A theory of human ecological control networks: the role of affordation, emotion, and intuition in the design of human environments
Abstract
This paper introduces a theory of the human ecological control networks (HECON). The proposed theory synthesizes the selected concepts from the psychological theory of affordances, cybernetics, ergonomics design, and neuroscience. Affordances of the environment are interrelated with the personal human attributes in terms of neural (brain) manifestations of emotions and intuition. A process of ecological perception and cognition, i. e. the human understanding of the environment in terms of what the environment affords to the operator and the acting upon such an understanding in order to exercise control over that environment (workplace design) was defined as affordation. The affordation-, emotion- and intuition-based design models for human environments correspond to the learning-, adaptive- and tuning-based human ecological control networks, respectively.
2 Figures
Ecological models of human performance based on aordance,
emotion and intuition
KRYSTYNA GIELO-PERCZAK{* and WALDEMAR KARWOWSKI{
{Liberty Mutual Research Center for Safety & Health, 71 Frankland Road,
Hopkinton, MA 01748, USA
{Center for Industrial Ergonomics, University of Louisville, Louisville,
KY 40292, USA
Keywords: Brain; Human performance models; Control; Aordance; Emotion;
Intuition; Learning; Adaptation; Tuning; Ecological approach.
This paper proposes a complementary approach to Rasmussen's taxonomy of the
human skill-, rule-, and knowledge-based performance models by combining the
ecological concept of aordances with the neural concepts of human emotion and
intuition. The classical cognitive engineering framework is extended through the
neuro-ecological approach, including personal human attributes important in
exercising control over the work environment. The proposed aordance-,
emotion-, and intuition-based models correspond to the three types of human
performance, namely: learning, adaptive and tuning control, respectively. The
new framework is not a predictive model of the operator behaviour, but rather it
describes the processes of neuro-ecological control of the human environment.
1. Introduction
At present there is a need for a conceptual framework of a human system with
perceptive insight into the complexity of the mutual relationships between human
performance and the environment (Gielo-Perzcak 2001b, Karwowski 1991, 1992).
Contemporary work environments often demand eective human control, predic-
tions and decisions in the presence of uncertainties and unforeseen changes in work
system parameters (Salvendy 1997). The description of human operators who
actively participate in purposeful work tasks in a given environment, and their
performance on such tasks should re¯ect the complexity of brain activity, which
includes cognition and the dynamic processes of knowing. Moreover, in addition to
the human operator's experience, knowledge and professional training, the models of
work processes should also include the perceptual, emotional, and intuitive aspects
of human performance at work.
One way in which human abilities at work can be categorized is by applying the
cognitive engineering framework proposed 20 years ago by Rasmussen (1983). This
approach includes the skill-based, rule-based, and knowledge-based modes of human
performance with reference to dierent classes of human information processing that
involve signals, signs and symbols, respectively:
*Author for correspondence. e-mail: Krystyna.Gielo-Perczak@LibertyMutual.com
ERGONOMICS, 2003, VOL. 46, NO. 1 ± 3, 310 ± 326
Ergonomics ISSN 0014-0139 print/ISSN 1366-5847 online #2003 Taylor & Francis Ltd
http://www.tandf.co.uk/journals
DOI: 10.1080/0014013021000035433
1. Skills-based automated processes without invoking conscious information
processes;
2. Rules-based routine processes, such as executing prescribed methods and
procedures;
3. Knowledge-based processes, such as de®ning objectives, identifying pro-
blems, and initiating problem-solving.
Skill-based behaviour refers to sensory-motor-performance which take place without
conscious control, as smooth, automated and highly integrated patterns of
behaviour. In this view, human activities are considered as a sequence of skilled
acts composed for the actual situation. Rule-based behaviour refers to goal-oriented
performance structured by feed-forward control through a stored rule or procedure
allowing composition of a sequence of subroutines in a familiar work situation
(Goodstein et al. 1988; Johannsen 1988). Rule-based performance is based on the
explicit know-how of employing the relevant rules, while the reference data consists
of references for recognition and identi®cation of states, events, or situations
(Rasmussen 1983). Knowledge-based behaviour refers to goal-controlled perfor-
mance, where the goal is explicitly formulated based on knowledge of the
environment and the aims of the person (Johannsen 1988). This kind of behaviour
allows the operator to develop and test dierent plans under unfamiliar and
uncertain conditions. It is particularly needed when skills or rules are either
unavailable or inadequate, so that conscious problem solving and planning are called
for in order to meet the demands of unfamiliar and complex situations (Goodstein
et al. 1988).
Recently, Hoc (2000) enlarged this model by a parallel cognitive approach to deal
with the necessary compromise between comprehension (diagnosis and prognosis)
and action in dynamic situations. Such dierent forms of mental models are also
implied by Rasmussen (1983). The ®rst application of these models was developed
and presented in a framework called the decision ladder, which can be used in
describing and analysing the decision-making processes. The second was the
supervisory control of highly complex systems from monodisciplinary control
problems to multidisciplinary human decision making (Stassen 1997). This
application was recently presented by Sheridan's (1992) and Parasuraman et al.
(2000) supervisory control models, which are qualitative models incorporating such
human operator tasks as planning, teaching, monitoring, intervening and learning.
The inter-dependencies of operators and resources of work systems which
contribute to performing the control activity (hardware and software; guidelines,
procedures and rules used to support and regulate the activities of the operator),
were also utilized by the theory of distributed cognition (Pasquini et al. 2000).
Recently, cognitive engineering is forward-looking to the testing of intuition-based
models, and the role of cognition in the ®eld of application known as physical
ergonomics (Karwowski et al. 1999).
2. Objectives
The majority of contemporary ergonomics literature refers to three modes of human
performance based on Rasmussen's (1983) framework that includes the skill-, rule-
and knowledge-based forms. This framework proposes that people can form internal
representations that constitute their structural and functional understanding of a
work system, and which allow them to describe, explain, understand, and predict the
311
Affordance, emotion and intuition
work system behaviour. Rasmussen (1983) proposed a cognitive engineering
approach to the modelling of human behaviour based on the classical stimulus-
response paradigm of information processing, with human performance as mapping
from a set of stimuli to a set of responses.
