Table of Contents

Cover

Contents

Title Page

Dedication

Preface

Part I: Historical Perspective

Chapter 1: Natural and Engineered Systems
1. Purposeful Design
2. User-Centered Design
3. Design against Failure
4. Summary
5. References
Chapter 2: Historical Roots
1. Engineering for Physical Limitations
2. Engineering for Human Cognition
3. The Modern Era
4. A Fractured Field
5. Summary
6. References
Chapter 3: The Current Practice
1. Aerospace
2. Medicine
3. Automotive Industry
4. Computer Industry
5. Summary
6. References

Part II: The Environment

Chapter 4: The Varied Nature of Environments
1. Static vs. Dynamic Domains
2. Sources of Difficulty in Static Environments
3. Sources of Difficulty in Dynamic Environments
4. Internal vs. External Pacing
5. Error Tolerance
6. Summary
7. References
Chapter 5: The Social Context
1. Methodological Consequences of Group Size
2. Communication and Coordination Consequences of Group Size
3. Summary
4. References
Chapter 6: Analysis Techniques
1. Modeling Static Environments: Finite State Representations
2. Modeling Dynamic Environments
3. Control Theory
4. Measuring Complexity Using Information Theory
5. Modeling Throughput Using Queuing Theory
6. Summary
7. References

Part III: The Human Element

Chapter 7: Determinants of Human Behavior
1. The Human Factor
2. Structure and Content
3. Levels of Analysis
4. Summary
5. References
Chapter 8: The Structure of Human Information Processing
1. Processing Stages
2. Cognition and Action
3. Cognition and Goal-Directed Behavior
4. Response Selection
5. The Nature of Capacity Limitations
6. Summary
7. References
Chapter 9: Acquiring Information
1. Sensory Processing
2. Attention
3. Summary
4. References
Chapter 10: Central Processing Limitations on Multitasking
1. Bottleneck Theories
2. Central Bottleneck Theory and Human-Computer Interaction
3. Capacity Theories
4. Multiple Resource Theory
5. Applications of Single-Channel and Multiple Resource Theories
6. Timesharing
7. Timesharing Strategies and the Control of Processing
8. Summary
9. References
Chapter 11: Memory
1. Types of Memories
2. Retaining and Forgetting Information
3. Retrieving Information
4. Summary
5. References
Chapter 12: Decision Making
1. Anatomy of a Decision
2. Normative Approaches to Decision Making
3. Nonoptimality of Human Decisions
4. Cognitive Approaches to Decision Making
5. Heuristics in Human Decisions
6. Other Influences on Decision Making
7. Process Models of Human Decision Making
8. Naturalistic Decision Making
9. Relationship between Decision-Making Models and Systems Engineering
10. Summary
11. References

Part IV: Human-System Integration

Chapter 13: A Case Study in Human-System Performance: The Exxon Valdez
1. An Account of the Grounding of the Tankship Exxon Valdez
2. The Nature of the Error
3. Summary
4. References
Chapter 14: Human Error
1. Human Error and System Error
2. The Nature of Human Error
3. Theories of Human Error
4. Situation Awareness
5. Summary
6. References
Chapter 15: Contextual Factors Affecting Human-System Performance
1. Workload
2. Interruption
3. Operator State
4. Summary
5. References
Chapter 16: The Role of Automation in Human-System Performance
1. Using Automated Devices
2. Levels of Automation
3. A Taxonomy of Automation Levels
4. Automation as a Decision Support Aid
5. Automation and System Safety
6. Summary
7. References
Chapter 17: Supporting Human-System Performance
1. Alarms and Alerts
2. Information Displays
3. Create Barriers
4. Summary
5. References

Index

End User License Agreement

List of Illustrations

Chapter 2: Historical Roots

Figure 2.1 Early stone tools were adapted to fit the hand.
Figure 2.2 Woodwind instruments.
Figure 2.3 Chinese character types.
Figure 2.4 Simplified control loop for landing an aircraft.

