State of science: evolving perspectives on ‘human error’

Figures & data

Figure 1. Trends in the use of the term ‘human error’ across journal articles from 1940 to 2020, comparing: Human Factors, Applied Ergonomics, Ergonomics and all records in Scopus (note, Scopus figures refer to secondary y axis due to higher numbers overall).

A graph showing trends between the year 1940 and 2020. All trend lines are close to zero until approximately 1960. Between 1960 and 2020, trend lines for the journals Human Factors, Applied Ergonomics, and Ergonomics appear to rise somewhat. Between 1960 and 2020, the trend line for all records in Scopus rises more sharply.

Table 1. Perspectives on safety management and accident causation.

Perspective	Typical conceptualisation of human-in-system behaviour	Typical unit of analysis	Error-as-cause	Safety-I or Safety-II
Mechanistic perspective	Complicated	Micro – the human	Often	Safety-I
Individual perspective	Complicated	Micro – the human	Often	Safety-I
Interactionist perspective	Complicated	Meso – the human and broader context, sometimes the organisation	Often	Safety-I
Systems perspective	Complex	Macro – the system as unit of analysis	–	Safety-I or -II

Table 2. Overview of human performance and human error models; in order of publication date.

Model	Key aspects/description	Human error component/types	Theoretical underpinning
Perceptual cycle model (PCM; Neisser 1976)	Cyclical model of perception where top-down processing (schemata) and bottom-up processing (information in the world) drive one another An active schema sets up the expectations of the individual in a particular context Expectations direct behaviour to seek certain kinds of information and provide a ready means of interpretation As the environment is sampled, the information updates and modifies the schema, which directs further search	Errors can occur where: Inappropriate schemas are activated Schemas are incomplete due to a lack of previous experience (leading to undesirable search strategies or undesirable actions, or both)	Schema theory (Bartlett 1932) Notion that human thought is closely coupled with interaction in the world
Activation-Trigger-Schema (ATS) / Norman-Shallice model (e.g. Norman 1981)	Accounts for action slips – events where an action is not executed as intended. Like the PCM, is based on the activation and selection of schemas A triggering mechanism requires that appropriate conditions be satisfied for the operation of a schema Accounts for both external sources of schema activation (i.e. from the world) and internal sources (such as thoughts, associations and habits)	Main sources of action slips include: Slips during formation of intention (errors in classifying the situation or resulting from ambiguous or incompletely specified intentions) Mode errors (erroneous classification of the situation) Description errors (ambiguous or incomplete specification of intention) Unintentional activation of schemas (schemas not part of a current action sequence become activated for extraneous reasons) Loss of activation or decay of schema False triggering (a properly activated schema triggered at an inappropriate time) Failure to trigger (active schema is not invoked)	Schema theory (Bartlett 1932) PCM (Neisser 1976)
Skill, rule, and knowledge (SRK) framework (Rasmussen 1982)	Proposes that information is processed at three levels: Skill-based: Behaviour is controlled by learnt and stored patterns of behaviour. Rapid, effortless, occurs outside conscious control Rule-based: Rules stored in memory applied in setting a plan for action Knowledge-based: Conscious planning and problem solving to determine appropriate response in unfamiliar or unusual situations Rasmussen’s (1974) decision ladder represents the interaction of the three levels identifying information processing stages and resultant states of knowledge from the time of becoming alerted of the need to make a decision to action execution	Three types of errors, based on the information processing levels: Skill-based errors relate to variability of force, space or time coordination Rule-based errors involve incorrect classification or recognition of situations, erroneous associations to tasks, or to memory slips in recall of procedures Knowledge-based errors relate to goal selection Performance shaping factors acknowledged to influence or contribute to errors	General problem solver model, based on means-ends analysis (Newell and Simon 1972)
Execution-evaluation cycle model of human information processing (Norman 1988)	In The Design of Everyday Things, Norman proposed seven stages of action associated with interacting with the world (four stages of execution and three stages of evaluation): Forming the goal Forming the intention Specifying an action Executing the action Perceiving the state of the world Interpreting the state of the world Evaluating the outcome	Errors occur where mismatches exist between the system and the human: The gulf of execution relates to the extent to which the system or artefact provides opportunities for action that relate to the user’s intentions The gulf of evaluation relates to the extent to which the actual interaction possibilities of the system or artefact fit user-perceived interaction possibilities	Ecological psychology (Gibson 1979) Distributed cognition (Hutchins 1995)
Generic error modelling system (GEMS; Reason 1990)	GEMS provides a way to classify errors at each level of performance defined in Rasmussen’s (1982) SRK model, with addition of intentional behaviours (violations) Errors can occur at each level of performance or are associated with operating at an inappropriate level of performance	Four categories of unsafe acts: Skill-based errors (i.e. slips and lapses), usually relating to attention Rule-based mistakes, involving the misapplication of good rules or the application of bad rules Knowledge-based mistakes, involving incomplete or inaccurate understanding, or cognitive biases Violations, involving routine violations, exceptional violations or acts of sabotage	SRK model (Rasmussen 1982) Other psychological models and literature (e.g. Tversky and Kahneman 1974)
Model of human information processing (Wickens and Hollands 1992)	Proposes that environmental stimuli are first processed by the short-term sensory store, then move through stages of perception, decision and response selection and response execution Limited attentional resources are applied to support processing, and both short term and working memory influence perception, decision and response Feedback loops exist from the response to the environment, which affects the stimuli available for processing	Errors can occur within the different stages of processing: Perception Memory Decision making Response execution	Cognitive psychology, linear input-output models such as: Broadbent’s (1958) stage model of attention Baddeley and Hitch’s (1974) model of working memory
Contextual control model (COCOM; Hollnagel 1993; Hollnagel and Woods 2005)	Focuses on the functions that explain performance within a joint cognitive system Considers how the joint system (encompassing humans and technology) acts to achieve its goals while at the same time responding to events in the wider environment Cyclical model shows how the current understanding of the situation (constructs) directs and controls the action taken, and how the action taken is fed back along with information received to modify current understanding	Three main constituents: Competence – possible actions or responses that a system can apply to a situation according to recognised needs and demands Control – orderliness of performance and the way in which competence is applied (i.e. scrambled, opportunistic, tactical, strategic) Constructs – what the system knows or assumes about the situation in which the action takes place, similar to schemata, these are the basis for interpreting information and selecting actions	Neisser’s (1976) PCM, extended to incorporate action and control
Rewritable routines (Baber and Stanton 1997; Stanton and Baber 1996)	Analyses human-technology interactions Human-machine interactions follow a sequence of states and the human engages in simple action sequences, based on the affordances of the environment, to move from the current state to the next. Only simple planning is undertaken at each stage, rather than planning the entire interaction through to the final goal state To move between states, the human holds a record of the interaction in working memory which is modified at each state. Because the record is modified at each state, it is considered ‘rewritable’	Errors can occur where: Working memory decays due to the record not being updated or rehearsed within a certain timeframe User takes action that appears that it will help them progress to a relevant state although it will not User incorrectly rejects an action that would have helped them progress to a relevant state	Newall and Simon’s (1972) concept of problem-solving as moving through a problem space Suchman’s (1987) situated planning Neisser’s (1976) PCM Baddeley and Hitch’s (1974) model of working memory