As a continuation of the above categorization scheme of human performance, in
this paper we propose an expanded and supplementary framework, based on
dynamic human functioning. Such a framework is de®ned as mapping from a set of
aordances, emotions, and intuition to human learning-, adaptive- and tuning-based
performance. The proposed approach is complementary to the Rasmussen's (1983)
three modes of activity control, in that it expands the previous framework with the
neural (brain) networks in reference to ecological perspective to human-environment
design (Gibson 1979). This neuro-ecological approach to modelling of human
performance focuses on brain processing, including information pickup, emotions,
and intuition. As the human brain is a dynamical system that aims to exercise control
over the environment, human performance can be modelled as a dynamic, nonlinear
process taking place over the interactions between the human brain and the
environment, based on the concepts of aordances, emotion and intuition. The
interrelationships between aordances, emotion and intuition play an important role
and should be taken into account in modelling human performance at work.
3. A neuro-ecological approach based on aordance, emotion and intuition
Today's work context refers to mental demands that aect a worker's ability to
perform a variety of tasks. Mental elements represent the ecology of human ±
machine systems in which the work tasks are performed (Hancock et al. 1995). These
elements can impede the worker's short-term ability to perform the job activities. In
any job activity, a certain number of steps are distinguished, including: activation,
observation, identi®cation, interpretation, evaluation, task de®nition, procedure
de®nition, and execution. An appropriate level of comprehension is needed to act
appropriately, but the priority for short-term control of the situation does not
always enable the human operator to comprehend the situation fully before acting
(Gielo-Perczak 2001a). Human consciousness is manifested in several brain
activities, including thought, perception, emotion, will, memory, and imagination
(Parasuraman 2000). For example, drivers often face situations in which routine
operations involve these individual attributes; particularly, when the driver senses a
hazard, he consciously becomes aware of it and diagnoses it quickly. However, we
also need tools for prediction of human performance with de®ned error modes
taking into account human emotions, imagination and intuition with reference to
aordances of the environment.
3.1. The theory of aordances
The noun `aordance' was ®rst introduced by Gibson (1979) to underscore the
complementarity of the human and the environment. The aordances of the
environment are what it oers or provides the human (and other organisms), for
human bene®t or human ill. According to Gibson (1986) what we perceive when we
look at objects are their aordances, not their qualities, i.e. what the objects aord us
is what we normally pay attention to. Humans aim to change and do change the
environment in order to change what the environment aords them.
Gibson (1979) proposed an in¯uential framework for the ecological psychology
based on his theory of aordances. Aordances are opportunities for action for a
312 K. Gielo-Perczak and W. Karwowski
particular organism. Thus, an aordance is objective in the sense that it is fully
speci®ed by externally observable physical reality, but subjective in the sense of being
dependent on the behaviour of a particular kind of organism. Gibson (1979)
suggested that perception of the world is based upon perception of aordances, of
recognizing the features of the environment, which specify behaviourally relevant
interaction. An animal's ecological niche is de®ned by what its habitat aords. When
an animal's physiological state no longer meets its internal demands, action is
generated to bring it to a more satisfying state.
In view of the above, there is no need to assume the presence of a complete
internal representation of the external world; when environmental regularities are
allowed to take part in behaviour, they can give it coherence without the need of
explicit internal mechanisms for binding perceptual entities. The question is: what
kind of information processing does the brain do and what is its purpose. The
answer is that the brain is exerting control over its environment. It does so by
constructing behavioural control networks, which functionally extend outside of the
body, making use of consistent properties of the environment including the
behaviour of other organisms. These networks and the control they allow are the
very reason for having a brain. To understand them, we must move beyond the
input-output processing emphasized by computational models of human perfor-
mance.
3.2. Ecological approach to human performance modelling
As behaviour aords behaviour, environment aords performance and performance
aords performance. According to Turvey and Shaw (1999), from an ecological
perspective the classical distinction between perceptual and conceptual needs to be
realized. Aordances and their speci®cations are counterpoints to the view that the
meanings constraining human behaviour reside in the brain. A prevalent
presumption of cognitivism is that knowledge in the form of concepts of
denumerable static objects and relations is needed for an animal to behave
felicitously (e.g., Nilsson 1991). Aordances contradict this presumption. As an
animal moves with respect to surrounding substances, surfaces, places, etc., some
opportunities for action persist, some newly arise, and some dissolve, even though
the surroundings analysed formally as conceptualized objects and relations remain
the same. A change of pace or a change of location can mean that a cleft in the
ground now aords leaping over whereas at an earlier pace or location it did not
(Turvey and Shaw 1995). Further, subtle changes of action can give rise to multiple
and marked variations in the opportunities for subsequent actions. The environ-
ment-for-the-animal is dynamic and action-oriented while the environment-in-itself,
that which has been the target of cognitive modelling by means of concepts, is ®xed
and neural with respect to an animal and its actions (Kirsch 1991).
A rethinking of the conceptual versus perceptual distinction in terms of
invariance detection is encouraged by arguments for the symmetry and speci®city
of animal-environment systems. Detecting invariants is at the heart of what an
animal knows about its environment. There are two kinds of invariant detection,
which are dierent but complementary. The kind of detection that underlies the
perception of persisting substances, surfaces, places, is more fundamental than the
kind of detection that underlines the perception of similarities. Gibson suggested the
term resonance of perceptual systems and the term abstraction by perceptual systems
for the kind of invariance that relates to classes and categories. `The real progress of
313
Affordance, emotion and intuition
explaining peculiarly human instances of knowing about will have to await an
understanding of the symmetry and speci®city that ®x the directness of perceiving as
a property of all animal-environment systems.' (Turvey and Shaw 1999)
4. Emotion
Damasio (1994) notes the neglect of emotion in twentieth century cognitive science
and neuroscience and suggests that, without considering emotion, we cannot place
the mind in relation to its revolutionary origins, nor to its role relative to the
organism as a whole. Also, according to Levine (2000), the rapid development of
neurobiology and neuroscience has led to a greater understanding of biological
cognitive functioning. Moss and Damasio (2001) note that without emotion we
cannot understand how the organism maintains homeostasis in the face of
environmental challenges. Emotions, and the feelings that follow emotions, are an
integral part of the value systems necessary for laying down long-term memory and
for reasoning and conscious decision-making.
