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Procedural knowledge
Procedural knowledge
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Procedural knowledge (also known as know-how, knowing-how, and sometimes referred to as practical knowledge, imperative knowledge, or performative knowledge)[1] is the knowledge exercised in the performance of some task. Unlike descriptive knowledge (also known as declarative knowledge, propositional knowledge or "knowing-that"), which involves knowledge of specific propositions (e.g. "I know that snow is white"), in other words facts that can be expressed using declarative sentences, procedural knowledge involves one's ability to do something (e.g. "I know how to change a flat tire"). A person does not need to be able to verbally articulate their procedural knowledge in order for it to count as knowledge, since procedural knowledge requires only knowing how to correctly perform an action or exercise a skill.[2][3]

The term procedural knowledge has narrower but related technical uses in both cognitive psychology and intellectual property law.

Overview

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Procedural knowledge (i.e., knowledge-how) is different from descriptive knowledge (i.e., knowledge-that) in that it can be directly applied to a task.[2][4] For instance, the procedural knowledge one uses to solve problems differs from the declarative knowledge one possesses about problem solving because this knowledge is formed by doing.[5]

The distinction between knowing-how and knowing-that was brought to prominence in epistemology by Gilbert Ryle who used it in his book The Concept of Mind.[3]

Know-how is also often referred to in layman's terms as street smarts (sometimes conceived as the opposite of book smarts), and a person employing their street smarts as street wise. Know-how is often tacit knowledge, which means that it can be difficult to transfer to another person by means of writing it down or verbalising it. The opposite of tacit knowledge is explicit knowledge.

Definition

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Procedural knowledge is the "know how" attributed to technology defined by cognitive psychologists, which is simply "know how to do it" knowledge. Part of the complexity of it comes in trying to link it to terms such as process, problem solving, strategic thinking and the like, which in turn requires distinguishing different levels of procedure.[6] It is the ability to execute action sequences to solve problems. This type of knowledge is tied to specific problem types and therefore is not widely generalizable.[7] Procedural knowledge is goal-oriented and mediates problem-solving behavior.[8]

The concept of procedural knowledge is also widely used in mathematics educational researches. The well-influential definition of procedural knowledge in this domain comes from the introductory chapter by Hiebert and Lefevre (1986) of the seminal book "Conceptual and procedural knowledge: The case of mathematics", in which they divided procedural knowledge into two categories. The first one is a familiarity with the individual symbols of the system and with the syntactic conventions for acceptable configurations of symbols. The second one consists of rules or procedures of solving mathematical problems. In other words, they define procedural knowledge as knowledge of the syntax, steps conventions and rules for manipulating symbols.[9] Many of the procedures that students possess probably are chains of prescriptions for manipulating symbols. In their definition, procedural knowledge includes algorithms, which means if one executes the procedural steps in a predetermined order and without errors, one is guaranteed to get the solutions, but not includes heuristics, which are abstract, sophisticated and deep procedures knowledge that are tremendously powerful assets in problem solving. [10] Therefore, Star (2005) proposed a reconceptualization of procedural knowledge, suggesting that it can be either superficial, like ones mentioned in Hiebert and Lefevre (1986), or deep.[11][9] Deep procedural knowledge is associated with comprehension, flexibility and critical judgement. For example, the goals and subgoals of steps, the environment or type of situation for certain procedure, and the constraints imposed upon the procedure by the environment.[12] Research on procedural flexibility development indicates flexibility as an indicator for deep procedural knowledge. Individuals with superficial procedural knowledge can only use standard technique, which might lead to low efficiency solutions and probably inability to solve novel questions. However, more flexible solvers, with a deep procedural knowledge, can navigate their way through domain, using techniques other than ones that are over-practiced, and find the best match solutions for different conditions and goals. [13][11][14]

Development

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The development of procedural knowledge is always entangled with the development of declarative knowledge. Researchers suggested that initial problem solving involves explicitly referring to examples and participants start with pure example-based processing.[15][16] The examples illustrate the solution of a similar problem and the problem solver analogically maps the solution of the example onto a solution for the current problem. People make extensive reference to examples even when they are initially taught the rules and principles.[17] It is believed that when people acquire cognitive skills, first an example is encoded as a declarative structure. When participants are tested on their first problems, they have two possible ways to respond. If the example matches the problem they learned, they can simply retrieve the answer. However, if it does not match, they must analogically extend the example.[16] With repeated practice, general rules develop and the specific example is no longer accessed. In this way, knowledge transitions from a declarative form (encoding of examples) to a procedural form (production rules), which is called the adaptive control of thought—rational (ACT-R) theory.[18]

However, on certain occasions, procedural and declarative knowledge can be acquired independently. Research with amnesiac patients found that they can learn motor skills without the ability to recollect the episodes in which they learned them. The research also found that the patients learned and retained the ability to read mirror-reversed words efficiently, yet were severely impaired in recognizing those words. This research gives evidence about the neurological differences between procedural and declarative knowledge.[19][20] Researchers also found that some normal subjects, like amnesiac patients, showed substantial procedural learning in the absence of explicit declarative knowledge. Even though declarative knowledge may influence performance on a procedural task, procedural and declarative knowledge may be acquired separately and one does not need to have knowledge of one type in order to build the other type. The influence of declarative knowledge may be due to the facilitation of a process of pathway activation that is outside of conscious awareness.[21] If the prime is highly predictive of the target, the amount of facilitation is increased because of an active, conscious, attentional effect that is superimposed on the pathway activation.[22] Therefore, if and when subjects develop explicit declarative knowledge of procedure, they can use this knowledge to form attentional expectancies regarding the next item in this procedure.[21]

