KNOWLEDGE OF RESULTS (KR KOR)

Knowledge of Results (KR) Definition and Scope

Knowledge of Results (KR) is fundamentally defined as the type of augmented, post-response feedback provided to an individual regarding the outcome or success of their completed movement or behavioral attempt relative to an established goal. This extrinsic feedback mechanism informs the learner whether they achieved the desired result, failed to meet the criteria, or how close they came to the target objective. Unlike intrinsic feedback, which arises naturally from sensory systems during the performance of the task itself, KR is typically delivered by an external source, such as a coach, teacher, or measuring instrument, and is specifically focused on the success or failure relative to the environmental goal. The primary function of Knowledge of Results is to serve as a crucial error detection mechanism, allowing the learner to compare their actual performance consequence with the intended outcome, thereby facilitating the necessary behavioral adjustments for future attempts. This concept is central to virtually all theories of motor learning and skill acquisition, providing the critical informational link required for systematic behavioral modification and performance improvement across diverse educational, psychological, and clinical settings.

The scope of KR extends beyond simple pass/fail notifications; it encompasses any precise information relayed to the learner about the external, measurable goal of the task. For instance, in an industrial quality control scenario, KR would be the precise percentage of acceptable parts produced, quantifying the output success (or failure) of a specific operational procedure. This definition rigorously emphasizes that KR is focused strictly on the external, observable, and measurable consequence of the action, distinctly separate from the quality of the movement patterns used to achieve that consequence. The provision of timely and accurate KR is universally recognized as a prerequisite for effective learning, particularly during the early stages of skill development, as it helps solidify the cognitive connection between the executed action and its subsequent effect on the environment. Without this critical, objective feedback loop, learners lack the necessary data to systematically correct errors, often leading to performance stagnation or, critically, the unintentional reinforcement of incorrect and inefficient behaviors.

The importance of KR is rooted in its recognized dual function: informational and motivational. Informational feedback provides the essential cognitive data necessary for error correction, strategy refinement, and the updating of the learner’s internal model of the task demands and dynamics. When KR indicates success, the internal strategy is reinforced; when it indicates failure, a cognitive restructuring process is initiated. Conversely, the motivational component significantly influences the learner’s persistence, effort expenditure, and overall engagement. Receiving positive KR can substantially boost confidence and reinforce the desire to continue practicing difficult tasks, while negative KR, when delivered constructively and diagnostically, highlights precise areas needing improvement, thereby focusing the learner’s attention and effort efficiently. Effective implementation of Knowledge of Results must therefore strike a careful balance between these two functions, ensuring that the objective information provided is precise enough to guide technical and strategic changes while simultaneously maintaining the learner’s self-efficacy and sustained engagement throughout the often lengthy process of skill acquisition.

Historical Foundations: The Role of B.F. Skinner and Operant Conditioning

The conceptual genesis of modern Knowledge of Results theory is deeply embedded in the empirical behavioral psychology work conducted by American psychologist B.F. Skinner, whose research began robustly in the 1930s. Skinner’s groundbreaking theory of operant conditioning established the foundational principle that behavior is largely determined and controlled by its immediate consequences. He proposed that feedback, specifically termed ‘reinforcement’ or ‘punishment,’ could be systematically used to modify and shape complex behavior patterns. Although Skinner predominantly utilized terms related to consequence utility, these consequences functionally served the role that KR later occupied in learning research—informing the organism about the efficacy of its response relative to an environmental contingency. In Skinner’s rigorous framework, a consequence that increases the future likelihood of a behavior recurring is termed positive reinforcement, which aligns conceptually with positive KR, whereas a consequence designed to decrease the frequency of a behavior acts as punishment or negative reinforcement, which functionally serves as a form of negative KR.

A crucial methodology developed and extensively utilized by Skinner that directly illustrates the systematic application of KR is shaping, sometimes referred to as the method of successive approximations. Shaping is a procedural technique where rewards (positive reinforcement/KR) are provided only for behavioral responses that represent closer and closer approximations of a desired, ultimate behavior. In this iterative process, the learner receives continuous, precise feedback—the presence or absence of the reward—immediately following each minute modification of their action that moves them closer to the target behavior. This sequential and highly contingent delivery of feedback ensures that complex, non-spontaneous behaviors, which might be impossible to learn in a single step, are methodically broken down into manageable and achievable components. For example, when training specialized animals for specific tasks or teaching children complex language and motor skills, shaping utilizes rapid, outcome-focused KR to incrementally guide the subject’s responses toward the final goal.