Furthermore, as pointed out by Grossberg and Gutowski (1987), many common
decision-making tendencies that appear irrational may be a by-product of a system
designed for eective, if not optimal, real-time processing of a complex informa-
tional environment. Emotions are also warning signals on our activities when a more
pressing concern arises (Harris 2000). Harris points out imagination as an eect of
emotional involvement. He suggests that we experience emotion in response to
events that have not yet happened. We deliberate our future by experience of fear,
hope or excitement. The persuasive role of emotion in decision-making has been
provided by Damasio (1994). Damasio showed that adults with lesions in the frontal
cortex often fail to display any autonomic reactions by choosing risky courses of
action, whether tested in the laboratory or observed in daily live.
Emotions are integrated in a hierarchy of neurological processes that in¯uence
brain perceptual functions. There is behavioural and physiological evidence for the
integration of perception and cognition with emotion. An emotion de®nes the
organism's dynamic structural pattern and their interactions may lead to speci®c
responses in a work system (environment). Emotions as mental states represent
responsiveness (fear, pleasure, anger) and, along with sensation and perception,
cognition, personal and social context, are modi®ed by memory (Stuss and
Alexander 2000).
4.1. The anatomical basis and evidence from functional studies
Emotions are integrated in a hierarchy of neural operations from arousal and drive
to perceptual processes to self-awareness. Anatomical evidence converges to
illuminate the structure of the progressive integration of perception and cognition
with emotion. Essential structures for the perception, expression and developmental
evolution of awareness include the hypothalamus, amygdala, and sensory associa-
tion cortices (particularly of the right hemisphere) and the frontal lobes (Stuss and
Alexander 2000). Allman et al. (2001) propose that the anterior cingulate cortex is a
specialization of the neocortex rather than a more primitive stage of cortical
evolution. The anterior cingulate cortex contains a class of spindle-shaped neurons
that are found only in humans and the great apes.
The technique of functional magnetic resonance imaging (fMRI) revitalized
cognitive neuroscience; providing real-time correlates, accessibility, relatively low
cost (Grabowski and Damasio 1996). The functional ®ndings (Gevins et al. 1997)
314 K. Gielo-Perczak and W. Karwowski
suggest the anterior cingulate cortex is a specialized area of the neocortex devoted to
the regulation of emotional and cognitive behaviour. A large body of EEG data
indicates that the anterior cingulate is the source of a 4- to 7-Hertz signal present
when the subject is performing a task requiring focused concentration. The
amplitude of this signal increases with task diculty. There is also evidence that
when the subject is aware of having made an error, there is a negative de¯ection in
one cycle of this oscillation. This phenomenon has been referred to as `error-related
negativity' and it arises from the anterior cingulate cortex (Luu et al. 2000).
4.2. Intentionality and self-control
Our form of intention is important as a level of emotion. Feeling strongly about
something helps us accomplish it. `Strong', however, does not mean an outwardly
showy demonstration of feeling. Strong emotions may be felt quietly and inwardly.
Posner and Rothbart (1998) have proposed that the anterior cingulate cortex is
involved in the maturation of self-control as the individual progresses from infancy
to childhood to adulthood. There is also evidence of increased functioning in the
anterior cingulate cortex in individuals with greater social insight and maturity (Lane
et al. 1997). The anterior cingulate cortex has an important role in emotional self-
control as well as the capacity to focus on dicult problems, error recognition, and
adaptive responses to changing conditions.
4.3. Functional models of emotion
Functional models of emotion are intended to reveal the adaptive function of
emotion. Emotions contribute to an adaptive value for the organism by contra-
dicting the convention that emotion is not rational (Moat and Frijda 2000).
Emotions are concern-activated response patterns which take control in order to
prepare the organism for a suitable change in action. A concern can be the thought
of a desired state or event. Moat and Frijda (2000) proposed the three models of
emotions:
1. ACRES is the linear process model from appraisal to action tendency.
2. Will models concerns and appraisal. The model can be easily interrupted by
an emotional stimulus.
3. EMMA represents action readiness and coping. It demonstrates the basic
rationality of emotion.
All these models exhibit dierent strengths. The ®rst reveals that displaying emotions
are appropriate to the situations. The second supports the view that emotion is
essential to cognition.
The third concentrates on the emotional process like `irrational panic'. All these
models con®rm that an emotion should be considered as a key element in the work
control process.
Humans possess emotions and it is crucial how they activate and transpose them
to a work environment. We need to determine the in¯uence of emotions on the
phenomenon of out-of-logic mental activity, taking into account individual
dierences to which people depend on the experiential and rational thought
processing. Epstein et al. (1996) applied a global theory of personality referred to as
cognitive-experiential self-theory or CEST (Epstein 1994). CEST integrates the
cognitive and psychodynamic unconscious by assuming the existence of two
315
Affordance, emotion and intuition
interacting modes of information processing: a rational system and an emotionally
driven experiential system. On the basis of this theory Epstein et al. (1996) assumed a
person can be a `thinking type' ± the Need for Cognition (NFC) individual or a
`feeling-type' ± Faith in Intuition (FI) individual, a unimodal or bimodal person,
respectively. The ®rst type represents rationality and the second represents
experience.
5. Intuition
Intuition, or a concept of tacit knowledge, refers to information that cannot easily be
communicated verbally. Intuition to the ancients was ®re, but more in the sense of a
spark that initiates; it is the creative, the divine spark, the spirit itself (Young 1976).
For an intuitive person, free decision is eective as an opposite in nature to the
predictable type of decision. Intuition responds to its own philosophy of the future,
but it does not react in conformity to the laws that govern and contain secular
interchange (Young 1976). Intuition guesses the exceptional and it does not work by
rules. Intuitions are often things learned from direct experience and training.
Intuition has close links with perception and underlying assumptions about
perception. By examining intuition through an ecological psychology framework, the
problem takes on a dierent character which is no longer focused solely on the
expert's cognitive (or perceptual) processes. We may ®nd that what researchers have
called `intuition' is what Gibson (1986) termed `direct perception'. Although some
aspects of intuition may be hard-wired through evolution, intuition as direct
perception is information-based and can be developed through education and
extensive, deliberate practice. Characterized as direct perception, intuition is an
observable, lawful phenomenon that is measurable and potentially teachable.