Activation

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Lashley (1951) proposed that behavioral sequences are typically controlled with central plans, and the structure of the plans is hierarchical. Some evidences also support this hypothesis. Same behaviors can have different functional interpretations depending on the context in which they occur. The same sound pattern can be interpreted differently depending on where it occurs in a sentence, for example, there and their. Such contextual dependence is only possible with functionally overarching states of the sort implied by hierarchical plans. [23] The initiation time of a movement sequence and the inter-response times of the sequence elements can increase with its length.[24] Further, inter-response times can depend on the size of the phrase that is about to be generated. The larger the phrase, the longer the inter-response time.[25][26] Such data have been interpreted in terms of decoding or unpacking hierarchical plans into their constituents. Moreover, learning difficulties changes with the easiness of behavioral sequences.[27][28] Finally, long-term learning of skills is naturally characterized by the process of forming ever larger hierarchical units or chunks.[24] People learn control structures for successively larger units of behavior, with newly learned routines calling up or relying on more elementary routines, like learning to play simple notes before being able to play a piano concerto.[29]

As for process of behavior plan forming, Rosenhaum et al. (2007) proposed that plans are not formed from scratch for each successive movement sequence but instead are formed by making whatever changes are needed to distinguish the movement sequence to be performed next from the movement sequence that has just been performed.[30] There are evidences found that motor planning occurs by changing features of successively needed motor plans.[31] Also, Rosenhaum et al. (2007) found that even single movements appear to be controlled with hierarchically organized plans, with starting and goal postures at the top level and intermediate states comprising the transition from the starting to the goal at the lower level.[30]

Interaction with conceptual knowledge

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The most common understanding in relation to the procedural and conceptual knowledge is of the contrast of knowing how and knowing that.[32] Some see the distinction as a contrast between the tacit knowledge of technology and the explicit knowledge of science.[33] Conceptual knowledge allows us to explain why, hence the distinction of "know how" and "know why".[34] Conceptual knowledge is concerned with relationships among items of knowledge, such that when students can identify these links, it means they have conceptual understanding. Cognitive psychologists also use the term declarative knowledge to contrast it with procedural knowledge, and define it as "knowledge of facts".[35] However, declarative knowledge may be a collection of unrelated facts, whereas conceptual knowledge puts the focus on relationships.[36] Also, declarative knowledge is an inert form of knowledge which contrasted with procedural knowledge as an active form, but conceptual knowledge can be part of an active process. Therefore, it is important to know that conceptual knowledge is not simply factual knowledge but consists of ideas that give some power to thinking about technological activity.

Evidence from mathematics learning research supports the idea that conceptual understanding plays a role in generation and adoption of procedures. Children with greater conceptual understanding tend to have greater procedural skill.[37] Conceptual understanding precedes procedural skill.[38] Instruction about concepts as well as procedures can lead to increased procedural skill.[39] And increasing conceptual knowledge leads to procedure generation.[40][41] However, this relationship is not unidirectional. Conceptual and procedural knowledge develop iteratively, but the conceptual knowledge may have a greater influence on procedural knowledge than the reverse.[41][42] Conceptual instruction led to increased conceptual understanding and to generation and transfer of a correct procedure. Procedural instruction led to increased conceptual understanding and to adoption, but only limited transfer, of the instructed procedure.

Technical uses of the phrase

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Artificial intelligence

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In artificial intelligence, procedural knowledge is a type of knowledge that can be possessed by an intelligent agent. Such knowledge is often represented as a partial or complete finite-state machine or computer program. A well-known example is the procedural reasoning system, which might, in the case of a mobile robot that navigates in a building, contain procedures such as "navigate to a room" or "plan a path". In contrast, an AI system based on declarative knowledge might just contain a map of the building, together with information about the basic actions that can be done by the robot (like moving forward, turning, and stopping), and leave it to a domain-independent planning algorithm to discover how to use those actions to achieve the agent's goals.

Cognitive psychology

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In cognitive psychology, procedural knowledge is the knowledge exercised in the accomplishment of a task, and thus includes knowledge which, unlike declarative knowledge, cannot be easily articulated by the individual, since it is typically subconscious (or tacit). Many times, the individual learns procedural knowledge without being aware that they are learning.[43] For example, most individuals can easily recognize a specific face as attractive or a specific joke as funny, but they cannot explain how exactly they arrived at that conclusion or they cannot provide a working definition of attractiveness or being funny. This example illustrates the difference between procedural knowledge and the ordinary notion of knowing how, a distinction which is acknowledged by many cognitive psychologists.[44]

Ordinarily, we would not say that one who is able to recognize a face as attractive is one who knows how to recognize a face as attractive. One knows how to recognize faces as attractive no more than one knows how to recognize certain arrangements of leptons, quarks, etc. as tables. Recognizing faces as attractive, like recognizing certain arrangements of leptons, quarks, etc. as tables, is simply something that one does, or is able to do. It is, therefore, an instance of procedural knowledge, but it is not an instance of know-how. In many cases, both forms of knowledge are subconscious.