Chapter 4: The Varied Nature of Environments

Figure 4.1 Schematic depiction of factors that affect human performance in complex environments. The importance is indicated by the level in the hierarchy (width), starting with the characteristics of the physical environment and ending with practitioner characteristics.
Figure 4.2 Examples of different control dynamics. (a) Linear dynamics: outputs proportional to inputs for levels of input. (b) Nonlinear negative dynamics: change in output decreases for same change in inputs at high input levels (characteristic of negative feedback). (c) Nonlinear positive dynamics: change in output increases for same change in inputs at high input levels (characteristic of positive feedback). (d) Nonlinear catastrophic dynamics: output stable but changes abruptly at some level of continuous input. (e) Nonlinear chaotic dynamics: output unpredictable with small changes in input.
Figure 4.3 Nonlinear dynamics of aircraft control surfaces. Diagram (a) shows the accelerated airflow over a curved wing that generates lift. Other forces also act on the aircraft, as indicated in diagram (b). The pilot controls the aircraft by altering lift on the wings and other control surfaces (e.g., rudder). Controlling the aircraft is thus a matter of adjusting the pressure differentials to achieve the correct forces, a process that is inherently nonlinear.
Figure 4.4 Nonlinear dynamics can often be transformed to nearly linear input-output relationships. Graph (a) shows the nonlinear input-output relationship defined by the equation of y = x³ + 4x². Graph (b) shows the linear relationship obtained when the logarithm of the input is used to determine the logarithm of the output.
Figure 4.5 Differences in control order. The task is to use a joystick (pictured) to move a “ball” from the bottom to the top of the screen. The user makes a control input and observes a change in position of the ball over time. Zero-order (position) control: position of the ball is determined by position of the joystick. First-order (velocity) control: ball moves at constant speed determined by joystick position. Velocity must be returned to zero to stop the ball. Second-order (acceleration) control: ball moves first with increasing speed, then with decreasing speed. Both acceleration and velocity must be cancelled for the ball to come to rest.

Chapter 5: The Social Context

Figure 5.1 The size of the social network affects system response time as well as the time needed to conduct empirical tests of system functioning.
Figure 5.2 Methods of inquiry change as the scale of operations increases, becoming more qualitative, more subjective, and more focused on the system than the individual.
Figure 5.3 A hierarchical organization of sales representatives
Figure 5.4 The same sales force depicted in Figure 5.3 with lateral communication and authority linkages

Chapter 6: Analysis Techniques

Figure 6.1 A simple finite-state model of searching through a list of linked Web pages, such as a menu or the World Wide Web. Each link is examined in turn to determine if it is close enough to the desired target to explore further. If so, it is selected. If not, the next link is selected and the process repeats. If no more links remain, the user is assumed to press the Back button and select a new link from the “parent” page.
Figure 6.2 Simplified control loop for landing an aircraft. K = gain, ∫ = integration. Monitored state variables include airspeed, descent rate, altitude, and heading. The control agent used feedback of error from desired state to select an action. Evaluation of error should include lags in system response, and actions should include lags in internal response.
Figure 6.3 Illustration of the effect of activation strength, noise and signal distributions, and criterion on the decision to say that a target stimulus was present or absent. The abscissa plots the activation strength as a random variable. The ordinate plots the probability of a specific activation strength (on the abscissa) given the noise and signal distributions. The black region represents False Alarms (saying that a target was present when there was only noise). The gray region represents Misses (saying a target was not present when it was). When the distributions overlap, as they often do at threshold, the placement of the criterion determines the proportion of False Alarms and Misses.
Figure 6.4 Illustration of an easy discrimination in signal detection theory. Overlap in signal and noise distributions is minimal, as the means are far apart relative to their variances. Note that placing the criterion (β) midway between the means of the two distributions minimizes joint error (False Alarms and Misses). Moving the criterion to further reduce Misses, as shown in graph (b), produces a large increase in False Alarms for a small reduction in Misses.
Figure 6.5 Elements of queuing theory. Events arrive to a processor (server) randomly with some density. The server takes a specified time to process each item. Which item is chosen to process next is set by a policy. The number of items in a queue, and their average wait time, can be calculated from the arrival rate, processing time, and queue policy.

Chapter 7: Determinants of Human Behavior

Figure 7.1 Behavior results from the interaction of the user, her goals, and the context (world) in which she is immersed.
Figure 7.2 Depiction of information flow in terms of system control. The state of the controlled system is sensed and assessed against the goals of the context. This leads to a plan of action, the consequences of which are predicted. If the prediction conforms to the desired behavior, the action is taken; if not, the plan is further assessed and refined.