Skinner’s revolutionary emphasis on the systematic manipulation of observable consequences provided the foundational paradigm for understanding how extrinsic feedback powerfully influences learning trajectories. His extensive body of work definitively established that Knowledge of Results is not merely supplementary information but is an indispensable and powerful environmental driver of sustained behavioral change. The overall efficiency and effectiveness of operant conditioning techniques are entirely dependent upon the accurate, timely, and contingent presentation of the consequence—the KR—following the specific response being targeted. This early historical context profoundly underscores the view that KR functions as a potent environmental variable that controls both response strength and directional learning. Modern applications of KR in diverse areas such as programmatic self-instruction, therapeutic biofeedback interventions, and expert performance training owe a profound conceptual and methodological debt to Skinner’s demonstration that targeted outcome feedback can reliably produce predictable and lasting behavioral modification.

KR in Cognitive Learning Theory: Gagne’s Events of Instruction

While the initial behavioral tradition focused almost exclusively on the reinforcing properties of consequences, the later cognitive perspective, particularly as articulated by influential educational psychologist Robert Gagné in the 1950s and 60s, seamlessly integrated Knowledge of Results into a broader, highly structured model of instructional design. Gagné’s seminal framework, known as the “Events of Instruction,” proposes a systematic sequence of nine external instructional steps necessary to facilitate optimal internal cognitive learning processes. Within this detailed, hierarchical sequence, the provision of feedback, which primarily constitutes Knowledge of Results, is identified as the eighth essential event: “Providing Feedback.” Gagné contended that after the learner has performed the task or attempted to execute the learned skill (Event 7), they must receive immediate and specific information about the correctness or completeness of their response to successfully close the cognitive feedback loop and transition the learning to long-term memory.

According to Gagné’s comprehensive model, KR plays an absolutely vital role in helping learners confirm or reject their internal hypotheses about the task requirements and solidify the cognitive connections between the initial stimulus, the executed response, and the resulting outcome. If the learner executes an action and receives positive KR, they cognitively register that their internal instructions, strategy, or memory recall were effective, thereby strengthening the memory trace and reinforcing the underlying principle. Conversely, if negative KR is received, the learner is immediately prompted to engage in rigorous error detection and correction processes, leading to the necessary refinement of their cognitive strategy before the next attempt. This cognitive emphasis fundamentally highlights that KR is not solely a motivator but is a crucial piece of intellectual information required for the precise refinement of complex skills and the stable transition of learning from transient short-term working memory into reliable, long-term memory structures.

Gagné strongly stressed that the overall effectiveness of KR is heavily contingent upon its strategic integration within the holistic instructional sequence. For instance, KR is maximally potent and informative when it is strategically preceded by the learner recalling and applying the rules or principles (Event 7: Eliciting Performance) and is subsequently followed by methods designed to promote long-term retention and generalization (Event 9: Enhancing Retention and Transfer). This structural and temporal integration ensures that learners fully understand the context and implications of the feedback and are cognitively prepared to apply the resulting adjustments to future, related tasks. Therefore, in the cognitive view, Knowledge of Results operates as a critical, informational bridge that transforms raw, experiential attempts into meaningful, actionable knowledge, guaranteeing that the consequences of an action are used constructively to adapt and optimize internal cognitive frameworks and schemata.

Distinction Between Knowledge of Results (KR) and Knowledge of Performance (KP)

In the specialized field of motor learning and control, particularly within experimental psychology, a crucial and rigorous theoretical distinction is consistently maintained between Knowledge of Results (KR) and Knowledge of Performance (KP). While both categories represent forms of augmented, extrinsic feedback, they differ fundamentally and functionally in the precise type of information they convey. KR, as established, provides information strictly about the outcome of the action relative to the overarching environmental goal—the success or failure criteria measured against the target. For example, if a trainee missile operator is attempting to hit a target zone, KR would be the actual measured distance the projectile landed from the center point (e.g., “The missile missed the target by 50 meters to the north”). This outcome-focused feedback is mandatory for confirming the overall success or quantifiable failure of the attempt relative to the mission goal.