For instance, research by cognitive psychologist Pawel Lewicki has shown that procedural knowledge can be acquired by subconscious processing of information about covariations.[45]

Educational implications

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In the classroom, procedural knowledge is part of the prior knowledge of a student. In the context of formal education procedural knowledge is what is learned about learning strategies. It can be the "tasks specific rules, skills, actions, and sequences of actions employed to reach goals" a student uses in the classroom. As an example for procedural knowledge Cauley refers to how a child learns to count on their hands and/or fingers when first learning math.[46] The Unified Learning Model[47] explicates that procedural knowledge helps make learning more efficient by reducing the cognitive load of the task. In some educational approaches, particularly when working with students with learning disabilities, educators perform a task analysis followed by explicit instruction with the steps needed to accomplish the task.[48]

One advantage of procedural knowledge is that it can involve more senses, such as hands-on experience, practice at solving problems, understanding of the limitations of a specific solution, etc. Thus procedural knowledge can frequently eclipse theory.

One limitation of procedural knowledge is its job-dependent nature. As a result, it tends to be less general than declarative knowledge. For example, a computer expert might have knowledge about a computer algorithm in multiple languages, or in pseudo-code, but a Visual Basic programmer might know only about a specific implementation of that algorithm, written in Visual Basic. Thus the 'hands-on' expertise and experience of the Visual Basic programmer might be of commercial value only to Microsoft job-shops, for example.[citation needed]

Intellectual property law

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In intellectual property law, procedural knowledge is a parcel of closely held information relating to industrial technology, sometimes also referred to as a trade secret which enables its user to derive commercial benefit from it. In some legal systems, such procedural knowledge has been considered the intellectual property of a company, and can be transferred when that company is purchased. It is a component of the intellectual property rights on its own merits in most legislations but most often accompanies the license to the right-of-use of patents or trademarks owned by the party releasing it for circumscribed use. Procedural knowledge is not however solely composed of secret information that is not in the public domain; it is a "bundled" parcel of secret and related non-secret information which would be novel to an expert in the field of its usage.

Industrial know-how

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In the context of industrial property (now generally viewed as intellectual property or IP), know-how is a component in the transfer of technology in national and international environments, co-existing with or separate from other IP rights such as patents, trademarks and copyright and is an economic asset.[49] When it is transferred by itself, know-how should be converted into a trade secret before transfer in a legal agreement.

Know-how can be defined as confidentially held, or better, closely held information in the form of unpatented inventions, formulae, designs, drawings, procedures and methods, together with accumulated skills and experience in the hands of a licensor firm's professional personnel which could assist a transferee/licensee of the object product in its manufacture and use and bring to it a competitive advantage. It can be further supported with privately maintained expert knowledge on the operation, maintenance, use/application of the object product and of its sale, usage or disposition.

The inherent proprietary value of know-how is embedded in the legal protection afforded to trade secrets in general law, particularly, case law.[50] Know-how, in short, is private intellectual property which can be said to be a form of precursor to other intellectual property rights. The trade secret law varies from country to country, unlike the case for patents, trademarks and copyright for which there are formal conventions through which subscribing countries grant the same protection to the property as the others; examples of which are the Paris Convention for the Protection of Industrial Property and the World Intellectual Property Organization (WIPO), under United Nations, a supportive organization designed "to encourage creative activity, [and] to promote the protection of intellectual property throughout the world".

The World Trade Organization defined a trade secret by the following criteria:[51]

Natural and legal persons shall have the possibility of preventing information lawfully within their control from being disclosed to, acquired by, or used by others without their consent in a manner contrary to honest commercial practices (10) so long as such information: (a) is secret in the sense that it is not, as a body or in the precise configuration and assembly of its components, generally known among or readily accessible to persons within the circles that normally deal with the kind of information in question; (b) has commercial value because it is secret; and (c) has been subject to reasonable steps under the circumstances, by the person lawfully in control of the information, to keep it secret.

For purposes of illustration, the following may be a provision in a license agreement serving to define know-how:-

Know-how shall mean technical data, formulas, standards, technical information, specifications, processes, methods, codebooks, raw materials, as well as all information, knowledge, assistance, trade practices and secrets, and improvements thereto, divulged, disclosed, or in any way communicated to the Licensee under this Agreement, unless such information was, at the time of disclosure, or thereafter becomes part of the general knowledge or literature which is generally available for public use from other lawful sources. The burden of proving that any information disclosed hereunder is not confidential information shall rest on the licensee.

Disclosure agreements

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There are two sets of agreements associated with the transfer of know-how agreement: disclosure and non-disclosure agreements, which are not separately parts of the principal know-how agreement.[citation needed]

The initial need for disclosure is due to the requirement of a licensee firm to know what is the specific, unique, or general content of the know-how that a licensor firm possesses that promises value to the licensee on entering into a contract. Disclosure also aids the potential licensee in selecting among competitive offers, if any. Such disclosures are made by licensors only under non-disclosure or confidentiality agreements in which there are express undertakings that should the ultimate license not materialize, the firm to whom the disclosure is made will not reveal, or by any manner apply, any part of the disclosed knowledge which is not in the public domain or previously known to the firm receiving the information.

Non-disclosure agreements are undertaken by those who receive confidential information from the licensee, relating to licensed know-how, so as to perform their tasks. Among them are the personnel of engineering firms who construct the plant for the licensee or those who are key employees of the licensee who have detailed access to disclosed data, etc. to administer their functions in operating the know-how-based plant. These are also in the nature of confidentiality agreements and carry the definition of know-how, in full or truncated part, on a need-to-know basis.

Employee knowledge

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Under English law, employees have duties of good faith and fidelity until their employment ceases, whence only the former still applies.