Table 3. Human error methods and taxonomies.

Method	Purpose	Human error taxonomies applied	Theoretical/empirical underpinning	Example application areas	Total citations	Citations last 15 years	Citations last 15 years (excl. self-citations)
Retrospective methods
Human Factors Analysis and Classification System (HFACS; Weigmann and Shappel 2001)	Accident analysis	Failure taxonomy with four levels – unsafe acts (errors and violations), preconditions for unsafe acts, unsafe supervision, organisational influences	Reason’s (1990) GEMS model	Rail (Madigan, Golightly, and Madders 2016) Aerospace (Alexander 2019) Healthcare (Igene and Johnson 2020)	252	238	228
Prospective methods
Human error assessment and reduction technique (HEART; Williams 1986)	HEI and HRA	Generic task types, taxonomy of error-producing conditions and taxonomy of remedial measures	Engineering reliability analysis and engineering experience HTA based on theory of goal-directed behaviour	Chemical tanker maintenance (Akyuz and Celik 2015) Process control maintenance (Noroozi et al. 2014) Surgery (Onofrio and Trucco 2020)	140	106	106
Human Error HAZOP (Kirwan and Ainsworth 1992)	HEI	Guidewords focussed on human error (e.g. not done, less than, more than, repeated)	Engineering reliability analysis HTA based on theory of goal-directed behaviour	Shale gas fracturing (Hu et al. 2019) Chemical storage (Yang et al. 2019)	n/a^6	n/a	n/a
Human error identification in systems tool (HEIST; Kirwan 1994)	HEI	Behaviour categories (e.g. activation / deactivation, observation / data collection, identification of system state, goal selection), global PSFs (e.g. time, procedures, task complexity), EEMs, PEMs, error reduction guidelines	Rasmussen’s SKR framework HTA based on theory of goal-directed behaviour	Oil and gas control room (Nezhad, Jabbari, and Keshavarzi 2013) Aviation (Stanton et al. 2010)	124^7	11	11
TAFEI (Baber and Stanton 1994)	HEI	None	General systems theory – interactions between components (humans and machines) rather than the components themselves Theory of Rewritable Routines (Baber and Stanton 1997; Stanton and Baber 1996) HTA based on theory of goal-directed behaviour	Ticket vending machines (Baber and Stanton 1996) IT maintenance (Barajas-Bustillos et al. 2019)	50	34	18
Systems for Predicting Human Error and Recovery (SPEAR; CCPS (Centre for Chemical Process Safety) 1994)	HEI	EEMs (e.g. action errors, checking errors, communication errors) and PEMs (e.g. spatial misorientation, misinterpretation)	Similar to SHERPA	None identified	2^8	2	2
Human Error and Recovery Assessment (HERA) Framework (Kirwan 1998a, 1998b)	HEI	EEMs and PSFs	Toolkit approaches to EFE EEM taxonomy adapted from SHERPA & THERP Integration of HEIST Integration of Human Error HAZOP	Nuclear power (Kirwan 1998b)	51	41	41
Technique for Human Error Assessment (THEA; Pocock et al. 2001)	HEI and HRA	Types of cognitive failure relating to goals, plans, actions, perception / interpretation / evaluation	Execution-evaluation cycle of information processing (Norman 1988) HTA based on theory of goal-directed behaviour	Flight deck re-design (Pocock et al. 2001) Security sensitive computer interfaces (Maxion and Reeder 2005)	37	26	15
Human Error Template (HET; Stanton et al. 2006)	HEI (predictive)	Checklist of 12 error modes (e.g. failure to execute, task execution incomplete, task executed in wrong direction)	Error modes selected based on existing HEI methods and study of pilot errors HTA based on theory of goal-directed behaviour	Aviation (Stanton et al. 2006) Industrial manufacturing (Tajdinan and Afshari 2013)	41	41	25
Systematic Human Error Reduction and Prediction Approach (SHERPA; Embrey 1986)	HEI (predictive)	EEMs (e.g. action errors, checking errors, communication errors,) and PEMs (e.g. spatial misorientation, misinterpretation) PIFs (e.g. noise, lighting, fatigue)	PEMs based on Rasmussen’s SRK framework HTA based on theory of goal-directed behaviour	Aviation (Harris et al. 2005) Petrochemical processing (Ghasemi et al. 2013)	151	135	127
Both retrospective and prospective methods
Cognitive reliability and error analysis method (CREAM; Hollnagel 1998)	HEI, HRA and accident analysis	Common performance conditions (similar to PSFs; e.g. adequacy of organisation, work conditions), error modes (phenotypes; e.g. action at wrong time), person-related genotypes (e.g. false observation), technology-related genotypes (e.g. software fault), and organisation-related genotypes (e.g. design failure)	COCOM model HTA based on theory of goal-directed behaviour	Submersible diving (Chen et al. 2019) Maritime (Wu et al. 2017)	1,149	1,119	1,101
Technique for the Retrospective and Predictive Analysis of Cognitive Errors (TRACEr; Shorrock and Kirwan 2002)	HEI and accident analysis	Task error, Information, Performance shaping factors, EEMS, Internal error modes, PEMs, Error detection, Error correction	Information processing model (Wickens and Hollands 1992) EEM classification adapted from THERP (Swain and Guttmann 1983)	Air traffic management (Shorrock and Kirwan 2002) Rail (Baysari, Caponecchia, and McIntosh 2011)	192	186	178

