress in oncology: Towards a revolution in cancer therapy? Edited by Jacques Barbet, Adrien Foucquier, and Yves Thomas, 79–109, New York: Wiley.

    Besides covering different approaches to AI in terms of cognitive models and mathematics, the author also discusses other factors that facilitate AI development, such as the support from the government and tech giants, the availability of big data for data mining, and the invention of power computing technologies (e.g., GPU).

  • Christian, Brian. 2020. The alignment problem: Machine learning and human values. New York: W. W. Norton.

    This book discusses the ethical and psychological challenges that occur when the goals of AI systems and human values are misaligned. A critical analysis of how embedded biases impact decision-making and social impact in AI models is presented in the book. The book uses a storytelling approach and lay terms.

  • Domingos, Pedro. 2015. The master algorithm: How the quest for the ultimate learning machine will remake our world. New York: Basic Books.

    This book explores five different schools of AI, including the symbolist, connectionist, Bayesian, evolutionary, and analogical approaches. Domingos suggests integrating different tribes together in order to develop a more holistic approach. This book is highly accessible.

  • Metz, Cade. 2021. Genius makers: The mavericks who brought AI to Google, Facebook, and the world. New York: Dutton.

    The book chronicles the achievements, struggles, and visions of several AI mavericks, including Geoffrey Hinton, Yann LeCun, and Yoshua Bengio. It also includes the voice of opposition to deep learning from Gary Marcus. The author uses a narrative approach, and thus the book is highly readable.

  • Pearl, Judea, and Dana Mackenzie. 2018. The book of why: The new science of cause and effect. New York: Basic Book.

    The first author, Pearl, is a Turing Award Laureate. This book explores the history of causal inference, a fundamental concept of research that is seldom discussed among machine learning researchers. One of the core messages of the book is that for causal inferences researchers need the right data, not necessarily big data.

  • Russell, Stuart, and Peter Norvig. 2021. Artificial intelligence: A modern approach. 4th ed. Harlow, UK: Pearson.

    This book presents a comprehensive overview of AI, including its history, concepts, techniques, and applications. It covers the historical development of AI from the symbolic approach to more recent statistical and connectionist methods.

  • Schneider, Susan. 2019. Artificial you: AI and the future of your mind. Princeton, NJ: Princeton Univ. Press.

    DOI: 10.2307/j.ctvfjd00r

    The purpose of this book is to examine the potential impact of AI on our understanding of consciousness, identity, and personhood from a psychological standpoint. There are thought-provoking questions raised about the relationship between human psychology and AI, but some issues are hypothetical and might not be strongly relevant to AI users.

  • Yu, Chong Ho. 2022. Data mining and exploration: From traditional statistics to modern data science. Boca Raton, FL: CRC Press.

    This book is a user-friendly introduction to both conceptual and procedural aspects of cutting-edge data science and machine learning methods, such as neural networks, bagging, and boosting. Unlike other books that are tied to a particular software application, this book covers a variety of tools, including SAS®, JMP®, SPSS®, and Python.

back to top

Users without a subscription are not able to see the full content on this page. Please subscribe or login.

How to Subscribe

Oxford Bibliographies Online is available by subscription and perpetual access to institutions. For more information or to contact an Oxford Sales Representative click here.