Human Error and Perceptual Control Theory

09 Apr 2018

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In this paper, I will explore and advocate the importance of both Human Error and Perceptual Control Theory for design of complex human-machine systems, enhanced user experience and better human performance and safety.

Human Error

Errors are the result of actions that fail to generate the intended outcomes (SKYbrary). Human operators are one of the biggest sources of errors in any complex system (Shelton, 1999). According to Sanders & McCormick (1976), Human error is an inappropriate or undesirable human decision or behavior that reduces, or has the potential for reducing effectiveness, safety and performance of a system and/or human (Kurniawan).

Error Types

Researchers have differentiated between two types of error:1) slipsand lapses 2) mistakes.

Slips and lapses

According to SKYbrary, a person intends to carry out an action, the action is appropriate, carries it out incorrectly, and the desired goal is not achieved - an execution failure has occurred. Execution errors result from failures in the execution and/or storage stage of an action sequence. Slips relate to observable actions and are commonly associated with attentiveness or perceptual failures. Lapses are more internal events and generally involve failures of memory (SKYbrary).


As mentioned in, Mistakes occur when an intended outcome is not achieved even though there was adherence to the steps in the plan. This is usually a case in which the original plan was wrong, was followed, and resulted in an unintended outcome (

Error mechanisms

The following three error mechanisms are widely accepted, which correlate with human performance levels.

Skill-based errors

  • Errors of execution
  • Occur during highly routine activities or automated tasks with occasional checks
  • Action chosen by the operator but not in accordance with the operator’s intentions
  • Done by highly experienced individuals due to inattention or distraction

Rule-based errors

  • Applies to familiar situations
  • Incorrect application of a good rule, correct application of a bad rule or failure to apply a good rule

Knowledge-based errors

  • Occur in unique and unfamiliar situations
  • Result from inadequate analysis or decision making (trial and error)
  • Done by operators with insufficient knowledge

Applications of Human error theory

Human error is inevitable. However, human error mitigation strategies could be devised by understanding various error mechanisms and triggers, as depicted in the human error theory. Superior system design, better recruitment and selection of operators, training, stress and fatigue prevention measures, better equipment procedures and improved work environment can reduce error consequences and likelihood. By understanding human error, system designers can plan for likely error scenarios, and implement barriers to prevent or mitigate the occurrence of potential errors. Some approaches to build a better human machine interaction system are explained below.

Identification of error environment

First step in human error mitigation approach is to understand the work environment, recognize capacity of the users, identify possible loopholes n the system and be familiar with potential user error occurrence and consequences. Likelihood of an error and severity of potential harm should also be examined. For example, FDA (Food and Drug Administration) requires manufacturers to submit a failure analysis report FMEA (Failure Mode and Effect Analysis) while launching any new medical device.

Design solutions to address errors

Error elimination

First and foremost design strategy is to eliminate design features, which are sources of user errors. Design weaknesses identified during observation and task analysis should be removed or revised. Removal of excessive and irrelevant information, inclusion of validity checks and task automations are some design aspects that support error elimination approach. Additionally, carrying out periodical test runs of the system might be helpful in eliminating some of the rules based errors.

Error reduction

Designers should try to reduce error occurrences for features that cannot be removed completely. Building consistent designs and providing alerts, warnings, confirmations and other necessary feedback to users may prevent users to make errors of execution (skill-based errors).

Consequence elimination

Consequence elimination is an approach to prevent potential harm after the occurrence of error. Designers can devise features that provide information about potential harm and ways to correct the situation (e.g. undo) and/or prevent onset of side effects (e.g. automatic locking or shutdown, process delays) in order to prevent error consequences.

Consequence reduction

This is a last design alternative a designer can look into if above mentioned options are not feasible to incorporate. Decreasing the effect of error is helpful especially in catastrophic situations. Design of supplementary features is usually necessary to achieve this purpose. Backup and restore features, automatic reporting to stakeholders/police/medical teams and automated substitute drug delivery are some of the techniques for reducing effects of consequences.

Error elimination and error reduction are often the most cost effective methods to avert user errors.


Knowledge-based errors can be eliminated to some extent by providing system-oriented trainings, especially to novice users. On the other hand, a different training program could be devised for experienced users. Periodical trainings could keep experts up-to-date with latest developments in their field and assess their knowledge of system procedural checks. Essentially, this may help minimizing skill-based errors.