It is sometimes unclear what forms of "know how" that was divulged to an employee in order to carry out their functions and then becomes their own knowledge rather than a secret of their previous employer. Some employers will specify in their employment contracts that a grace period will apply to know how that starts when a person leaves them as an employee.

Specifying exactly what information this includes would increase the likelihood of it being upheld in court in the event of a breach, i.e. saying "when your employment contract is terminated, you must keep all information about your previous employment with us secret for four years" would be difficult to support because that person has to be able to use the skills and knowledge they learnt to gain employment elsewhere.

See also

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
Procedural knowledge, often termed "knowing how," encompasses the ability to execute actions, perform skills, and apply methods to accomplish tasks or solve problems, typically acquired through practice and represented as sequences of operations or rules in cognitive models. This form of knowledge stands in contrast to declarative knowledge, which involves "knowing that"—the representation of facts, concepts, and propositions that can be explicitly stated and recalled. In cognitive science, procedural knowledge is frequently modeled as production rules—condition-action pairs that guide behavior automatically once compiled from initial declarative encodings. The distinction between procedural and declarative knowledge traces its philosophical roots to Gilbert Ryle's 1949 work , where he argued against an intellect-versus-action dichotomy, emphasizing practical abilities over mere theoretical understanding, though this distinction remains debated in contemporary philosophy between intellectualist and anti-intellectualist views; the modern psychological framework was formalized in John R. Anderson's 1983 ACT* theory of cognition. Anderson proposed that serves as a precursor, which, through repeated use and tuning, transforms into efficient procedural knowledge via mechanisms like compilation, enabling faster and more automatic performance. This transition is central to understanding skill acquisition, as seen in domains like , where procedural fluency (e.g., executing algorithms for ) emerges iteratively alongside conceptual understanding (e.g., grasping why the algorithm works). In educational contexts, procedural knowledge is vital for developing competence, particularly in procedural-heavy subjects like arithmetic or programming, where learners progress from effortful rule application to intuitive execution. highlights bidirectional relations: gains in procedural knowledge can enhance conceptual insights, while strong conceptual foundations facilitate more flexible procedural adaptations, countering rote learning pitfalls. Beyond , procedural knowledge underpins expertise in cognitive architectures and , informing models of human learning and intelligent tutoring systems that simulate production rule acquisition. Examples include motor skills like or , which become proceduralized over time, freeing cognitive resources for higher-level .

Core Concepts

Definition and Characteristics

Procedural knowledge, also known as "knowing how," refers to the ability to perform tasks or execute procedures effectively, typically demonstrated through practical action rather than verbal explanation. This form of knowledge encompasses the skills and routines required to accomplish specific activities, such as coordinating movements or applying step-by-step processes, and is often represented internally as production rules or sequences of actions in cognitive models. The concept traces its philosophical origins to Gilbert Ryle's 1949 work , where he distinguished "knowing how" from "knowing that," arguing that the former involves intelligent performance that cannot be fully reduced to factual propositions. Key characteristics of procedural knowledge include its non-propositional nature, meaning it is not easily articulated in declarative statements; its skill-based orientation, focusing on execution rather than description; and its implicit quality, where individuals may perform tasks competently without conscious awareness of the underlying steps. It is also context-dependent, adapting to situational demands, and generally develops through repeated practice, leading to increased over time. Subtypes of procedural knowledge include motor skills, which involve physical coordination; cognitive routines, such as mental strategies for problem-solving; and algorithms, which are formalized sequences for computational or logical tasks. Everyday examples illustrate its practical, performative essence: riding a bicycle relies on procedural knowledge of balance and pedaling without needing to explain the physics involved; tying shoelaces demonstrates fine motor sequencing acquired through ; driving a car integrates perceptual-motor skills for ; playing a musical instrument requires coordinated finger movements and timing; and following a entails sequential application of instructions in a setting. These instances highlight how procedural knowledge enables fluid, goal-directed behavior in real-world scenarios.

Distinction from Declarative Knowledge

, often termed "knowing that," encompasses factual and propositional information that can be explicitly stated and verbally articulated, such as knowing that is the capital of . This type of knowledge is stored in a conscious, retrievable form, allowing individuals to describe facts, definitions, or truths without necessarily demonstrating them through action. In contrast, procedural knowledge, or "knowing how," involves implicit skills and action-oriented abilities, such as riding a , which are manifested through performance rather than verbal explanation. While is explicit and verbalizable, procedural knowledge is typically unconscious and resistant to forgetting once automatized, enabling fluid execution even if the underlying rules cannot be easily articulated. can be recalled but may not translate directly to application without additional procedural components, highlighting the boundary between mere awareness of facts and the capacity to act on them. Philosopher , in his seminal work , critiqued intellectualist theories for erroneously reducing "knowing how" to a mere form of "knowing that," arguing that procedural knowledge is a primitive category irreducible to declarative propositions. Ryle contended that treating skills as disguised factual knowledge leads to a , as abilities like intelligent action cannot be fully captured by verbal descriptions alone. This distinction carries significant implications for and the , challenging reductionist views that prioritize declarative forms as the foundation of all . For instance, Noam Chomsky's critique of in underscores how innate procedural mechanisms enable creative language use beyond of factual associations, resisting explanations that conflate skills with accumulated .