Chapter 8: The Structure of Human Information Processing

Figure 8.1 Flow of human information processing from sensory input to motor output. Each arrow represents a notional unit of information. Selective attention limits the amount of perceptual information input at any given time to cognition. A major function of cognitive processing is action selection, though that term should not be interpreted as a complete description of cognitive activity.
Figure 8.2 Example of goal-directed behavior in a choice response time task. Time flows downward. Task goals must be accessed to determine which of several responses to make.
Figure 8.3 Eye tracking patterns for the picture in the upper left vary dramatically depending on the information the viewer is asked to glean.
Figure 8.4 A notional graph depicting a hypothetical relationship between the number of pitches in a pitcher’s repertoire and the time it will take a batter to select a particular swing. Solid line shows the linear relationship between response time (Swing Selection Time) and entropy (H) when all pitches are equally likely. The dashed line shows predicted swing selection time when the pitch can be predicted 60 percent of the time. The dotted line shows the estimated amount of time available to select the swing for a successful hit.
Figure 8.5 Diagram (a) shows the layout of the burners on the stove top of one of the authors (Remington) with the control knobs arranged vertically on the right. Numbers in parentheses indicate the burner controlled by each knob. Although the correspondence is regular, there is no perceptual cue to indicate the spatial correspondence. The layout in diagram (b) arranges the knobs in spatial correspondence to the burners. Note how it is no longer necessary to indicate which knob controls which burner.