In stark contrast, Knowledge of Performance (KP), often technically referred to as kinematic or biofeedback, provides detailed information about the quality, execution, or mechanical aspects of the movement pattern itself, entirely independent of the external outcome measure. Continuing with the missile operation example, KP would be precise feedback regarding the specific movement characteristics executed by the operator, such as “Your input lever speed was 20% too fast during the final 3 seconds of tracking,” or “You exerted excessive rotational force on the joystick.” KP is absolutely essential for diagnosing and correcting the underlying movement errors or inefficient techniques that contributed to the eventual suboptimal outcome. Crucially, a learner might sometimes receive positive KR (they achieved the final goal) while their movement pattern remains inefficient or risky, requiring subsequent KP for true long-term optimization and injury prevention.

The strategic and differential use of both KR and KP is vital for comprehensive and stable skill acquisition. Early in the learning process, when the primary objective is simply to achieve a functional outcome, KR is often prioritized to establish the basic relationship between action and result, building confidence. However, as the learner progresses into the refinement stage, seeking efficiency, adaptability, and consistency, Knowledge of Performance becomes increasingly vital for fine-tuning specific muscle timing, optimizing kinematic efficiency, and ensuring movement consistency. Researchers consistently find that while KR is often mandatory for learning tasks where the outcome is not intrinsically perceptible, KP typically accelerates learning significantly when complex coordination patterns or highly technical movements are involved. Effective instructors and training systems must therefore skillfully manage the relative presentation frequency and precision of both types of feedback, ensuring the learner receives the right kind of information at the appropriate stage of the learning curve, critically preventing over-reliance on a single dimension of performance evaluation.

Characteristics of Effective Knowledge of Results

The measurable efficacy of Knowledge of Results is not merely inherent in its existence but resides fundamentally in the specific manner, structure, and form in which it is strategically delivered to the learner. Effective KR must possess several key, research-supported characteristics, including appropriateness of form, precise valence (positive or negative), and systematic consistency. KR can manifest in numerous forms, ranging from simple, immediate nonverbal cues, such as a coach’s nod or a timer stopping, to highly complex quantitative data displayed on sophisticated digital interfaces. Verbal KR is the most common form, involving specific statements detailing the outcome, while nonverbal KR, such as auditory signals, visual charts, or numerical readouts, can be equally powerful, especially within fast-paced or continuous motor tasks. Crucially, the chosen form must be instantly and easily understood by the learner and compatible with the task demands, ensuring that the learner’s limited cognitive resources are not unnecessarily overburdened by the complex task of interpreting the feedback itself.

The valence of KR—whether the information indicates success (positive) or error (negative)—significantly influences both future performance strategies and motivational drive. Positive KR empirically confirms the success of the attempt and serves as a powerful reinforcer, increasing the likelihood that the successful behavioral strategy will be reliably repeated. It plays a key role in building high self-efficacy and encouraging persistent effort, especially during periods of difficulty. Conversely, Negative KR precisely informs the learner of failure, error magnitude, or outcome discrepancy, unequivocally signaling that fundamental behavioral modification is necessary. While frequent negative feedback can be acutely demotivating if delivered poorly or inconsistently, it remains absolutely essential for driving the critical error detection and correction processes. High-quality negative KR must be delivered constructively and diagnostically, highlighting only the quantifiable discrepancy between the achieved result and the precisely desired goal without resorting to personal critique, thereby maintaining the strict focus solely on the objective outcome variable.

Furthermore, effective KR delivery must be meticulously tailored to both the specific individual receiving the feedback and the immediate learning context. What constitutes appropriate feedback for a novice attempting to master a simple, discrete task (e.g., hitting a specific key on a keyboard) may be entirely inappropriate and detrimental for an expert engaged in a continuous, high-stakes task (e.g., performing microsurgery). Experts generally benefit most from highly precise, quantitative KR that enables the execution of minute, detailed adjustments, whereas novices typically require simpler, qualitative KR focused primarily on major outcome errors and reinforcing successful elements. Across all levels, the overarching goal is always to maximize the informational value and actionable content of the feedback while simultaneously minimizing the probability of fostering external dependence, a debilitating phenomenon where the learner relies excessively on the external source of feedback rather than developing and trusting their own vital intrinsic error detection capabilities.

Temporal Considerations: The Importance of Timing and Frequency

The time interval that elapses between the completion of a response and the subsequent delivery of Knowledge of Results—technically known as the KR delay interval—is a highly critical determinant of measurable learning effectiveness and long-term retention. Traditional behavioral theories often strongly emphasized the necessity of immediate feedback for rapid conditioning, suggesting that KR should be provided almost instantaneously after the response to strengthen the stimulus-response association maximally. While this immediacy is demonstrably beneficial for rote learning tasks or during the initial, exploratory stages of complex skill acquisition, contemporary research in motor learning has compellingly demonstrated that a brief, structured delay in KR delivery is often not detrimental to learning, and in some strategic instances, may even be beneficial, provided the learner utilizes the delay interval to cognitively estimate and process the outcome of their attempt.

