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Procedural knowledge

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Changing a flat tire is a form of procedural knowledge.

Procedural knowledge is the type of knowledge that helps you know how to do something, even if you cannot always explain exactly how you are doing it. This includes skills like riding a bike, typing on a keyboard without looking, or solving math problems by following steps you have practiced.[1] It is different from declarative knowledge, which is about knowing facts, like knowing that the capital of France is Paris. With procedural knowledge, you are doing something, not just remembering a fact.[2] Often, people learn these skills through repetition and practice, and over time the task becomes automatic. This process, called automatization, means you can perform the skill without thinking about each step.[3]

In the brain, procedural knowledge is stored in areas like the basal ganglia and cerebellum, which control movement and habits.[4] This part of the brain works differently from the part that stores facts.[5] A famous example is a patient known as H.M., who lost the ability to form new memories of facts (declarative knowledge) but could still learn new physical tasks like drawing a shape in a mirror. This shows that our brains keep procedural and declarative knowledge in separate systems.[6] Teachers and scientists who study how people learn often explain that procedural knowledge starts with declarative knowledge. For example, at first you might need to read instructions on how to do a math problem. But after doing it many times, you no longer need to think about the steps, you just know how to do it.[7] One model that describes this process is called ACT-R, which says we build "if-then" rules in our brains (like "If I see this kind of problem, then I do this step next") through repeated practice.[8]

In computers and robots, procedural knowledge is used as step-by-step instructions, like a recipe. A cooking robot, for example, might be programmed to follow steps in a certain order: heat the pan, add ingredients, stir for 5 minutes, and so on. These are examples of algorithms, which are clear sets of instructions that computers follow to complete tasks.[9] Procedural knowledge also helps us use language without thinking about grammar rules. Native speakers of a language can usually tell when a sentence “sounds wrong,” even if they cannot explain the grammar behind it. That is because their brain uses procedural knowledge from years of listening and speaking.[10]

In real-life jobs and activities, procedural knowledge is very important. Surgeons, pilots, athletes, musicians, and firefighters all need to practice routines over and over until they can do them perfectly, especially in high-pressure situations.[11] They often use simulations, like flight simulators or surgical practice tools, to safely train these skills before doing them in real life.[12] Since procedural knowledge is about doing, it is usually tested in ways that involve performance, not just written tests. You might take a practical exam, play a role in a scenario, or show how to complete a task.[13] This type of knowledge also connects to metacognition, which means knowing when and how to use what you know, for example, choosing the best way to solve a problem based on the situation.[14]

In workplaces, procedural knowledge is part of training materials like instruction manuals or step-by-step guides. But a lot of it also lives in people's heads and is not always written down, which can be a problem if someone leaves the job and takes that knowledge with them.[15] That is why organizations try to capture and share this kind of knowledge, especially in important areas like healthcare or nuclear power where safety is critical.[16] Procedural knowledge can sometimes become outdated. For example, the way a company used to fix machines might no longer be the best method. That is why businesses use systems like Six Sigma or lean methods to constantly check and improve how tasks are done.[17]

References

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  1. ↑ Anderson, John R. (1982). "Acquisition of cognitive skill". Psychological Review. 89 (4): 369–406. doi:10.1037/0033-295X.89.4.369. ISSN 1939-1471.
  2. ↑ Squire, Larry R. (2004-11-01). "Memory systems of the brain: A brief history and current perspective". Neurobiology of Learning and Memory. Multiple Memory Systems. 82 (3): 171–177. doi:10.1016/j.nlm.2004.06.005. ISSN 1074-7427.
  3. ↑ Logan, Gordon D. (1988). "Toward an instance theory of automatization". Psychological Review. 95 (4): 492–527. doi:10.1037/0033-295X.95.4.492. ISSN 1939-1471.
  4. ↑ Doyon, Julien; Bellec, Pierre; Amsel, Rhonda; Penhune, Virginia; Monchi, Oury; Carrier, Julie; Lehéricy, Stéphane; Benali, Habib (2009-04-12). "Contributions of the basal ganglia and functionally related brain structures to motor learning". Behavioural Brain Research. Special issue on the role of the basal ganglia in learning and memory. 199 (1): 61–75. doi:10.1016/j.bbr.2008.11.012. ISSN 0166-4328.
  5. ↑ Squire, Larry R.; Zola, Stuart M. (1996-11-26). "Structure and function of declarative and nondeclarative memory systems". Proceedings of the National Academy of Sciences. 93 (24): 13515–13522. doi:10.1073/pnas.93.24.13515. PMC 33639. PMID 8942965.
  6. ↑ Corkin, Suzanne (2002). "What's new with the amnesic patient H.M.?". Nature Reviews Neuroscience. 3 (2): 153–160. doi:10.1038/nrn726. ISSN 1471-0048.
  7. ↑ Anderson, John R.; Bothell, Daniel; Byrne, Michael D.; Douglass, Scott; Lebiere, Christian; Qin, Yulin (2004). "An Integrated Theory of the Mind". Psychological Review. 111 (4): 1036–1060. doi:10.1037/0033-295X.111.4.1036. ISSN 1939-1471.
  8. ↑ Anderson, John R.; Bellezza, Francis S. (1993). Rules of the mind. Hillsdale, N.J.: Erlbaum. ISBN 978-0-8058-1200-8.
  9. ↑ "Artificial Intelligence: A Modern Approach, 4th US ed". aima.cs.berkeley.edu. Retrieved 2025-08-01.
  10. ↑ Reber, Arthur S. (1989). "Implicit learning and tacit knowledge". Journal of Experimental Psychology: General. 118 (3): 219–235. doi:10.1037/0096-3445.118.3.219. ISSN 1939-2222.
  11. ↑ Ericsson, K. Anders; Krampe, Ralf T.; Tesch-Römer, Clemens (1993). "The role of deliberate practice in the acquisition of expert performance". Psychological Review. 100 (3): 363–406. doi:10.1037/0033-295X.100.3.363. ISSN 1939-1471.
  12. ↑ Lateef, Fatimah (2010). "Simulation-based learning: Just like the real thing". Journal of Emergencies, Trauma, and Shock. 3 (4): 348–352. doi:10.4103/0974-2700.70743. ISSN 0974-519X. PMC 2966567. PMID 21063557.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  13. ↑ Wiggins, Grant P. (1993). Assessing student performance: exploring the purpose and limits of testing. The Jossey-Bass education series (1st ed.). San Francisco, Calif: Jossey-Bass. ISBN 978-1-55542-592-0.
  14. ↑ Flavell, John H. (1979). "Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry". American Psychologist. 34 (10): 906–911. doi:10.1037/0003-066X.34.10.906. ISSN 1935-990X.
  15. ↑ Nonaka, Ikujirō (1995). The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Hirotaka Takeuchi (1st ed.). Oxford: Oxford University Press, Incorporated. ISBN 978-0-19-509269-1.
  16. ↑ Wiig, Karl M. (1993). Knowledge management foundations: thinking about thinking: how people and organizations create, represent, and use knowledge. Arlington, Tex: Schema Press. ISBN 978-0-9638925-0-8.
  17. ↑ George, Michael L. (2002). Lean Six Sigma: combining Six Sigma quality with lean speed. New York: McGraw-Hill. ISBN 978-0-07-138521-3.
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