Chapter 9: Acquiring Information

Figure 9.1 The visual system is sensitive to only a small fraction of the electromagnetic spectrum, consisting of the wavelengths of light between about 400–700 nm.
Figure 9.2 Illumination. Diagram (a) shows a case of pure specular reflection, in which the light is reflected back at the same angle at which it struck without scatter. The angle of incidence (θ) is the same as the angle of reflection. Diagram (b) shows how light at different wavelengths is absorbed or reflected. Blue (B), green (G), yellow (Y), and red (R) wavelengths strike a surface that reflects only wavelengths in the red portion of the spectrum. The surface will appear red under normal sunlight, as only red wavelengths will reach the eye.
Figure 9.3 The power of sunlight is concentrated in and distributed relatively uniformly across the visible spectrum (Taylor & Kerr, 1941). Graph (a) shows the relative percentage of the spectral power of sunlight in the visible spectrum. Graph (b) shows the relative percentage of power in a cool white fluorescent lamp. Units are in percentage of total energy. The vertical dotted lines represent regions corresponding to the wavelengths of the specified colors.
Figure 9.4 The basic components of the eye. Light rays are bent by the cornea, and then enter the eye through the pupil, whose diameter is determined by muscles controlling the iris. The lens produces a focused image on the back of the retina, where photoreceptors transform the light into neural activity which is then processed through layers of neurons and passed on to the visual areas of the brain by way of the optic nerve.
Figure 9.5 The Snellen Eye Chart is commonly used for assessing visual acuity.
Figure 9.6 Diagram (a) shows a high-contrast, low-frequency square wave grating. Diagram (b) shows a low-contrast, high-frequency square wave grating. Corresponding amplitude profiles are shown below each grating. Square wave gratings are constructed by summing the odd harmonics of a sine wave grating.
Figure 9.7 Computation of visual angle. S denotes the size of the object, D the distance of the object from the lens, Y the distance from the lens to the retina, and V the angle subtended by S. R is the image of S projected on the retina.
Figure 9.8a Contrast sensitivity as a function of spatial frequency in cycles per degree of visual angle (cpd). Contrast sensitivity is the inverse of threshold. The higher the contrast sensitivity, the lower the contrast needed for threshold perception of a luminance difference. This panel shows the effect of differences in mean field luminance on a 525-nm grating. Sensitivity increases with increasing luminance.
Figure 9.8b The effect of retinal eccentricity. Sensitivity increases nearer the fovea. Contrast sensitivity also varies as a function of grating type (square wave, sinusoid) and flicker rate.
Figure 9.9 A demonstration of the effect of luminance contrast on reading. The top bar is a grey-scale image showing the luminance contrast produced by yellow text on a white background. It reads, “White text on a Yellow background is bad because both have high luminance, reducing the luminance contrast.” The middle bar is a similar grey-scale image that reads, “Blue on Black is bad as both are low in luminance and reduce contrast.” The bottom bar reads, “What about Red on Green?” Red on green is difficult because of the way color is processed.
Figure 9.10 An Anstis Array. All letters should be equally visible while fixating on the center dot. The Anstis Array demonstrates that equating the cortical representation of objects compensates for decreases in acuity with retinal eccentricity.
Figure 9.11 The adaptation function, measured as the contrast threshold (left vertical axis) as a function of time in the dark. Rod threshold is greater than cone threshold when light-adapted. Cones adapt more rapidly to the dark than do rods, but sensitivity levels off early.
Figure 9.12 Examples of visual crowding. In each letter grouping, try to read the single letter, or the letter in the middle, while maintaining fixation on the small black dot. Note how flanking letters increase difficulty in the periphery, but not near the fovea.
Figure 9.13 Spectral sensitivity functions for three cone types and rods. The peak of the L cone is the primary cone type for responses to red (R). The M cones correspond roughly to green (G), and the S cones are specialized for the short wavelengths of blue (B). The rod response is also shown. Values on the y-axis reflect the relative sensitivity with respect to the peak sensitivity, not the absolute sensitivity.
Figure 9.14 A simplified version of color and luminance processing. Activation is summed in the luminance channel. Red (R) and green (G) cones provide most of the activation. R and G are summed to get yellow (Y). Color difference channels compute the difference of R and G separately from the difference between R and Y (R + G).
Figure 9.15 CIE XY chromaticity diagram. The central circle represents the point of achromatic white, which equates to equal amounts of all three primaries (X = Y = 1/3). The triangle shows the colors possible given three common primaries for color television.
Figure 9.16 The relationship of airflow and pressure inside an open tube (flute) and a tube open only at one end (clarinet). Sound is reflected from the ends of the tube. At the open end the pressure is equivalent to the external pressure, but not at the closed end.
Figure 9.17 Four different kinds of waveforms. The square, triangle, and sawtooth waves can all be constructed by a weighted sum of the harmonics of sine waves. For example, both the square wave and the triangle wave contain only the odd harmonics of the fundamental sine wave. For the sine wave we note its amplitude (A), and wavelength (λ).
Figure 9.18 The structures of the ear.
Figure 9.19 Equal loudness contours. Each line reflects the sound pressure level at the tested frequency (x-axis) required to match the perceived loudness of a 1000-Hz tone at a specified loudness (phon). The ear is most sensitive in the 2–5 kHz range.
Figure 9.20 Approximate bandwidth (equivalent rectangular bandwidth) of the critical band as a function of frequency. The plot illustrates how the bandwidth increases with frequency. Critical bands describe the frequency tuning in the auditory system and determine the amount of interference (masking) that will occur if two tones are presented.
Figure 9.21 The effect of masking noise raising the threshold of detection of a target sound.
Figure 9.22 The interaural time difference. The sound arrives at the observer’s right ear at T_n, at the left ear at T_n+D. In this case D = 2l. The head also attenuates the sound, especially at high frequencies, creating a sound shadow.
Figure 9.23 Spatial attention. A cue is presented pointing left or right to indicate the probable location of the target. The arrow cue is 80 percent Valid, 20 percent Invalid. The task is simply to press a key whenever either box is illuminated. Graph (b) compared reaction times for Valid and Invalid cues with those of a Neutral cue. Compared to a Neutral cue, Valid cues facilitate reaction time, Invalid cues inhibit reaction time.
Figure 9.24 Feature and conjunction feature search. In diagram (a), targets are a black X or black T. Search is among white Xs and gray Ts. Targets are easy to find regardless of distractors, and set size has little effect. In diagram (b), targets are either a white T or a black X. Search is among black Ts and white Xs, so the target is the conjunction of color and shape. As the set size gets larger, search becomes increasingly difficult.
Figure 9.25 Typical search times for feature and conjunction search as a function of set size. Lines represent best fitting linear functions. Slope of “Target Absent” trials in conjunction search is twice that of “Target Present” trials, consistent with an item-by-item search that terminates when the target is found.
Figure 9.26 A model of attention allocation in visual search. The features of stimuli are coded in separate feature maps. Activity at each stimulus location in each feature map is weighted according to task relevance and is then combined to form an activation map. Attention can be allocated to locations and objects in order of these activation values, or by selecting a specific feature map.
Figure 9.27 Capture by abrupt-onset characters. The fixation display consisted of placeholders that indicated where characters would appear. In addition, an abrupt-onset character was presented at a location not indicated in the fixation display. The abrupt onset could be either a target or a distractor. The right panel shows that when the abrupt onset was the target, set size had no effect on search times, consistent with the abrupt onset having captured attention.
Figure 9.28 Two of the four conditions from Folk, Remington, and Johnston (1992). Each of four groups was presented with a single pairing of cue type (color, onset) and target type (color, onset). Diagram (a) shows the sequence of events for the Onset Cue/Color Target group and the Color Cue/Onset Target group. On any trial, cues could be Valid (same location as target) or Invalid (different location). Diagram (b) shows that cue validity had an effect only when the cues and targets were of the same type: Onset Cue/Onset Target and Color Cue/Color Target. When participants were looking for color, onsets would not capture attention, nor would color capture attention when they were looking for an abrupt-onset target.