Acquisition and Development

In Human Ontogeny

Procedural knowledge begins to emerge in human infants during the sensorimotor stage of , as described by , where basic skills are acquired through sensory experiences and motor actions rather than verbal instruction. Around 3 to 6 months of age, infants develop initial procedural abilities, such as coordinating hand-eye movements to grasp objects, marking the transition from reflexive to intentional actions. This grasping skill, initially a palmar reflex that evolves into voluntary reaching by 4 months, exemplifies early formation, enabling infants to manipulate their environment without conscious awareness of the underlying steps. Empirical studies from the 1990s, including longitudinal observations of sensorimotor coordination, confirm that these foundational procedures solidify through repeated trial-and-error interactions, laying the groundwork for more complex skills. In childhood and , procedural knowledge refines through everyday activities like play and formal schooling, transitioning from to integrated cognitive procedures. Motor milestones, such as walking, typically begin around 12 months and become automatized by age 2, allowing children to navigate varied terrains with increasing efficiency, as evidenced by Karen Adolph's longitudinal research on locomotion from the late 1980s to 2000s. Cognitively, basic arithmetic routines—such as counting on fingers or applying algorithms—emerge between ages 5 and 7, becoming fluent through practice and supporting higher mathematical reasoning. These developments highlight a shift toward procedural , where skills like or simple problem-solving sequences are executed with minimal cognitive effort by , driven by maturational changes and environmental exposure. During adulthood, procedural knowledge remains relatively stable throughout adulthood, with skills like or professional routines performed fluidly due to consolidated traces. In aging, while complex procedures may decline due to neurodegenerative processes, simpler habits often remain intact; for instance, studies on patients show preserved retention of overlearned motor sequences, such as habitual walking patterns, despite impairments in new learning. This relative preservation of contrasts with steeper declines in declarative recall, underscoring its robustness across the lifespan. Cross-cultural variations influence procedural milestones, with universal patterns like grasping and walking emerging similarly worldwide, yet environmental factors accelerate certain skills in specific contexts. In societies, children exhibit tool-use proficiency—such as wielding sticks for play or foraging—due to greater opportunities for independent exploration. Longitudinal research from the 2000s, including Adolph's comparative studies, reveals that childrearing practices, like or floor time, can shift motor development timelines by months without altering core procedural foundations.

Through Practice and Instruction

Procedural knowledge is often acquired through deliberate practice, which involves structured, goal-oriented repetition combined with immediate feedback to refine skills toward expertise. According to et al. (1993), deliberate practice differs from mere repetition by focusing on specific aspects of performance that require improvement, often under the guidance of a teacher or coach, leading to superior skill development in domains such as and chess. For instance, in their study of violinists, elite performers had accumulated approximately of deliberate practice by age 20, far exceeding that of less accomplished peers, illustrating how sustained, feedback-driven repetition builds procedural proficiency. Instructional strategies further facilitate procedural knowledge acquisition by providing structured support that aligns with learners' capabilities. Vygotsky's concept of the (ZPD) posits that learners can master complex procedures with guidance from more knowledgeable others, gradually internalizing skills through techniques such as modeling demonstrations and prompting. This approach is evident in educational settings where tasks are broken into subtasks—for example, teaching surgical procedures by first demonstrating each step, then allowing supervised practice—enabling progressive buildup of procedural competence. Complementing this, Bandura's emphasizes observational modeling, where individuals acquire procedures by imitating observed behaviors, as demonstrated in his experiments showing that children learned aggressive actions through watching adult models. Various learning types contribute to procedural development, including trial-and-error, which allows refinement through iterative attempts and error correction, and simulation-based that replicates real-world scenarios safely. In , flight simulators enable pilots to practice procedures repeatedly without risk, improving response accuracy and under pressure. However, barriers such as high can hinder progress; Sweller's cognitive load theory highlights how excessive demands on during complex procedural learning impede acquisition, recommending instructional designs that minimize extraneous load to facilitate transfer to novel contexts. serves as a key facilitator, enhancing persistence in practice, while poor transfer often arises from context-specific without generalization strategies. In modern vocational programs, techniques like —reviewing procedures at increasing intervals—have proven effective for enhancement. Studies from the 2010s, such as those applying in , report improvements in retention and performance compared to massed practice, particularly in fields like healthcare and technical trades. More recent studies from the , including applications in preparation and , continue to demonstrate the effectiveness of for long-term retention of procedural skills. This method counters forgetting curves by reinforcing over time, making it a of efficient protocols.

Neural and Cognitive Mechanisms

Brain Structures and Processes

Procedural knowledge relies on a network of subcortical brain structures, with the playing a central role in habit formation and action sequencing. The facilitate the gradual acquisition and execution of skills through parallel loops involving the cortex, , and , enabling the consolidation of repetitive behaviors into automatic routines. The contributes to procedural learning by supporting and error correction, particularly in fine-tuning timing and precision during skill acquisition. Within the , the is key for reward-based procedural learning, integrating sensory inputs with motivational signals to reinforce adaptive sequences. Neuroimaging studies provide robust evidence for these structures' involvement. (fMRI) research from the onward demonstrates that as procedural tasks automatize with practice, activation decreases in prefrontal regions while subcortical areas like the and show sustained or enhanced engagement, reflecting a shift from effortful control to implicit execution. Classic cases of , such as patient H.M., who suffered bilateral hippocampal damage, reveal preserved despite profound declarative deficits; H.M. improved on mirror-tracing tasks over sessions without recalling prior practice, underscoring the independence of procedural systems from hippocampus-dependent fact storage. Positron emission tomography (PET) scans in similar amnesic patients confirm intact and cerebellar activity during skill learning, further dissociating procedural from declarative pathways. Dopamine modulates these processes by reinforcing procedural habits via midbrain projections to the striatum, where phasic signals encode prediction errors to strengthen rewarded action sequences. Synaptic plasticity in basal ganglia circuits, particularly long-term potentiation (LTP) at corticostriatal synapses, underlies the enduring changes that support habit formation, with repeated stimulation enhancing synaptic efficacy in medium spiny neurons. Pathological conditions highlight these mechanisms' specificity. In , degeneration of the , especially the , leads to profound procedural deficits, such as impaired and motor habit formation, even in early stages when declarative memory remains relatively spared. Autism spectrum disorder often involves atypical procedural learning, with showing altered basal ganglia-cerebellar connectivity that disrupts generalization of skills and adaptation to novel contexts. Evolutionarily, procedural knowledge mechanisms are highly conserved across mammals, evident in maze navigation tasks where and cerebellar circuits enable implicit route learning akin to tool use, suggesting deep phylogenetic roots for habit-based .