5.1. Analysis of intuition
Intuition can be analysed as groups of: situational factors, process characteristics
and product. The situational factors of intuition refer to the factors that must be in
place in order for intuition to occur, namely knowledge and experience, empathy, as
well as incomplete data and uncertain situations. The second group of intuition
characteristics has to do with the process of intuition, which is more interpretive,
holistic and goal-directed. The third category of characteristics has to do with
intuition as a product which includes problem-solving and the rational justi®cation
of intuition by means of re¯ection.
5.2. Evidence from functional studies
King and Appleton (1997) emphasized that intuition experienced by nurses is
associated with physical sensations, for example mouth feeling dry, skin ¯ushing,
muscles tightening and pulse racing. Thus the process of recognition at an intuitive
level can be detected by the frequency of skin-galvanic (SGR) signal, and then at the
level of completed decision making by a motor or verbal reaction. Bechara et al.
(1997) observed that the subjects began to generate anticipatory skin conductance
responses (SCRs) whenever they pondered a choice that turned out to be risky. The
results suggest that, in normal individuals, nonconscious biases guide behaviour
before conscious knowledge does. Without the help of such biases, overt knowledge
may be insucient to ensure advantageous behaviour.
Dehaene et al. (1999) observed changes in EEG signals between the sensations
reactions and intuition. The results suggest that some characteristics of brain maps
316 K. Gielo-Perczak and W. Karwowski
re¯ect the individual psychological features determined on the basis of Jung's
typology. The series of behavioural and brain-imaging experiments provided
evidence that mathematical intuition emerges from the interplay between the
bilateral areas of the parietal lobes involved in visual-spatial processing (Dehaene et
al. 1999). Support for this came from Albert Einstein, who stated:
Words and language, whether written or spoken, do not seem to play any part in
my thought processes. The psychological entities that serve as building blocks for
my thought are certain signs or images, more or less clear, that I can reproduce
and recombine at will. (Hadamard 1945)
The results of research designed to explore relationships between intuition and
two dimensions of cognitive style, handedness and gender revealed that dominance
and visual-verbal style were consistently related to intuition (Fallik and Eliot 1985).
Recent experiments con®rmed that our brains react more strongly to unpredicted
reward. Using functional magnetic resonance imaging, Berns et al. (2001) recorded
human brain responses when a subject predicted reward. Their results demonstrated
an interesting separation in the brain response to predictability and to subjective
reports of preference. The brain response to preference was exclusively cortical, but
the response to predictability showed speci®c activation of reward systems also
known to be the target of midbrain dopaminergic neurons. Also, this ®nding
suggests that the subjective report of preference may be dissociated from neural
circuits known to be powerful determinants of conditioned behaviours.
5.3. Component of decision-making
Intuition is an internal experience. It is a way of knowing something directly without
an intervening analytic thought. Intuition adds value when creativity and innovation
are necessary. Intuition is the skill of responding appropriately, in the moment, to
speci®c situations.
The essential nature of intuition cannot be ignored in decision-making. In the
current health climate, which demands measurable research-based evidence, the
involvement of intuition as an element of judgment is often diminished. The result is
that many nurses are being forced to be covert in their use of this crucial aspect of
judgment and focus on the rational elements of decision-making. However, research
evidence suggests that intuition occurs in response to knowledge, is a trigger for
action and/or re¯ection. Subjects with highly intuitive self-receptivity, are more open
to receiving information, represent common-sense understanding, skilled know-how,
and risk-taking, have a desire to tune in, and are potentially vulnerable (King and
Appleton 1997). Hansten and Washburn (2000) stressed in their ®ndings increased
emphasis and interest in the development of intuition: from business to healthcare.
Additionally, as reported by Gott (1988) a new generation of Air Force technical
training systems requires technical intuition in system diagnosis or assessing the
`libraries of the mind'. There was a need to consider more detailed sets of hypotheses
and much richer representations of the problem, thus a multiyear and multimillion
dollar Air Force Basic Job Skills Research Program was proposed at the Air Force
Human Resources Laboratory in Texas. However, there are critical opinions of the
intuition approach such as those by Cosmides and Tooby (1994) who propose
theories of adaptive function as an evolutionary theory and solution to cognitive
science. Thus, in our opinion it is crucial to consider intuition as a component of
317
Affordance, emotion and intuition
adaptive function as we propose a neuro-ecological approach to human performance
modelling. Intuition should be enhanced in the workplace, becoming familiar with
some of the ways in which experienced, and facilitating co-workers to express their
intuitions more openly. There is a need to blend a theoretical and tacit knowledge.
6. The neuro-ecological models of human performance
Many system control problems arise from a lack of attention to the interactions
among dierent human system components in relation to aordances of the
environment. Aordances must be considered since they can oer both the positive
(bene®ts) and negative (injury) (Gibson, 1986). In order to predict the ecological
model connectivity there is an emerging need to model the mutual relationships of
aordation, perception, emotion and intuition. These speci®c relationships reveal
three basic types of human ecological control. A model with perception in addition
to cognition and body sensors is considered as a learning type. A model possessing all
distinctions of a learning type plus emotion is considered as an adaptive type,andan
adaptive model type with intuition is considered as a tuning type (®gure 1). The
proposed model types can be represented analytically in terms of control-theoretical
models for quantitative predictions in the various work (environment) behaviours
(activities).
An indication that this classi®cation is relevant was presented by Gibson (1986)
and Nunez and Freeman (2000). According to the authors, traditional cognitive
science is Cartesian in the sense that it takes as fundamental the distinction between
the mental and the physical. The authors propose that minds are not architectural
modular structures that deal in information but are constituted by the dynamic
interactions of perceiver and percept, knower and that which is to be known. Levine
(2000) vastly expanded the concepts of adaptive resonance and associative learning
in human performance.
Picard (2000) pointed out that in the human brain, a critical part of our ability
to see and perceive is not logical, but emotional. Also, Picard (2000) suggested that
if we want computers to interact naturally with us, we must give computers the
ability to recognize, understand, have and express emotions. Davenport and Beck
(2001) created a model for measuring and managing human attention as a most
valued and personal resource. Bateson (2000) was concerned that processes of
knowing were related to perception, communication, coding and translation.
However, he suggested that there is dierentiation of logical levels, including the
relationship between the knower and the known, knowledge looping back as
Figure 1. Types of human performance based on aordance, emotion and intuition.
