Cognitive Science

The Basic Idea

Cognitive science is an interdisciplinary field of study designed to understand how the mind functions through the scientific method. In doing so, cognitive science allows us to better understand the concept of intelligence and therefore build intelligent systems.

Cognitive science tends to view mental processes as a function of various computations, connections, and symbols. An incredibly diverse and impactful field, cognitive science has been a key facet in the development of artificial intelligence, neural networks, and mental processes. Using techniques like computational modelling, brain imaging, and behavioral experiments, cognitive scientists have offered fresh perspectives on heavily studied concepts like language, attention, perception, and memory. While key insights primarily stem from psychology, linguistics, and computer science, cognitive science also incorporates perspectives from anthropology, neuroscience, and philosophy.

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Key Terms

Computation: The act of calculating an answer. Cognitive scientists view this as the underlying mechanism through which our minds process information and solve problems.

Associative Networks: In addition to computation, another key facet in cognitive science is associative networks. This viewpoint outlines that mental processes exist as interconnected networks which can be activated by a stimulus.

Neural Networks: Interconnected model of neurons which attempt to mimic the cognitive processes of the human brain. Can be both biological or artificial.  Neural networks are a key element in creating the “brain” of an artificial intelligence systems.

Mental Representations:  A theoretical concept in cognitive science which describes the mental symbols held in our minds. These allow us to imagine what something would look, feel, or act like, even if we have never experienced it before. For example, you can picture what it would be like to have a third arm, despite likely never having one. These representations allow us to abstractly imagine situations and solve problems with which we have little to no experience.


Since the time of Plato and Aristotle, people have studied the mystery of what goes on between our ears: the nature of the mind. Eventually, philosophical musings about the mind transformed into the scientific study of psychology.

In the early 20th century, psychology was dominated by a theoretical framework called behaviorism. Behaviorism viewed psychology as solely the study of behavior, and viewed living beings as learning creatures who pair stimuli with responses. Behaviorists only concerned themselves with observable reactions, and, due to the unobservable nature of our internal worlds, dismissed the mind as irrelevant. People were, for the most part, viewed as blank slates, filled with what they learned over their lives.

The behaviorist viewpoint dominated mainstream psychology until something strange happened in the early 1950s. In the 20 years prior, key developments in computer science gave way to the first modern computers, early neural networks, and a general theory of computation. All of a sudden, humans were capable of creating machines who, through the simple use of algorithms, were seemingly far more efficient at complex tasks than we were. Using these newfound discoveries, almost simultaneously, multiple researchers from various different fields began to produce and share new bodies of work.

Using these newfound theories of computation and associative networks, these academics ignited the cognitive revolution: an academic movement that challenged the learning-based approach of behaviorism. After all, computers never learned to associate problems with solutions; instead they used a few generalized algorithms which were designed to predict a variety of potential outcomes. This allowed psychologists, linguists, and other academics to question if humans used similar algorithms, heuristics, or other internal mechanisms to perform mental functions. In short, studying the mind was back in style.


Warren McCulloch and Walter Pitts

Cyberneticists in the 1940s who published a paper proposing the possibility of an artificial neural network. This paper began the push towards an associative view of intelligent systems.

Alan Turing

Viewed as the “father of theoretical computer science and artificial intelligence,” British mathematician Alan Turing played an indirect role during the early days of cognitive science. By formalizing concepts such as algorithms and computation with his famous Turing Machine, Turing laid the groundwork for theoretical concepts that would later be used as models of mental processing and intelligence.

George Miller

American psychologist and one of the founders of cognitive science. Miller disagreed with the behaviorist consensus that mental processes should be ignored, and instead believed them to be fundamental to understanding human behavior. As the founder of cognitive psychology, the majority of his studies revolved around psycholinguistics and working memory. Notably, he discovered the power of “chunking”: how breaking up information can allow us to circumvent the limits of mental processes.

Noam Chomsky

Now a renowned public intellectual, social critic, and linguist, Noam Chomsky once played a key role in the development of cognitive science. He first made a name for himself in the academic world by writing a brutal review of B.F. Skinner’s Verbal Behavior in 1959. Skinner, as a founder of behaviorist theory, proposed that language was a learned behavior between parents and children. Chomsky disagreed, and convincingly argued that we have innate linguistic structures which allow us to learn languages at a young age, despite limited input from our parents. He argued this point so convincingly that his theory of generative grammar became a mainstay in modern linguistic thought and a key building block for cognitive science’s theory of mental representations.