The frequency and systematic scheduling of KR are equally paramount design considerations in instructional strategy. Continuous KR, where feedback is provided after every single attempt (a 100% frequency schedule), is undeniably highly effective for rapidly improving performance metrics during the initial acquisition phase. However, this high frequency KR often inevitably leads to a pronounced dependency effect, where the learner becomes excessively reliant on the external information source and consequently fails to develop the internal, self-correcting mechanisms necessary for stable, sustained performance once the external KR is ultimately withdrawn. To effectively counteract this dependency and facilitate superior retention and transfer of the skill, researchers and practitioners strongly advocate for reduced frequency KR schedules, often systematically implemented through advanced techniques such as bandwidth feedback, faded feedback, or summary feedback protocols.

Summary KR involves strategically withholding feedback until a predetermined block of trials (e.g., 10 attempts) has been successfully completed, after which aggregated KR concerning the outcomes of those trials is provided. This strategic delay actively encourages the learner to estimate and analyze their own errors during the inter-trial intervals, thereby vigorously promoting the necessary development of intrinsic feedback systems and highly accurate error detection capabilities. Similarly, bandwidth KR is provided only when the learner’s performance outcome falls demonstrably outside a predetermined, acceptable margin of error around the target goal. By systematically reducing the frequency of feedback, these methods strategically force the learner to engage in deeper, more effortful cognitive processing and active self-correction, which ultimately leads to superior long-term retention and stability of the acquired skill, definitively demonstrating that less frequent, but strategically timed and accurate, KR is often superior for promoting true, durable learning over mere short-term performance gains.

Specificity, Precision, and Relevance in KR Delivery

For Knowledge of Results to achieve its maximal potential effectiveness, it must be characterized by high levels of specificity, quantifiable precision, and direct relevance to the immediate, current learning goal. Vague or generalized feedback, such as “That was much better,” offers minimal, if any, actionable informational value for systematic error correction and strategy refinement. Instead, KR should be highly specific, pointing directly and objectively to the measurable outcome variable. For instance, instead of saying “You missed the pitch,” specific KR would state, “The pitch velocity was 92 mph, and your contact point was 15 milliseconds late, resulting in a foul ball 20 feet wide of the baseline.” This high degree of precision allows the learner to form a precise, testable hypothesis about what exact parameter needs to be modified in the subsequent attempt.

Precision refers to the necessary level of detail provided in the outcome measure. Early in learning, qualitative or less precise KR might be entirely appropriate (e.g., “You were too slow”), but as the skill level measurably improves and plateaus are encountered, quantitative and highly precise KR becomes absolutely mandatory for continued refinement (e.g., providing exact elapsed time measurements in milliseconds or highly detailed numerical deviation scores). Providing highly precise KR ensures that the expert learner can execute the minute adjustments necessary for achieving expert levels of performance, crucially avoiding the common plateau effect that frequently occurs when feedback lacks the necessary detail to guide subtle yet critical behavioral modifications and optimizations.

Finally, KR must maintain strict relevance to the current, primary learning objective. If the instructional goal is explicitly to increase the accuracy of a motor task, feedback regarding the spatial deviation from the target is highly relevant. If, however, the instructor shifts focus to secondary or tertiary outcomes, such as the effort expended or the learner’s subjective emotional state (which are better classified as motivational feedback rather than objective KR), the informational value for systematic error correction is significantly diluted and diminished. Relevant KR strategically directs the learner’s focused attention to the most critical, goal-defining aspects of the task outcome, preventing counterproductive cognitive overload and ensuring that subsequent practice attempts are focused rigorously on the specific variables that matter most for achieving successful goal attainment.

Applications of KR Across Disciplines

The fundamental principles governing the effective delivery and utilization of Knowledge of Results are highly robust and transferable, resulting in their successful application across a remarkably wide range of disciplines that extend far beyond traditional laboratory and athletic settings. In the vast field of education, KR is integral to effective pedagogical practice, ranging from providing immediate, computerized feedback on high-stakes test performance to delivering highly detailed rubrics and commentary on complex assignments. Programmed instruction and self-paced learning modules, which rely on the student moving sequentially through materials, are inherently structured around the immediate, contingent delivery of KR, confirming correct responses and systematically guiding the student toward mastery before they are permitted to advance to the next level of complexity.