Chapter 10: Central Processing Limitations on Multitasking

Figure 10.1 The two tasks of Figure 8.2 presented as Task 1 (T1) and Task 2 (T2) in the Psychological Refractory Period paradigm. Onset of the T2 stimulus (S2) occurs at varying times after onset of the T1 stimulus (S1), referred to as the stimulus onset asychrony (SOA). R1 and R2 refer to the responses to S1 and S2, respectively.
Figure 10.2 Central Bottleneck Theory account of delays in RT2 in the PRP paradigm. At short SOAs, the mental processing of T1 and T2 overlaps, creating competition for the response-selection stage (RS). This competition causes postponement of RS on T2, resulting in elevated RT2. The dotted line in graph (b) plots predicted results from Central Bottleneck Theory based on response times at the shortest and longest SOAs.
Figure 10.3 Stimuli and responses for dual-task driving study (Levy, Pashler, & Boer, 2006). An auditory tone was presented at varying times (SOA) prior to the illumination of brake lights of the leading car. Participants made a vocal response to the auditory stimulus. In the single-foot driving condition, the participant lifted the foot off the gas pedal and depressed the brake with the same foot as quickly as possible.
Figure 10.4 Results of the single-foot driving condition dual-task driving study (Levy, Pashler, & Boer, 2006). Brake response times (BRT) show PRP elevation similar to less familiar manual responses in laboratory. Movement time is constant across SOAs. The effect of SOA on BRT is due primarily to the time needed for participants to remove the foot from the gas pedal.
Figure 10.5 The resources of the Model Human Processor and their connections.
Figure 10.6 Performance-resource functions for two tasks. Task A performance peaks with 60% allocation of resources, Task B with 10%. Performance on A is resource limited up to 60%, after which it is data limited. B shows strong data limits throughout.
Figure 10.7 Performance-resource functions for tasks of varying difficulty. As task difficulty increases, more resources must be allocated to achieve the same level of performance. Task A is easy, reaching peak performance with about 40% allocation. Task B is more difficult, reaching peak performance only with full resource allocation. Task C is very difficult, with no performance improvements until virtually full allocation.
Figure 10.8 Dual-task trade-offs as depicted by the resource-performance functions. As resources are devoted to T1 (solid line), T2 performance (dashed line) declines. In graph (a), T2 is relatively easy. When performance on T1 reaches 90%, the remaining resources (shaded area) are sufficient to perform T2 at around 85%. In graph (b), T2 is more difficult. T2 performance is less than 40% when T1 is at 90%.
Figure 10.9 Resources and associated processing modules in Multiple Resource Theory. There are four resource dimensions associated with (1) Stages, (2) Modalities, (3) Codes, and (4) Responses. Each enclosed module has its own resources. The perceptual and response stages also have resources that limit independent internal processing. Auditory and visual outputs can lead to both verbal and spatial codes. Verbal codes are associated with vocal output, spatial codes with manual output.
Figure 10.10 The effect of the number of intervening items on the response time to a task switch. Here the x-axis is in logarithmic coordinates.
Figure 10.11 Speed-accuracy trade-off. The shaded region extending from 0–C indicates the region in which speed and accuracy show significant trade-off.