John McCarthy and Marvin Minsky

Each of these individuals were incredibly important pioneers in AI research. First, Minsky developed the first artificial neural network using vacuum tubes. Together, McCarthy and Minksy coined the term “artificial intelligence” and provided the majority of theoretical backing for the idea. McCarthy then developed Lisp, a key programming language for modelling intelligent systems.

Allen Newell and Herbert Simon

Key pioneers in AI research, Newell and Simon are most prominently known for their development of the first artificial intelligence program.


In the aftermath of the cognitive revolution, the cognitive viewpoint overtook behaviorism, becoming the dominant lens from which psychology was studied. Furthermore, cognitive science carved a legitimate stake for itself as its own field of academic study. There now exist multiple departments, journals, professors, and degrees specifically tailored toward understanding and forwarding cognitive science.

Key findings in the field of cognitive science have been used to help us understand decision-making. As decision-making is often roped into problem-solving, it makes sense that examining the mind from a computational, problem-focused standpoint is advantageous. Cognitive science has delivered on this promise: the majority of evidence about limited short-term memory, divided attention, use of heuristics, and warped perception of risk come from studying the brain from this cognitive perspective.

Aside from decision-making, cognitive science has broadened our perspectives on other social, linguistic, and mental phenomena. It has been used to understand how the brain’s functional systems operate, providing insights into processes such as how we perceive, learn, and think. Furthermore, cognitive science has also been useful in understanding mental disorders and dysfunctions, offering an integrated framework to understand multiple psychiatric phenomena. In the realm of linguistics, cognitive linguistics, through its theories like generative grammar and natural language, has come to revolutionize academic practice. Finally, cognitive science has allowed people to develop better understandings of social psychological processes such as persuasion, coercion, and negotiation.

While these discoveries are important, they are overshadowed by one looming development: artificial intelligence. The prospect of a thinking machine has long been an idea that both worries, excites, and intrigues people, but only recent developments in cognitive science have made it a reality. Using tools such as neural networks, cognitive scientists have come to view the mind as a computational network and been able to drastically increase our artificial intelligence capabilities.

While the prospect of a hyper-intelligent, HAL-9000-esq computer is still a distant fantasy, artificial intelligence has already come to revolutionize our day-to-day lives in many subtle ways, ranging from your daily Spotify playlists to helping radiologists detect cancer in patients. Looking towards the future, improving artificial intelligence could be a key factor in how history unravels. In such an innovative period in history, our increasing capabilities, understanding, and ethical choices regarding this powerful tool could be key in either healing or worsening societal ills, such as climate change and global inequities.


While cognitive science has brought significant developments, it has also left us with many questions. For example, while cognitive science has attempted to tackle the issue, consciousness still remains a major hurdle. Given that cognitive science is the scientific study of the mind, one would think consciousness would lay within its purview. However, the field might be more similar to behaviorism than we think, as it too relies on observation of outward display of mental functions. After all, how would we study consciousness without observable stimuli like facial expressions? Moreover, how do you observe the thing that allows you to observe all things? While a growing number of cognitive scientists are attempting to understand this difficult problem, consciousness may present a key limitation in cognitive science.

It is also understandable why the ideas that fuel the majority of cognitive science research can make people uncomfortable. Fundamentally, the ideas presented threaten our understanding of what it means to be human. If life is purely computations, 0’s and 1’s, and associative networks, what does that say about free will, moral responsibility, and the purpose of human existence? What separates us from simple machines, capable of being predicted, modelled, and understood?

Luckily, existential questions like these are being addressed by the philosophical angle of cognitive science. This viewpoint attempts to integrate both the valuable discoveries made by cognitive science and philosophical thought to help us to understand what these discoveries could mean for our understanding of our physical world, the mind, consciousness, religion, ethics, and epistemology. While this combination still has many hurdles to overcome, as cognitive scientist L.A. Paul stated, “I have quite a bit of sympathy for the idea that psychology and cognitive science have much to offer philosophy, and that the reverse is true as well.” 