Activation and Automaticity

Procedural knowledge is typically activated through contextual cues and environmental stimuli that trigger well-learned sequences of actions with minimal conscious effort. For instance, an experienced automatically shifts gears in response to changes in road speed and engine sound, relying on situational prompts rather than deliberate planning. This activation occurs via consistent stimulus-response mappings stored in , where relevant nodes are rapidly engaged without taxing capacity. The development of automaticity in procedural knowledge progresses through stages from controlled processing, which is effortful and serially executed, to autonomous processing, which becomes habitual and parallel. According to Schneider and Shiffrin's model, controlled processing demands attention and is capacity-limited, while automatic processing emerges after extensive consistent , allowing involuntary activation of response sequences. This transition is often measured using dual-task paradigms, where automaticity is indicated by minimal performance decrement on the primary procedural task during concurrent cognitive demands, reflecting reduced interference as skills become effortless. Automaticity confers benefits such as enhanced efficiency in multitasking and reduced , enabling individuals to allocate attention to higher-level goals while executing routine procedures. However, it also poses risks, including resistance to modification and the persistence of maladaptive habits, such as ingrained poor posture during prolonged sitting, which can lead to errors or inflexibility in changing environments. techniques like reaction time studies and error rate analyses further quantify this, as seen in research on expert typists where skilled demonstrates substantial automatic keypress , minimizing conscious intervention. Factors influencing the activation and maintenance of procedural knowledge include sleep consolidation, which strengthens traces through targeted reactivation during non-REM sleep, improving subsequent recall and execution. Acute stress, in contrast, generally exerts limited negative effects on procedural recall compared to declarative , though high levels may disrupt performance under novel conditions. These processes are enabled by brain regions such as the , which support the shift to habitual responding.

Interactions with Other Knowledge Forms

Integration with Declarative Knowledge

Procedural knowledge integrates with through complementary cognitive systems, where factual information stored in declarative memory guides the selection, initiation, and refinement of action sequences managed by . This synergy is central to Michael Ullman's declarative/procedural (DP) model, which posits that declarative memory, reliant on structures, provides the contextual facts necessary to activate and modulate procedural representations in frontal-subcortical circuits. For instance, in , declarative knowledge of traffic rules—such as yield signs or speed limits—initializes the appropriate procedural routines for maneuvering a , ensuring safe and contextually appropriate execution. Real-world examples illustrate this integration across domains. In language processing, declarative memory encodes vocabulary and exceptions (e.g., irregular verbs like "go-went"), while procedural memory applies grammatical rules to generate fluent speech or comprehension, allowing seamless combination for effective communication. Similarly, in mathematical problem-solving, declarative recall of formulas (e.g., the ) directs the procedural steps of algebraic manipulation, enabling efficient computation beyond rote application. These interactions highlight how declarative inputs initialize procedural chains, creating hybrid cognitive processes that enhance performance in skilled tasks. Feedback loops further strengthen this integration, as outcomes from procedural execution update declarative stores, refining future interactions. For example, in learning a like , initial declarative understanding of rules (e.g., fouls or positioning) informs procedural practice of techniques such as or ; repeated play then generates feedback that bolsters both rule comprehension and automatization. Empirical evidence from acquisition studies supports these hybrid traces, with 2010s fMRI research on mathematical learning revealing overlapping activations in declarative (hippocampal) and procedural () regions during transition from novice to performance. Behavioral experiments on perceptual categorization also demonstrate mutual influences, where declarative strategies can enhance procedural learning without full . Despite these synergies, limitations arise from potential conflicts between systems, particularly in novel situations where may override entrenched procedural habits to enable adaptation. In emergencies, such as an unexpected road hazard during , declarative awareness of safety protocols (e.g., evasive actions) can interrupt automatic procedural steering, preventing maladaptive responses but risking momentary inefficiency. This override mechanism, while adaptive, underscores the DP model's observation that procedural rigidity can hinder flexibility without declarative intervention.