Note: EEM: external error mode; PEM: psychological error mechanism; PSF: performance shaping factor; PIF: performance influencing factor. Citations from Scopus, to end 2020, self-citations and citations from books included unless otherwise noted.

Table 4. Human error defined in relation to complex systems properties.

Complex system property	Description	Implications for how human error is viewed
Outcomes emerge from interactions between system components	Interactions between components produce emergent phenomena. These can only be understood by analysing the system as a whole, rather than examining components in isolation (Dekker 2011b; Leveson 2004)	A detailed understanding of a human error cannot provide an explanation of an accident, nor can predicting individual behaviours provide an indication of the level of system safety. Analyses should focus on the interactions that lead to behaviour and the situations within which these occur
System and component performance are variable	System components and systems themselves are constantly adapting in response to local pressures and unforeseen disturbances (Hollnagel 2009). Adaptation and variability ensure survival under ever-changing environmental conditions (Vicente 1999). The presence of performance variability makes it difficult to predict component behaviour and system performance	If performance is variable and adaptation is required to make systems work, human error simply describes the unwanted result of trade-offs, which under normal circumstances result in desirable outcomes
Systems are dynamic	Systems involve dynamic processes, such as the transformation of inputs into outputs, and the operation of feedback loops. Through these processes, systems evolve over time in response to changing conditions. Tending towards entropy, systems migrate towards a state of increased risk (Rasmussen 1997; Leveson 2011) and can drift into failure (Dekker 2011b)	Human behaviour should be viewed in the context of the dynamic factors and state of the system at the time it occurred
Systems are organised in hierarchical structures	Systems tend to self-organise into hierarchies of systems and sub-systems (Skyttner 2005). To understand a system, it is necessary to examine each relevant hierarchical level and its relationship with those above and below (Rasmussen 1997; Vicente 1999)	‘Human errors’ can occur at all levels of a system, from governments to CEOs, to supervisors and frontline workers Accidents can only be understood by going beyond the immediate work environment to the influences within the management system and broader social and political environment

Figure 2. Overview of the evolution of models (white boxes), methods (white ovals), and underpinning theory (grey boxes) by perspective. Note, alignment against perspectives is intended to be approximate and fuzzy rather than a strict classification.

An illustration of models, methods and theories showing the relationships between them, over time from the 1930s to present day.

Figure 3. A proposed way forward.

A list of recommendations for the way forward for the EHF discipline.

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