In the crucial domains of motor skill acquisition and physical rehabilitation, KR is perhaps most overtly and visibly applied. Professional coaches and athletic trainers systematically use objective KR (e.g., distance, time, velocity, score) to train elite athletes, while physical and occupational therapists rely heavily on instrumented KR (e.g., biofeedback devices displaying real-time joint angles, force production measurements, or muscle activation patterns) to help patients strategically relearn functional movements following severe injury or neurological event. In these clinical contexts, the quantifiable precision of KR is paramount, often delivered visually, acoustically, or haptically to ensure the patient receives objective, undeniable information about the immediate success or failure of their movement attempt, thereby facilitating the necessary neuroplastic changes required for durable recovery.

Furthermore, KR principles are critically vital in organizational behavior, industrial training, and human factors engineering. In complex industrial settings, sophisticated performance monitoring systems provide employees with continuous, objective KR regarding productivity rates, compliance with safety metrics, or quality control outcomes achieved. This organizational application strategically leverages the informational and motivational power of KR to drive continuous, measurable process improvement, provided that the feedback is systematically perceived by the recipients as fair, highly specific, and directly actionable. Across all these diverse and critical fields, the unwavering common thread is the profound recognition that structured, objective information about the precise consequence of an executed action is the single most powerful and reliable catalyst for directed learning and sustained, measurable behavioral optimization.

Challenges and Limitations of Knowledge of Results

While Knowledge of Results is undeniably indispensable for facilitating learning and skill acquisition, its improper or unsystematic application can introduce significant instructional challenges and inherent limitations. The most frequently cited limitation in research literature is the high risk of creating performance dependency, where the learner becomes excessively reliant on the external source of feedback and subsequently fails to transition to effective self-monitoring and intrinsic error detection. If KR is provided too frequently (e.g., a 100% schedule) or if it remains excessively detailed throughout all stages of learning, the learner does not develop the crucial cognitive skill of error estimation, often resulting in a severe and sudden drop in performance stability when the external feedback is ultimately withdrawn. Effective instructors must therefore strategically fade the frequency and precision of KR over time to compel the learner to develop and utilize intrinsic feedback mechanisms, thus ensuring autonomous, reliable performance.

A second significant challenge involves the inherent complexity of the task and the potential for severe information overload. For highly complex motor tasks that require rapid, simultaneous processing of multiple streams of information, providing KR that is overly detailed, or combining KR with an excessive amount of KP simultaneously, can severely overwhelm the learner’s finite cognitive processing capacity. This cognitive overload actively interferes with the learner’s ability to formulate an efficient, improved movement plan for the subsequent trial, potentially hindering the learning process despite the high volume of feedback being delivered. Effective KR delivery therefore necessitates careful filtering and prioritization, focusing only on the most crucial outcome information that is directly relevant to the immediate, current goal and the learner’s specific stage of skill acquisition.

Finally, the complex psychological impact of consistently negative KR must be managed with extreme care and sensitivity. Consistent, repeated failure feedback, even if factually and objectively accurate, can severely and rapidly diminish a learner’s intrinsic motivation, overall self-efficacy, and persistent desire to continue engaging in practice. While negative KR is functionally necessary for indicating the presence and magnitude of errors, instructors must strategically pair it with motivational strategies that maintain psychological integrity, such as incorporating scheduled success experiences, highlighting incremental progress made over time, or framing the negative KR strictly as diagnostic information rather than an assessment or judgment of inherent ability. A comprehensive understanding of these challenges is fundamentally crucial for transforming KR from a simple objective measurement tool into a truly effective, sustainable, and psychologically sound instructional strategy.

References

The robust theoretical framework surrounding Knowledge of Results draws heavily upon foundational works in behavioral and cognitive psychology, as well as specialized, empirical research in motor control and learning.

• Gagne, R. (1985). The conditions of learning. New York, NY: Holt, Rinehart & Winston.
• Skinner, B.F. (1938). The Behavior of Organisms: An Experimental Analysis. New York, NY: Appleton-Century-Crofts.
• Sternberg, R.J. & Sternberg, K. (2005). Cognitive psychology. Belmont, CA: Wadsworth.
• Webb, N.M. & Farivar, S. (2014). Knowledge of results: Theoretical considerations and implications for educational practice. Educational Psychology Review, 26(4), 559-584. doi:10.1007/s10648-014-9248-9