318 K. Gielo-Perczak and W. Karwowski
knowledge of an expanded self. Generally, these concerns open a space of
possibilities in which the interrelationship between brain and cognitive science,
neuroscience, dynamic systems modelling, control of engineering systems and
human rationality can foster the value of aordation, emotion and intuition in
human performance.
6.1. Learning-based ecological model
The design of a learning-based control model involves the attributes of aordation,
cognition, and body sensors, which collectively determine the control of environment
(i.e. work process). The information pick-up is considered as a single-channel
mechanism (®gure 2). In this model, three phases are considered: 1) conversion of the
input aordance of a work process to sensory output by the body sensors, 2)
perception process, 3) cognition decision and response selection. The coecients
represented by ecological perception and body sensors are proportional to the age
and vision conditions of a worker.
The proposed ecological control model utilizes body sensors' feedback loop
together with the loop which continually adds the perception and cognition
parameters in an attempt to make the decision output as safe as possible. Feedback
control is based on using the outcome of the task in order to control it. It uses the
dierence between the actual output yand the input xin order to reduce it. Let's
consider human response as a learning type. The task corresponds to the control
dynamics of the interactions between the human body and its environment. The
feedback corresponds to the output of body sensors. Perception corresponds to the
nervous system which, with cognition, plays a controller's role. The control problem
is how to act in such a way that it accomplishes the desired task. In the linear case,
we can use the Laplace transform and describe each block with a transfer function.
Let us de®ne the transfer functions of the blocks in ®gure 2 as follow: Afor an
aordance, Tfor the task, Bfor the body sensors, Pfor ecological perception, and C
for cognition. In the Laplace domain, we can write the output as a function of the
input in ®gure 2 in terms of the block's transfer functions (the Laplace variable sis
avoided for simplicity):
x
yPT1C
1BPT1C:1
Figure 2. Learning type of human performance.
319
Affordance, emotion and intuition
The basic aordances of the environment are perceivable directly. One major
advantage of the feedback control is sensitivity to changes in the parameters of the
task. Through ecological perception and cognition, learning of a task takes place. A
learning type must have a particular response to a speci®c input signal by repeating
the instructions. Learning involves a process of forcing a human with precise action
to control (change) the environment, i.e. the work process.
6.2. Adaptive-based ecological model
Adaptation is considered to be a process of modifying the parameters of the system
and the control actions (®gure 3). An adaptive-based ecological control model
continuously searches for the optimum within its allowed environment (work
system) possibilities by an orderly trial-and-error process and can resolve a problem
according to the available information being utilized in the on-line execution of the
control.
An important element of adaptive control is the learning of drifting parameters
of the environment (work process). As the work process unfolds, additional
information becomes available either accidentally, through past control actions, or
as a result of active probing which itself is possible by control action. This can be
done by anticipating how future estimation will be bene®cial to the control objective.
An adaptive ecological model utilizes, in addition to the available information at the
time, the knowledge that future observations will be made and regulates its
adaptation or learning accordingly. The model considers the speci®cations of the
reference parameters of the task, which determine the desired ideal response of the
work process output to the command signal. The model consists of a feedback
control loop with body sensors with an outer loop which continually adjusts the
perception parameters in an attempt to make the decision output the same as the
reference parameters of the task output. The adjustment control capabilities of an
adaptive type make an error of controlling as close to zero as possible.
The transfer functions of the blocks in ®gure 3 are as follow: Afor an aordance,
Efor emotion, Pfor ecological perception, Cfor cognition, Tfor the task, Bfor the
body sensors, R
ef
± task reference parameter. An adaptive process transfer function
(the Laplace variable sis avoided for simplicity) is:
y
xEPT1RefTC
1ÿBEPT ÿEP CEPT 2
Figure 3. Adaptive type of human performance.
320 K. Gielo-Perczak and W. Karwowski
An adaptive-based ecological control model is able to: 1) identify and provide
continuous information about the present state of the workplace (i.e. information
pickup), and 2) make a decision to adapt to the workplace so as to tend toward
optimum control of performance.
6.3. Tuning-based ecological model
The tuning-based model represents a subgroup of adaptive types of ecological
control models; they can easy eectuate and implement complex tasks. They can
control complex processes with a wide variety of work tasks presented by
characteristics involving unknown parameters like time delay, time-varying work
process dynamics and stochastic disturbances. The tuning model can control
unknown tasks using recursively-estimated values of the work parameters. The
analytical control technique of the tuning model consists of three main steps:
identi®cation of a work process, control during each sample interval, and drive itself
toward the control optimum by making a proper modi®cation.
The tuning-based model, therefore, has the ability to tune itself initially and to re-
tune. The tuning system consists of two feedback loops with the body sensors and
intuition. Through the outer loop with intuition, a tuning type continually adjusts
the task parameters (®gure 4). This approach to tuning control is an ambiguous
control process itself. In some tuning controls it is possible to re-parameterize the
work process (i.e. environment) such that it can be expressed in terms of the work-
controlled parameters.
The transfer function for a tuning model with the parallel input/output signals to
intuition and task estimator is too complex to fully describe at this stage. For
simplicity a one-output signal was considered from task estimator, and a one input
and output signal from intuition. The transfer functions of the blocks in ®gure 4 are
as follow: Afor an aordance, Efor emotion, Pfor ecological perception, Ifor
intuition, Cfor cognition, Tfor the task, Bfor the body sensors, R
ef
± task reference
parameter, T
est
- task estimator. A tuning process transfer function (the Laplace
variable sis avoided for simplicity):
y
xEIPT
Iÿ1ÿTÿEPTCI CBI3
Figure 4. Tuning type of human performance.
321
Affordance, emotion and intuition
In terms of Gibson's ecological framework (1986), a tuning-based control model
resonates in response to a range or frequency of information which is more or less
close to its own knowledge. The maximum response occurs only when the range or
frequency of information coincides with its own. The resonance may be more or less
®nely `tuned', occurring when a knowledge of past and present systems are most
closely similar (Sheldrake 1981).
7. Conclusions
Nature has found wonderful solutions to complex problems, and our goal is often to
recognize them. A common procedure to help us understand human systems is to try
to formulate their behaviour mathematically, ®t the parameters of the mathematical
model to the experimental results, and then study the generalization of the model.