Role in Cognitive Models

Procedural knowledge plays a central role in dual-process theories of , which posit two distinct systems for information processing. is characterized as fast, intuitive, and automatic, relying heavily on procedural knowledge to enable quick, effortless responses based on learned routines, while System 2 involves slower, deliberate, and reflective thinking that draws more on for analysis and reasoning. This framework, as articulated by , highlights how procedural knowledge dominates in situations requiring expertise, where skilled individuals bypass reflective deliberation in favor of habitual, procedural actions to achieve efficiency. For instance, expert chess players or musicians exhibit System 1 dominance through procedural mastery, allowing superior performance without conscious effort. In connectionist models, procedural knowledge is represented as distributed patterns of activation across neural networks, simulating how skills emerge from interconnected nodes rather than explicit rules. The cognitive architecture, developed by John R. Anderson starting in 1983, exemplifies this by modeling procedural knowledge through production rules that compile over time, transforming declarative facts into efficient, chunked procedures for tasks like problem-solving. This approach underscores procedural compilation, where repeated practice refines knowledge into , mirroring human learning in computational simulations. Embodied cognition theories further integrate procedural knowledge by emphasizing its grounding in sensorimotor experiences, which shape abstract thought through metaphorical mappings. George Lakoff's conceptual metaphor theory illustrates how procedural actions, such as grasping or navigating, provide the experiential basis for understanding abstract concepts like comprehension ("grasping an idea") or progress ("moving forward"), linking bodily procedures to higher . This perspective argues that procedural knowledge is not isolated but embedded in physical interactions, facilitating the extension of concrete skills to symbolic reasoning. Critiques of procedural knowledge's role in cognitive models often revolve around debates between modularity and integration. Jerry Fodor's modularity hypothesis suggests cognitive processes, including procedural ones, operate in domain-specific modules isolated from central reasoning, contrasting with integrationist views that emphasize seamless interplay across knowledge types. Recent evolutions in the 2020s incorporate into procedural adaptation, modeling how procedural routines update probabilistically based on environmental cues, enhancing flexibility in dynamic contexts like habit formation. Applications of these models extend to simulating real-world phenomena, such as habits in , where procedural knowledge reinforces compulsive behaviors through automated loops in connectionist frameworks, or under , where dual-process interactions predict biases in . These simulations demonstrate procedural knowledge's explanatory power in pathological and adaptive , informing interventions that target .

Applications Across Disciplines

In Artificial Intelligence

In artificial intelligence, procedural knowledge is represented through structured mechanisms that encode sequences of actions or rules for task execution. Early approaches in systems utilized production rules, which are conditional statements of the form "if-then" to capture decision-making processes. For instance, the system, developed in the 1970s at , employed over 500 production rules to diagnose bacterial infections and recommend antibiotic therapies, enabling inference to simulate procedural reasoning. Similarly, scripts and schemas in knowledge bases provide templated representations of stereotyped event sequences, facilitating the modeling of dynamic interactions in domains like natural language understanding or . These structures allow AI systems to anticipate and execute procedural flows, such as scripts in conversational agents. In , procedural knowledge manifests implicitly through learned policies and generative algorithms. Procedural content generation in video games, exemplified by released in 2016, relies on deterministic algorithms to create vast, explorable universes, including planets, flora, and fauna, by applying noise functions and parameter mappings to produce varied yet coherent procedural outcomes. In , policies derived from value and policy networks encode procedural strategies as sequences of actions optimized for rewards; , developed by DeepMind in 2016, learned such policies through , generating move sequences that defeated human champions by implicitly representing Go-playing procedures without explicit rule encoding beyond game basics. A key challenge in AI is encoding tacit procedural knowledge, which involves intuitive, context-dependent skills difficult to formalize explicitly, leading to gaps in systems reliant on symbolic or data-driven representations. Hybrid architectures address this by integrating procedural and ; the SOAR cognitive architecture, originating from in the 1980s and continually refined, uses production rules for procedural execution alongside declarative chunks for factual recall, enabling chunking mechanisms to learn new procedures from problem-solving experience. Recent advances as of 2025 leverage large language models (LLMs) to generate procedural content via chain-of-thought (CoT) prompting, where models decompose complex tasks into intermediate reasoning steps, effectively simulating procedural sequences for planning and problem-solving. The original CoT method, introduced in 2022, significantly improved LLM performance on arithmetic and benchmarks by eliciting step-by-step procedures. In 2025, researchers introduced new frameworks to enable cheaper, more resilient AI agents capable of retaining and applying learned procedures across tasks. In , imitation learning has advanced procedural acquisition through demonstration, as seen in DARPA-sponsored challenges like the Robotics Challenge (2012–2015), which spurred developments in learning manipulation sequences from human , evolving into modern behavioral cloning techniques for tasks in unstructured environments. Despite these progresses, AI representations of procedural knowledge exhibit brittleness in novel environments, where trained policies fail to generalize due to to specific training distributions, as observed in agents collapsing under distributional shifts. Ethical concerns also arise in autonomous procedural decisions, particularly in self-driving cars, where algorithms must resolve dilemmas like prioritizing passengers versus pedestrians, raising issues of and fairness in real-time action selection. Frameworks for ethical AI in vehicles emphasize principles such as harm minimization and transparency to mitigate biases in procedural rule-setting.