Chapter 11: Memory

Figure 11.1 Components of the modal model of memory and their connections.
Figure 11.2 Forgetting curve.
Figure 11.3 Working memory system.
Figure 11.4 Ebbinghaus forgetting function.
Figure 11.5 Interference effects of intervening items on short-term memory performance.
Figure 11.6 Release from Proactive Interference.
Figure 11.7 Proactive Interference and forgetting as a function of number of previous lists learned.
Figure 11.8 Response time in retrieval from short-term memory.
Figure 11.9 Hierarchical network model of semantic memory. Activation of one node can spread to other nodes increasing activation for related items.
Figure 11.10 Sentence verification task results. The time to respond to TRUE questions increases with distance in the hierarchy, as measured by the number of nodes in between the exemplar and category label.
Figure 11.11 Demonstration of the encoding specificity principle. Recall is better in the same environment as learning.

Chapter 12: Decision Making

Figure 12.1 Framework for decision making.
Figure 12.2 Computation of utility and the expected value. In this example, you wager $1 on the roll of two dice and win $1.75 every time an even number greater than 4 occurs. The expected value is 0, which means that over the long run you expect to break even, neither winning nor losing. E < 0 means you expect to lose in the long run, E > 0 means you expect to win.
Figure 12.3 Estimated and true rates of death from various causes. Pattern illustrates the Availability heuristic, according to which people estimate the frequency of occurrence (base rate) by how accessible their memories are for the last incident. High estimated rates for low-occurrence events are influenced by news reports.
Figure 12.4 Payoff matrix for the Prisoner’s Dilemma. Each cell represents the utility of the strategy for both participants (Suspect A, Suspect B). Staying silent yields the best outcome only if the other stays silent as well. The tendency is to take the suboptimal alternative of testifying, as it yields the minimax solution.
Figure 12.5 A cognitive model of decision making illustrating stages and mental operations in arriving at a decision.
Figure 12.6 The process of selecting appropriate action according to Recognition-Primed Decision-Making (RPDM).
Figure 12.7 Example of recognition and pattern matching as the basis for decision making. The possible solutions are heavily influenced by the decider’s perceptual structure of the world and experience with similar situations.

Chapter 13: A Case Study in Human-System Performance: The Exxon Valdez

Figure 13.1 The geography of Prince William Sound and track of the Exxon Valdez leading up to the grounding on Bligh Reef.
Figure 13.2 Contextual Control Model. Multitasking difficulty is a function of the number of concurrently active goals the operator must satisfy and the time interval within which they must be done. As multitasking difficulty increases, control becomes less proactive, less responsive to future system states, and more reactive, increasingly focused on managing immediate demands.

Chapter 14: Human Error

Figure 14.1 Overlap in situation awareness for different subsystems of the shuttle Launch Control Center. The NASA Test Director coordinates all subsystems, but does not maintain complete situation awareness of the state of each system.

Chapter 15: Contextual Factors Affecting Human-System Performance

Figure 15.1 Simplified activity network for moving text showing how state networks can represent options. Boxes correspond to states in the process, arrows to operators that transition between states. Dotted arrows represent keyboard inputs, solid arrows mouse actions. The “Move + Click + Drag” represents mouse actions in selecting from a menu.
Figure 15.2 Analysis of cut-and-paste text editing using a mouse and menu. Example uses the Keystroke-Level Model (KLM) from Card, Moran, and Newell (1983).
Figure 15.3 Questions used by the NASA Task Load Index (NASA-TLX).