This is an interactive procedure that can elucidate the features of the work system,
teach us more about the system, and suggest ways for new experiments. Through
modelling of human performance in the workplace it is possible to test the
knowledge, reliability, and human limitations in order to aid in human control of the
environment. Knowledge of the environment is obtained by looking, along with
listening, feeling, smelling, and testing (Gibson 1986). Recognition that such control
systems exist, is necessary to allow for modelling of the human operators interacting
with their environment in cognitively plausible ways (Klopf et al. 1993).
In this paper, the classical cognitive engineering framework based on the human
skill-, rule-, and knowledge-based performance models as proposed by Rasmussen
(1983) was extended by an neuro-ecological approach, including aordances of the
environment and personal human attributes (emotion and intuition) important in
exercising control over the environment. The proposed aordance-, emotion-, and
intuition-based models correspond to the three types of human performance,
namely: learning, adaptive and tuning control, respectively. The new framework is
not a predictive model of the operator behaviour, but rather it describes the
processes of neuro-ecological control of the human environment.
In the future, modelling of human performance in the work process wi ll be extended
by application of nonlinear systems dynamics, including methods for analysing
stability, sensitivity analysis, and control theory (self-tuning control, adaptive control,
etc). With the aid of cybernetics, ecological psychology and neuroscience, in general,
and the cognitive neuroscience in particular, a neuro-ecological approach to
ergonomics should help to improve our understanding of the complexity of human
control of the environment, and the mysteries of human ecological behaviour.
References
ALLMAN J. M., HAKEEM A., ERWIN J. M., NIMCHINSKY E. and HOF P. 2001, The anterior
cingulate cortex. The Evolution of an interface between emotion and cognition, Annals
of the New York Academy of Sciences,935, 107 ± 117.
Table 1. Types of human performance in relation to a neural processes.
Individual attributes Learning Adapting Tuning
Aordation 444
Emotion 44
Intuition 4
322 K. Gielo-Perczak and W. Karwowski
ASHBY, R. 1965, Design for a Brain: The Origin of Adaptive Behaviour (London: Chapman and
Hall).
BATESON, G. 2000, Steps to an Ecology of Mind (Chicago, IL: The University of Chicago Press).
BECHARA, A., DAMASIO, H., TRANEL, D. and DAMASIO, A. R. 1997, Deciding advantageously
before knowing the advantageous strategy, Science, 275, 1293 ± 1295.
BERNS, G. S., MCCLURE, S. M., PAGNONI, G. and MONTAGUE, P. R. 2001, Predictability
modulates human brain response to reward, The Journal of Neuroscience, 21, 2793 ±
2798.
CAMBEL, A. B. 1993, Applied Chaos Theory (Boston, MA: Academic Press: Boston).
CISEK, P. 1999, Beyond the Computer Metaphor: Behaviour as Interaction, in R. Nunez and
W. J. Freeman (eds) 2000, Reclaiming Cognition: The Primacy of Action, Intention and
Emotion (Bowling Green, OH: Imprint Academic), pp. 125 ± 142.
COSMIDES, L. and TOOBY, J. 1994, Beyond intuition and instinct blindness: toward an
evolutionarily rigorous cognitive science, Cognition, 50, 41 ± 77.
DAMASIO, A.,R. 1994, Descartes' Error: Emotion, Reason, and the Human Brain (New York:
Oxford University Press).
DEHAENE, S., SPELKE, E., PINEL, P., STANESCU, R. and TSIVKIN, S. 1999, Sources of mathematical
thinking: behavioural and brain-imaging evidence, Science, 284, 970 ± 974.
DAVENPORT, T. H. and BECK, J. C. 2001, The Attention Economy: Understanding the New
Currency of Business (Cambridge, MA: Harvard Business School Publishing).
DAVIDSON, P. R., JONES, R. D., SIRISENA,H.R.andANDREAE, J. H., 2000, Detection of adaptive
inverse models in the human motor system, Human Movement Science, 19, 761 ± 795.
EPSTEIN, S. 1994, Integration of the cognitive and the psychodynamic unconscious. American
Psychologist,49, 709 ± 724.
EPSTEIN, S., PACINI, R., DENES-RAJ, V. and HEIER H. 1996, Individual dierences in intuitive-
experiential and analytical-rational thinking styles. Journal of Personality and Social
Psychology,71, 390 ± 405.
FALLIK, B. and ELIOT, J. 1985, Intuition, cognitive style, and hemispheric processing,
Perceptual and Motor Skills, 60, 683 ± 697.
FEIGENBAUM, M. J. 1980, Universal behaviour in nonlinear systems, Los Alamos Science,1, 4.
FLACH, J., HANCOCK, P., CAIRD, J. and VINCENTE, K. (eds) 1995, Global Perspectives on the
Ecology of Human-Machine Systems (Hillsdale, NJ: Lawrence Erlbaum Associates,
Publishers).
GEVINS, A., SMITH, M. E., MCENVOY, L. and YU, D. 1997, High-resolution EEG mapping of
cortical activation related to working memory: diculty, types of processing, and
practice. Cerebral Cortex,7, 374 ± 385
GIBSON, J. J. 1986, The Ecological Approach to Visual Perception (Hillsdale, NJ: Lawrence
Erlbaum Associates, Publishers).
GIBSON, J. J. 1979, The Ecological Approach to Visual Perception (Boston: Houghton Miin).
GIELO-PERCZAK, K. 2001a, The golden section as a harmonizing feature of human dimensions
and workplace design, Theoretical Issues in Ergonomics Science, 2, 335 ± 351.
GIELO-PERCZAK, K. 2001b, Systems approach to slips and falls research, Theoretical Issues in
Ergonomics Science,2, 124 ± 141.
GLEICK, J. 1987, Chaos: Making a New Science (New York: Penguin Books).
GOTT, S. P. 1988, Technical intuition in system diagnosis, or accessing the libraries of the mind,
Aviation, Space, and Environmental Medicine, 59, A59 ± A64.
GOODSTEIN, L. P., ANDERSON, H. B. and OLSEN, S. E. (eds.) 1988, Tasks, Errors and Mental
Models (London: Taylor and Francis).
GROSSBERG, S. and GUTOWSKI, W. 1987, Neural dynamics of decision making under risk:
Aective balance and cognitive-emotional interactions, Psychological Review,94, 300 ±
318.