Perceptual Control Theory (PCT)

Perceptual control theory (PCT) is a theory of human and animal behavior. It is based on the principles of control theory (Powers, 1973). As cited by Lulham (2005), at the core of PCT is the idea that many of the processes involved in how human functions are most appropriately conceptualized and modelled as dynamic control processes (Powers, 1999b). Control processes are proposed to be fundamental to many functions including those related to physiological (e.g. temperature regulation), neural (e.g. attention), motor control (e.g. driving a car), psychological (e.g. maintenance of a criminal identity) and social (e.g. staff-detainee relationship) functions (Lulham, 2005).

According to Cherry & Farrel (1998), PCT exploits the concept of a purpose behind the behaviour. A perception (which is a transformation of stimuli from the world) is then compared to its reference signal, and a perceptual error is generated. A person acts on the world in such a manner to minimise this error. The stabilisation of this control loop is the essence of PCT (Chery & Farrell , 1998).

Applications of PCT

Using perceptions for building complex systems

System environments are becoming increasingly complex. Traditional cause and effect methods of understanding system operations and user interactions may not work well in order to employ personalization and user experience in these complex systems.

According to, the person compares a ‘standard’ (what they want) with what they are experiencing right now (their perception). The difference between the two – the discrepancy or error is being measured. The bigger the error the more the effort the person makes to reduce it, until the error is zero – this means they get what they want ( The basic premise of PCT is that human behavior is not about the behavior itself, but about reinforcing desired perception (O’Neal, 2012). Understanding and applying this concept of individually preferred perceptions through PCT will help in designing effective personal information management for complex systems and enhancing the overall user experience.

User research and analysis

PCT is particularly helpful in understanding users’ behaviors and motives behind their actions. Often system designers evaluate possible system states and static change in control values needed to achieve those states. Tasks are carried out on the controls to attain the new state and user is though of as a controller of these tasks. Designers use task analysis method to perform user research and concentrate on physical tasks a user performs. However, instead of system oriented or designer oriented view, PCT goes much deeper and offers user’s standpoint.

PCT provides framework to realize dynamic nature of user interactions. System designers can use PCT to understand how users constantly perceive and compare system states and take dissimilar actions to reinstate appropriate system state every time. Furthermore, Powers (1973) proposed behavior as a control of perceptions. Instead of focusing on a physical activity during the task analysis, PCT suggests focusing on users’ behaviors that lead them to perform actions to achieve desired perception (system state). Thus, using the PCT framework, designers can integrate physical, cognitive and behavioral sides of a user’s interaction with the system.

Moreover, PCT analysis of intentions from different user groups exposes their shared narrative, which in turn, helps in finding system requirements for hidden, absent user (O’Neal, 2012). For example, a customer service representative might use call center software while working to resolve an issue with a customer. While the customer is not a direct user of the software system, he is affected indirectly. The customer here is a hidden user. His perceptions should be analyzed to understand his requirements and objectives while developing the call center system.

Empowered designs

PCT offers a design framework toward the satisfaction of the user's desired percepts. Human-machine system performance is enhanced when the displays and controls are designed to allow the operator to perceive and transmit information in order to minimize the perceptual error (Chery & Farrell , 1998).

According to PCT, when a user interacts with the system, he is constantly trying to bring equilibrium, changing his perception to the reference point, which is his new perception. Keeping this in mind, a designer should build the system that transforms from an old state to the new state seamlessly, provides estimate of gap between old and new state, furnish necessary feedback to keep the user aware of the environment variables and helps the user to manage disturbances. These functionalities will help users to gain accurate information regarding their perceptions, empower them to undertake correct amount of action to reach to new perception, ultimately helping them achieve self-regulation and stability. For example, the windshield of the car let the user scan his environment and gather information necessary to their perception, the car dashboard continuously displays speed and other important elements to help user assess different system states, and the gear stick helps user achieve new state from the old one smoothly. Other features like wiper, headlights, turn signals etc. facilitate users to manage disturbances as much as possible. Thus, human-machine designs should be compatible with user’s interpretations of information in order to improve their decision making process and overall system performance.


Both human error and PCT frameworks are valuable in building complex system designs to facilitate information management, enhance security and improve both system and human performance.

By understanding fundamentals of human errors, designers can build a system that is more usable, provides meaningful feedbacks and include training, procedural checks and incentive programs. However, according to Shelton (1999), there is a trade-off between making the HCI relatively easy and intuitive and ensuring that system safety is not compromised by lulling the operator into a state of complacency.

In PCT, the error is continuously measured to achieve equilibrium. Per Lulham (2005), those involved in developing the theory believe PCT has significant potential to change the way human functioning is understood. However, further research is required for advancement of PCT framework.



Lajja Mehta

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