In Education and Psychology

In educational frameworks, procedural knowledge is prominently featured in Bloom's revised , where it forms one of the four dimensions of knowledge alongside factual, conceptual, and metacognitive types. This dimension encompasses skills, algorithms, techniques, and methods for performing tasks within a discipline, aligning particularly with the cognitive process levels of "" (executing procedures) and "analyze" (breaking down processes). The original , developed in 1956, emphasized cognitive objectives but laid the groundwork for integrating procedural elements, while the 2001 revision by Anderson and Krathwohl explicitly incorporated them to guide instruction. These classifications have implications for design, promoting hands-on activities such as simulations and problem-solving exercises to foster procedural competence rather than rote . Psychological research on expertise highlights how procedural knowledge manifests differently between novices and experts, often through chunked representations in . In a seminal study, Chi et al. (1981) examined physics problem-solving and found that experts categorize problems based on underlying principles and procedures, forming larger, integrated chunks of knowledge, whereas novices rely on surface features and smaller, fragmented units. This difference underscores procedural knowledge's role in efficient recall and application, with experts demonstrating automated procedural sequences that novices lack, enabling superior performance in domain-specific tasks. Such findings from expertise research inform psychological models of skill acquisition, emphasizing deliberate practice to build these procedural structures over time. Assessment methods for procedural knowledge prioritize performance-based evaluations to capture skill execution, contrasting with multiple-choice formats suited to . Portfolios, simulations, and direct observations allow learners to demonstrate applied procedures in context, providing insights into proficiency and transferability that static tests cannot. For instance, in or , scenario-based assessments evaluate procedural steps like surgical techniques, revealing gaps in hands-on application. Multiple-choice questions, while effective for assessing factual recall, often fall short for procedural unless designed to simulate sequences. In psychological interventions, procedural knowledge is facilitated through structured routines, as seen in (CBT) for anxiety disorders. CBT incorporates procedural elements such as exposure hierarchies and behavioral experiments, teaching patients step-by-step techniques to confront fears and reframe responses, thereby building habitual skills for symptom management. These routines enhance procedural automaticity, reducing reliance on anxious declarative thoughts over time. Regarding neurodiversity, research identifies procedural strengths in , particularly in visuo-spatial processing, where individuals often excel at mentally rotating objects and navigating three-dimensional spaces—skills underrepresented in traditional assessments focused on verbal tasks. This highlights gaps in coverage, as dyslexia's procedural advantages in holistic can inform tailored therapeutic and educational supports. Current trends in the emphasize immersive technologies like (VR) for training procedural skills in fields such as . VR simulations enable repeated, risk-free practice of procedures like chest tube insertion, improving technical proficiency and confidence among learners compared to traditional methods. Studies show VR enhances procedural retention and transfer to real scenarios, with randomized trials demonstrating superior skill acquisition in procedural tasks. However, equity issues persist, as access to such practice opportunities remains uneven, disproportionately affecting low-income and underrepresented students who lack resources for hands-on or technology-enhanced learning. Addressing these disparities requires inclusive policies to ensure broad participation in procedural development activities.

In Law and Industry

In law, often protect procedural knowledge, encompassing methods, techniques, and processes that derive economic value from secrecy, as defined under the (UTSA) of 1985, which includes formulas, programs, devices, and processes not generally known or readily ascertainable. For instance, the Company's formula is safeguarded as a , emphasizing the procedural aspects of its production and mixing techniques rather than a mere list of ingredients, allowing indefinite protection without public disclosure. In contrast, protect declarative knowledge, such as detailed inventions or compositions, requiring full public revelation in exchange for a limited-term monopoly, whereas suit ongoing procedural know-how that is difficult to reverse-engineer, like protocols. This distinction enables firms to strategically choose protection for tacit procedural elements that could be compromised by disclosure requirements. In industrial settings, procedural knowledge manifests in manufacturing procedures, such as lean production techniques that optimize workflows through iterative skill-based adjustments rather than codified instructions. Toyota's Production System exemplifies this through tacit skill transfer, where experienced workers impart procedural expertise via on-the-job mentoring and problem-solving routines, fostering continuous improvement without formal documentation. in such firms relies on these transfers to maintain competitive edges, as procedural elements like just-in-time assembly are embedded in employee practices and protected as trade secrets to prevent replication by competitors. Disclosure of procedural knowledge in business transactions is governed by non-disclosure agreements (NDAs), which are standard in to shield confidential processes, such as proprietary operational methods shared during . These agreements outline handling procedures, duration of secrecy, and remedies for breaches, ensuring procedural information remains protected post-transaction. Landmark , including Kewanee Oil Co. v. Bicron Corp. (1974), affirmed that state trade secret protections against misappropriation—such as unauthorized use of confidential processes—do not conflict with federal law, allowing remedies like injunctions and damages for breaches involving procedural know-how obtained through relationships or improper means. In employee contexts, non-compete clauses restrict skilled workers from disclosing or utilizing procedural knowledge gained through training when joining competitors, addressing risks in industries where tacit skills form core value. In 2024, the U.S. Federal Trade Commission (FTC) attempted to implement a nationwide ban on non-compete agreements, but the rule was blocked by federal courts, and the FTC abandoned its appeal in September 2025, leaving enforceability to state laws. Such clauses remain enforceable under state regulations if reasonable in scope, duration, and geography, particularly for roles involving specialized processes, as they mitigate the transfer of proprietary methods that could harm former employers. Training investments in procedural skills, however, face challenges from knowledge flight, where employee turnover in tech industries leads to significant losses; studies indicate that 94% of employees would stay longer at a company that invests in their career development, underscoring the need for retention strategies to preserve procedural expertise amid high mobility. Contemporary applications highlight procedural knowledge in the , where workers like drivers accumulate tacit route optimization techniques—such as timing pickups based on traffic patterns and surge pricing—through , often unprotected by traditional IP but vulnerable to platform algorithms that standardize practices. In , protections increasingly emphasize trade secrets for procedural elements like cell culturing methods or purification processes, which complement patents by shielding iterative improvements that maintain secrecy in competitive R&D environments, as seen in pharmaceutical firms defending against in licensing disputes.

References

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