GRABOWSKI, T. J. and DAMASIO A. R. 1996, Improving functional imaging techniques: The
dream of a single image for a single mental event, Proceedings of the National Academy
of Science of the United States of America, 93, 1402 ± 1403.
GUASTELLO, S. J. 1995, Chaos, Catastrophe, and Human Aairs (Mahweh, NJ: Lawrence
Erlbaum Associates).
HADAMARD, J. 1945, An Essay on the Psychology of Invention in the Mathematical Field
(Princeton, NJ: Princeton University Press).
323
Affordance, emotion and intuition
HANCOCK, P., FLACH, J., CAIRD, J. and VINCENTE, K. (eds) 1995, Local Applications of the
Ecological Approach to Human-machine Systems (Hillsdale, NJ: Lawrence Erlbaum
Associates, Publishers).
HANSTEN, R. and WASHBURN, M. 2000, Intuition in professional practice: executive and sta
perceptions, Journal of Nursing Administration, 30, 185 ± 189.
HARRIS, P. L. 2000, Imagination and emotion, in G. Hatano, N. Okada, and H. Tanabe (eds),
Aective Minds, (Amsterdam: Elsevier Science), pp. 137 ± 145.
HOC, J. M. 2000, From human-machine interaction to human-machine cooperation,
Ergonomics, 43, 833 ± 843.
JOHANNSEN, G.1988, Categories of human operator behaviour in fault behaviour situations. In
L. P. Goodstain, H. B. Andersson, and S. E. Olsen (eds), Tasks, Errors, and Mental
Models (London: Taylor and Francis) pp. 251 ± 277.
KAPLAN, D. and GLASS, L. 1995, Understanding Nonlinear Dynamics (New York: Springer-
Verlag).
KARWOWSKI, W., MAREK, T. and NOWOROL, C. 1988, Theoretical basis of the science of
ergonomics, in Proceedings of the 10th Congress of the International Association, Sydney,
Australia (London: Taylor & Francis), 756 ± 758.
KARWOWSKI, W. 1991, Complexity, fuzziness and ergonomic incompatibility issues in the
control of dynamic work environments, Ergonomics,34, 671 ± 686.
KARWOWSKI, W. 1992, The human world of fuzziness, human entropy, and the need for general
fuzzy systems theory, Journal of Japan Society for Fuzzy Theory and Systems,4, 591 ±
609.
KARWOWSKI, W., MAREK, T. and NOWOROL, C. 1994, The complexity-incompatibility principle
in the science of ergonomics, in F. Aghazadeh (ed), Advances in Industrial Ergonomics &
Safety VI (London: Taylor and Francis), pp. 37 ± 40.
KARWOWSKI, W., GADDIE, P. and MARRAS, W. S. 1994, A dynamical systems approach for
analysis of the relationships between risk factors for low back disorders using the 3-D
graphical visualization models, in F. Aghazadeh, (ed.), Advances in Industrial
Ergonomics &Safety VI, (London: Taylor & Francis), 653 ± 656.
KARWOWSKI, W. 1992, The Human World of Fuzziness, Human Entropy, and the Need for
General Fuzzy Systems Theory, Journal of Japan Society for Fuzzy Theory and Systems,
4, 591 ± 609.
KARWOWSKI, W. 2000, Symvatology: The science of an artifact-human compatibility,
Theoretical Issues in Ergonomics Science, 1, 76 ± 91.
KARWOWSKI, W., GROBELNY, J., YANG, Y. and LEE, W. G. 1999, Applications of fuzzy systems
in human factors, in H. Zimmerman (ed), Handbook of Fuzzy Sets and Possibility
Theory, (Boston, MA: Kluwer Academic Publishers), pp. 589 ± 620.
KARWOWSKI, W., LEE, W. G., JAMALDIN, B., GADDIE, P., JANG, R. and ALQESAIMI, K. K. 1999,
Beyond psychophysics: The need for a cognitive engineering approach to setting limits
in manual lifting tasks, Ergonomics,42, 40 ± 60.
KING, L. and APPLETON, J. V. 1997, Intuition: a critical review of the research and rhetoric,
Journal of Advanced Nursing, 26, 194 ± 202.
KIRSCH, D. 1991, Foundations of AI: the big issues, Arti®cial Intelligence,47, 3 ± 30.
KLOPF, A. H., MORGAN, J. S. and WEAVER, S. E. 1993, A hierarchical network of control
systems that learn: Modelling nervous system function during classical and instrumental
conditioning, Adaptive Behaviour, 1, 263 ± 319.
KOSKO, B. 1992, Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to
Machine Intelligence (Englewood Clis, NJ: Prentice Hall).
LANE, R. D., REIMAN, E. M., AXELROD, B., YUN, L. S., HOLMES A. and SCHWARTZ G. E. 1997,
Neural correlates of levels of emotional awareness: evidence of an interaction between
emotion and attention in the anterior cingulate cortex. Journal of Cognitive
Neuroscience.10, 525 ± 535.
LEVINE, D. S. 2000, Introduction to Neural and Cognitive Modeling, 2nd edn. (Mahwah, NJ:
Lawrence Erlbaum Associates).
LEVINE, D. S. and ELSBERRY, W. R. (eds) 1997, Optimality in Biological and Arti®cial Networks?
(Mahwah, NJ: Lawrence Erlbaum Associates).
LIN, C.-T. and LEE, C. S. G 1996, Neural Fuzzy Systems (Upper Saddle River, NJ: Prentice
Hall).
324 K. Gielo-Perczak and W. Karwowski
LLOYD, S. 2002, Learning How to Control Complex Systems, available online at http://
www.santafe.edu/s®/publications/Bulletins/bulletin-spr95/10control.html.
LUU, P., FLAISCH, T. and TUCKER, D. M. 2000, Medial frontal cortex in action monitoring.
Journal of Neuroscience, 20, 464 ± 46.
MCDOWELL, M. J. 2001, Principle of organization: a dynamic-systems view of the archetype-as-
such, Journal of Analytical Psychology,46, 637 ± 654.
MOFFAT,D.C.andFRIJDA, N. H. 2000, Functional models of emotion, in G. Hatano, N.
Okada, and H. Tanabe (eds.), Aective Minds, (Amsterdam: Elsevier Science), pp. 59 ±
68.