Chapter 17: Supporting Human-System Performance

Figure 17.1 Emergent properties in human vision. Each “gauge” has a zero point and a normal operating range, indicated by the shaded portion.
Figure 17.2 Conflict detection times as a function of the size of the angle formed by a conflict pair and the density (number) of aircraft in a sector. In graph (a), data are plotted as a column bar graph; in graph (b) the same data are plotted as connected lines. In graph (a), attention is drawn to the comparison at each density level; in graph (b) the lines draw attention to the relationship of each angle to the density.
Figure 17.3 Example of (a) standard inside-out attitude display showing a 30° bank to the right and (b) an outside-in display of the same bank angle. Diagrams (c) and (d) show how this would be represented on a frequency-separated display. The representation in (c) shows the initial response to the bank command. The aircraft symbol rotates as it would in the outside-in display in (b) as the horizon begins to rotate toward its final position. In (d) the aircraft symbol has rotated back to its horizontal orientation while the horizon line now reflects the inside-out orientation of (a). By “quickening” the display with the initial rotation of the aircraft, the pilot gets immediate feedback as to the control action and predictive information about the future state of the aircraft.
Figure 17.4 Data from Figure 17.2 plotted as a stacked bar graph. Note how the relationships in each stack can be judged roughly against one another by inspecting the size of the rectangles. Exact comparisons require estimation of starting and ending points. Comparisons of the middle and end components of different stacks require adjustment based on the starting points, which are a function of earlier stack elements.
Figure 17.5 Frame effects in graph perception. The apparent slope of the line from A to B and B to C looks larger inside a tall narrow frame than a wider frame, and smallest inside the short and wide rectangle.
Figure 17.6 Swiss cheese model showing how human error leads to system error. Barriers prevent latent failures that promote human error at a given layer from leading to system-wide error. Only a few errors that manage to interact with failures in all barriers will lead to system error.

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Library of Congress Cataloging-in-Publication Data:

Remington, Roger W., 1947-

Introduction to humans in engineered systems / Roger Remington, Charles L. Folk, Deborah Boehm-Davis.

pages cm

Includes index.

Includes bibliographical references.

ISBN 978-0-470-54875-2 (hardback); ISBN 978-1-118-32995-5 (ebk); ISBN 978-1-118-33222-1 (ebk);

ISBN 978-1-118-33271-9 (ebk); ISBN 978-1-118-39373-4 (ebk); ISBN 978-1-118-39375-8 (ebk);

ISBN 978-1-118-39376-5 (ebk); ISBN 978-1-118-50762-9 (ebk)

1. Human engineering. I. Folk, Charles L. II. Boehm-Davis, Deborah Ann. III. Title.

T59.7.R46 2013

620.8’2—dc23

2012026243

Preface

Courses on human factors, human-system integration, engineering psychology, human-computer interaction, or applied psychology, though varying in specific content or approach, all share a common concern with the human as part of a system built by humans. The title of this book—Introduction to Humans in Engineered Systems—reflects that common link. Our core idea was to develop a program for the study of human-system integration based on the combination of a concept-oriented text with a flexible, interactive website. The book is designed to introduce major concepts and principles common across the various disciplines. As an integrating factor, the material is organized around the flow of information in control theoretic diagrams. A high-level treatment of control theory is a powerful way to link the various system elements, including the human, and to guide the analysis of real-world situations. The website (http://www.wiley.com/go/remington) provides a resource for pursuing topics in more depth. The website is conceived as a collection of exercises complete with the necessary programs to demonstrate concepts, case studies that provide a foundation for discussion, links to interesting demonstrations online, and material on topics not covered in detail in the text.

One of the underlying principles of control theory is that the behavior of human operators cannot be fully understood in terms of just mental and physical capabilities. It is necessary also to understand the goals the operator attempts to attain, the system being controlled (aircraft, car, computer), and the influence of the environment in which the system is embedded (including other people). The organization of the text reflects this focus on the human in context by treating four broad thematic areas.

Historical Perspective. This section is designed to prepare the reader for the material in later chapters by providing a fundamental understanding of the human as a component of a system. The concept of human-system integration is introduced with emphasis on systems-level thinking. A brief history chronicles the key role that usability has played in technological progress throughout human history, and documents how the increasing complexity of machinery and manufacturing has given rise to the modern study of human-system integration. Related disciplines (e.g., organizational psychology, engineering psychology) are discussed in terms of how they overlap with, or are different from, human-system integration.

The Environment. This goal of this section is to build awareness of the range of challenges posed by environments that characterize home and work. The key concepts introduced are adaptability and complexity. Because people are adaptable, the demands and incentives of the environment itself are strong determinants of behavior. Reliance on adaptability is seen in management approaches that emphasize a rule-governed, procedural, or incentive-based environment. Limits on adaptability are introduced through a discussion of environmental complexity and its role in human-system performance. Comparisons of fields such as medicine, transportation, and human-computer interaction provide examples of how different environments place different demands on human performance.