Related TDL Content

Cognitive Science Can Improve Decision Making

Cognitive science is a broad yet powerful field. In this piece, The Decision Lab’s Julian Hazell sits down with Michał Klincewicz, assistant professor of Cognitive Science at Tilburg University, to learn more about using cognitive science to solve real-world problems. From analyzing how individuals make moral decisions to designing a machine learning algorithm that finds conspiracy videos on YouTube, this piece really dives into the breadth and utility of cognitive science.

Combining AI and Behavioral Science Responsibly

Perhaps the most impactful development from cognitive science is artificial intelligence (AI). Day by day, developments in AI seem to rapidly expand its power and influence in our lives. Many have begun to ring the alarm bells about the ethical concerns of such a technology, especially when it is paired with behavioral science. The Decision Lab’s Julian Hazell examines this moral dilemma and attempts to navigate through it, both ethically and responsibly.


  1. Thagard, P. (2018, September 24). Cognitive science. Retrieved April 16, 2021, from
  2. Graham, G. (2019, March 19). Behaviorism. Retrieved April 16, 2021, from
  3. Miller, G. A. (2003). The cognitive revolution: A historical perspective. Trends in Cognitive Sciences, 7(3), 141-144. doi:10.1016/s1364-6613(03)00029-9
  4. Prezenski, S., Brechmann, A., Wolff, S., & Russwinkel, N. (2017). A cognitive modeling approach to strategy formation in dynamic decision making. Frontiers in Psychology, 8. doi:10.3389/fpsyg.2017.01335
  5. CogniFit Research. (2016, June 08). CogniFit. Retrieved April 16, 2021, from
  6. Stein, D. J. (1993). Cognitive science and psychiatry: An overview. Integrative Psychiatry, 9(1), 13–24.
  7. Jackendoff, R. (2007). Linguistics in cognitive science: The state of the art. The Linguistic Review, 24(4). doi:10.1515/tlr.2007.014
  8.  Eagly, A. H., & Chaiken, S. (1984). Cognitive theories of persuasion. Advances in Experimental Social Psychology, 17, 267-359. doi:10.1016/s0065-2601(08)60122-7
  9. Sen, I. (2018, November 26). How AI HELPS Spotify win in the music streaming world. Retrieved April 16, 2021, from
  10. Savage, N. (2020, March 25). How ai is improving cancer diagnostics. Retrieved April 16, 2021, from
  11.  Snow, J. (2021, February 10). How artificial intelligence can tackle climate change. Retrieved April 16, 2021, from
  12. Sayer, L. (2016). Inequality in an increasingly automated world. In 1314153996 965833893 UNESCO (Ed.), World social science report, 2016: Challenging inequalities, pathways to a just world, (pp. 177-179). UNESCO.
  13.  See 1.
  14. L.A. Paul. (n.d.). L.A. Paul Quote. Retrieved April 16, 2021, from

About the Authors

Dan Pilat's portrait

Dan Pilat

Dan is a Co-Founder and Managing Director at The Decision Lab. He is a bestselling author of Intention - a book he wrote with Wiley on the mindful application of behavioral science in organizations. Dan has a background in organizational decision making, with a BComm in Decision & Information Systems from McGill University. He has worked on enterprise-level behavioral architecture at TD Securities and BMO Capital Markets, where he advised management on the implementation of systems processing billions of dollars per week. Driven by an appetite for the latest in technology, Dan created a course on business intelligence and lectured at McGill University, and has applied behavioral science to topics such as augmented and virtual reality.

Sekoul Krastev's portrait

Dr. Sekoul Krastev

Sekoul is a Co-Founder and Managing Director at The Decision Lab. He is a bestselling author of Intention - a book he wrote with Wiley on the mindful application of behavioral science in organizations. A decision scientist with a PhD in Decision Neuroscience from McGill University, Sekoul's work has been featured in peer-reviewed journals and has been presented at conferences around the world. Sekoul previously advised management on innovation and engagement strategy at The Boston Consulting Group as well as on online media strategy at Google. He has a deep interest in the applications of behavioral science to new technology and has published on these topics in places such as the Huffington Post and Strategy & Business.

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