MOSS H. and DAMASIO A. R. 2001, Emotion, cognition, and the human brain. Annals of the
New York Academy of Sciences,935, 98 ± 100.
NILSSON, N. J. 1991, Logic and arti®cial intelligence, Arti®cial Intelligence,47, 31 ± 56.
NUNEZ, R. and FREEMAN, W. J. (eds) 2000, Reclaiming Cognition: The Primacy of Action,
Intention and Emotion (Bowling Green, OH: Imprint Academic).
PARASURAMAN, R., SHERIDAN, T. B. and WICKENS, C. D. 2000, A model for types and levels of
human interaction with automation, IEEE Transactions on Systems, Man, and
Cybernetics ±Part A: Systems and Humans, 30, 286 ± 297.
PARASURAMAN, R. (ed.) 2000, The Attentive Brain (Cambridge, MA: The MIT Press).
PARKS, R. W., LEVINE, D. S. and LONG, D. L. 1998, Fundamentals of Neural Network Modeling
(Cambridge, MA: The MIT Press).
PASQUINI, A., RIZZO, A., SCRIVANI, P., SUJAN, M. A. and WIMMER, M. 2000, Human, hardware
and software components in control system speci®cation, in Opdahl, A. L., Pohl, K. and
Rossi, M. (eds.), Proceedings of the Sixth International Workshop on Requirements
Engineering: Foundation for Software Quality, (Essener Informatik, Beitrege: Stock-
holm), pp. 31 ± 40.
PICARD, R. W. 2000, Aective Computing (Cambridge, MA: The MIT Press).
POON, C.-S. 1994, Hebbian synaptic plasticity: A neural mechanism of supervised learning and
adaptive control, in B. W. Patterson (ed.), Modelling and Control in Biomedical Systems:
Proceedings of the International Federation of Automatic Control Symposium,
(Galveston, TX: International Federation of Automatic Control - IFAC), pp. 517 ± 520.
POSNER, M. I. and ROTHBART, M. K. 1998. Attention, self-regulation and consciousness.
Philosophical Transactions of the Royal Society of London: Biological Sciences, 353,
1915 ± 1927
POWERS, W. 1973, Behaviour: The Control of Perception (New York: Aldine Publishing
Company).
REASON, J. 1997, Managing the risks of organizational accidents (Aldershot: Brook®eld, Vt.,
Ashgate).
RASMUSSEN, J. 1983, Skills, rules, and knowledge; signals, signs, and symbols, and other
distinctions in human performance models, IEEE Transactions on Systems, Man, and
Cybernetics ±Part C: Applications and Reviews, SMC-13, 257 ± 266.
RASMUSSEN, J. 1986, A catalogue of models, in Information Processing and Human-Machine
Interaction: An Approach to Cognitive Engineering (New York: North-Holland),
pp. 171 ± 183.
SALVENDY, G. (ed.) 1997, Handbook of Human Factors &Ergonomics (New York: John Wiley).
SHELDRAKE, R. 1981, A New Science of Life: The Hypothesis of Formative Causation (Los
Angeles: Jeremy P. Tarcher, Inc.).
SHERIDAN, T. B. 1992, Telerobotics, Automation, and Human Supervisory Control, (Cambridge,
MA: The MIT Press).
SMITHSON, M. 1982, Applications of Fuzzy Set Concepts to Behavioural Sciences,
Mathematical Social Sciences, 2, 257 ± 274.
SNOOK, S. H. 1985, Psychophysical acceptability as a constraint in manual working capacity,
Ergonomics, 28, 331 ± 335.
STASSEN, H. G. 1997, A concluding perspective on human-machine systems, in T. B. Sheridan
and T. van Lunteren (eds.), Perspectives on the Human Controller: Essays in Honor of
Henk G. Stasse, (Mahwah, NJ: Lawrence Erlbaum Associates), pp. 301 ± 311.
STEVENS, S. S. 1957, On the psychophysical law, Psychological Review,64, 153 ± 181.
325
Affordance, emotion and intuition
STUSS, D. T. and ALEXANDER, M. P. 2000, The anatomical basis of aective behaviour, emotion
and self-awareness: a speci®c role of the right frontal lobe, in G. Hatano, N. Okada and
H. Tanabe (eds), Aective Minds (Amsterdam: Elsevier Science), pp. 13 ± 25.
SVEDUNG, I. and RASMUSSEN, J. 2002, Graphic representation of accident scenarios: mapping
system structure and the causation of accidents, Safety Science, 40, 397 ± 417.
TURVEY, M. T. and SHAW, R. E. 1995, Toward an ecological physics and physical psychology,
in R. Solso and D. Massaro (eds), The Science of the Mind: 2001 and Beyond. (Oxford:
Oxford University Press) pp. 144 ± 169.
TURVEY, M. T. and SHAW, R. E. 1999, Ecological foundations of cognition, in R. Nunez and
W. J. Freeman (eds), Reclaiming Cognition: The Primacy of Action, Intention and
Emotion (Bowling Green, OH: Imprint Academic), pp. 95 ± 110.
WARREN JR., W. H. 1984, Perceiving aordances: visual guidance of stair climbing, Journal of
Experimental Psychology: Human Perception and Performance, 10, 683 ± 703.
WARREN JR., W. H. and WHANG, S. 1987, Visual guidance of walking through apertures: body-
scaled information for aordances, Journal of Experimental Psychology: Human
Perception and Performance, 13, 371 ± 383.
WICKENS, C. D. 1987, Information Processing, Decision-Making, and Cognition, in G.
Salvendy (ed), Handbook of Human Factors (New York: John Wiley & Sons), pp. 72 ±
107.
WICKENS, C. D. and CARSWELL, M. 1997, Information Processing, in G. Salvendy (ed),
Handbook of Human Factors &Ergonomics (New York: John Wiley), pp. 89 ± 112.
YOUNG, A. M., 1976, The Geometry of Meaning. (Mill Valley, CA: Robert Briggs Associates).
ZADEH, L. A. 1973, Outline of a New Approach to the Analysis of Complex Systems and
Decision Processes, IEEE Transactions on Systems, Man and Cybernetics,SMC-3, 28 ±
44
326 K. Gielo-Perczak and W. Karwowski
- CitationsCitations2
- ReferencesReferences13
People who read this publication also read
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.
This publication is from a journal that may support self archiving.
Learn more