This section also introduces the kinds of quantitative techniques that characterize modern human-system analysis. This introduction will familiarize students with task analysis techniques, information theory, finite-state analysis, and signal-detection theory; and provide a brief introduction to human-system modeling. The key organizing concept introduced here, and used throughout the book, is control theory. Control theory is treated at a conceptual level to provide a framework for representing the flow of information in a way that highlights the interaction of all the components of the system. We introduce noise as a real factor in performance, and emphasize the contribution of feedback and lag as issues in human usability. Thus, this section is designed to provide the concepts and knowledge necessary to recognize the potential for user-related issues.

The Human Element. In the first two sections, the human is treated as an adaptable component of the entire system. This section introduces the student to the limits on that adaptability by characterizing human capabilities and limitations in information processing. The control theory framework is again used to represent the flow from perception to situation understanding, from situation understanding to action, and from action back to perception. The key points are not just that people have limited processing capacity, but that we are limited in particular ways which have implications when humans occupy decision-making roles in complex systems. Although all of the many aspects of human behavior are potentially relevant to human-system performance, this section focuses on key characteristics that strongly shape behavior in human-system interactions. To aid students in understanding the range of behavior, we distinguish the characteristics of human behavior associated with the structural properties of the human information-processing system (i.e., the visual and auditory sensory systems, the role of attention in mediating perception, and limits on multitasking) from those associated with the contents of the information-processing system (i.e., memory storage/retrieval and decision making/action selection). Structural factors in general determine the limits on how much information can be processed, whereas content factors determine how that information is used. We emphasize that this distinction is somewhat artificial, in that behavior is ultimately the joint product of these two. Nonetheless, it can be helpful to students in making sense of the large body of literature on human behavior.

Human-System Integration. Up to this point, students have been presented with a broad understanding of the discipline, knowledge of techniques for inquiring into system performance, and how the information-processing and decision characteristics of humans shape performance. In this final section, we present an analysis of an illustrative case history (the Exxon Valdez disaster) with the goal of showing how concepts and principles in the first three sections can be applied to the analysis of real-world situations, again within the context of a control theory framework. The key idea is that common intuition can be replaced by a structured approach to thinking about systems outcomes. Thus, this section examines how the environment, the human element, and the task to be performed come together to affect system performance. Operational constructs of situation awareness, workload, human error, and usability are discussed in terms of the underlying psychological principles developed in the first three sections.

The website (http://www.wiley.com/go/remington) complements the text and is structured around modules. Each module is structured into sections, as appropriate, including the goal of the module, description of the exercise, materials needed, instructions, readings for further information, and reference to the corresponding book section. Some modules contain questions and descriptions of case studies that can be used as the basis for discussion. Others contain interactive exercises that either demonstrate phenomena (e.g., control order) or provide opportunities for students to further explore material described in the book (e.g., task analysis). Links to demonstrations available on the web that illustrate basic psychological phenomena are also provided. Finally, some modules focus on material not covered in depth in the text (e.g., anthropometry). The website is designed to grow over time to include additional modules and materials; we also intend to update the modules to keep the material fresh. For example, we anticipate that as new technologies (for example, the iPhone) are introduced, articles and examples of them will be incorporated into the site. The instructor can tailor these modules to meet various pedagogical goals. Some of the modules will be suitable for undergraduates at the junior or senior level, others more suitable for graduate courses. Instructors also can select web modules as desired to focus on topics as they see fit. Thus, engineering departments may choose modules associated with finite-state modeling of systems, whereas human factors courses may focus on the task analysis modules, and engineering psychology classes may omit both and instead add extra modules on auditory processing. We hope that instructors who adopt the book will contact us with suggestions for new topics that they would like to see covered.

As with any project, this one consumed a great deal of time and effort. We thank those who helped us along the way, including Shayne Loft, Beth Lyall, Jennifer McKneely, and Hal Pashler, who read the book and provided us with many suggestions for improvements (although they should not be faulted for any remaining inaccuracies); and Rebecca Davis, who helped us with reference checking and indexing, as well as editorial feedback. We thank David Kidd, Brian Taylor, and Nicole Werner, who developed the initial ideas and structure for the exercises included in our website. We also thank our (very) patient spouses, Karen Remington, Stuart Davis, and Valerie Folk, who gave us the space we needed to produce the program we desired. Without their support, this project would not have been possible.

Roger Remington, Deborah Boehm-Davis, Charles Folk