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Effort in Human Factors Performance and Decision Making Christopher D. Wickens Human Factors: The Journal of the Human Factors and Ergonomics Society published online 30 October 2014 DOI: 10.1177/0018720814558419 The online version of this article can be found at: http://hfs.sagepub.com/content/early/2014/10/30/0018720814558419 A more recent version of this article was published on - Nov 13, 2014
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558419
HFSXXX10.1177/0018720814558419Human FactorsEffort in Human Factors
At the Forefront of HF/E
Effort in Human Factors Performance and Decision Making Christopher D. Wickens, Alion Science and Technology, Boulder, Colorado
Objective: The aim of this study was to demonstrate the importance of effort in human factors. Background: Effort has made its appearance in several diverse formats and applications. Eight of these are integrated in the current writing related to learning, looking, task switching, visual search termination, information access, choosing decision strategies, and behaving safely. Method: This is based upon a literature review. Results: The common elements of these different effort applications are highlighted, particularly, their manifestations in either implicit or explicit expected value decisions. Conclusions: There is a need to show how the metrics of effort and workload assessment influence decisions in human factors, particularly, those related to safety. Keywords: decision making, effort, mental workload, multitasking, safety
Address correspondence to Chris Wickens, Alion Science and Technology, 4949 Pearl E. Circle, Boulder, CO 80301, USA; e-mail: [email protected]. HUMAN FACTORS Vol. XX, No. X, Month XXXX, pp. 1–8 DOI: 10.1177/0018720814558419 Copyright © 2014, Human Factors and Ergonomics Society.
Introduction
The concept of effort has been well articulated in psychology since over a century ago, when William James (1890) stated, “If you ask how many ideas or things we can attend to at once, the answer is not very easily more than one, unless the processes are very habitual” (p. 409). In such a statement, he implicitly deploys a scale of effort. The concept of effort or resource demand is well known to human factors researchers as mental workload, in either single or multitasking circumstances (e.g., Hancock & Meshkati, 1988; Moray, 1979). In these multitasking circumstances, effort is one of three components in predicting task interference (along with how one allocates resources between tasks and whether the tasks use the same or different resources; Wickens, 2008a). But beyond these two applications, to mental workload and multitasking, what is less appreciated and researched in the human factors community are the consequences of high effort, not just to dual task performance, in which high effort has a negative connotation, but also to learning and decision performance, where the consequences may be positive as well. Learning and decision researchers have written about these issues in isolation from other applications of effort (e.g., Paas & van Gog, 2009; Kahneman, 2011), but my purpose here is to draw them together in a way that can be more fully appreciated by the human factors community. In prior human factors research, the effort concept has made appearances in both applied and basic theoretical contexts but often in a secondary role or as a “supporting cast” to other cognitive variables, as in the several examples I cite next. Such diffusion of its applications may somewhat mask its overall importance as an underlying construct in human factors applications to decision making; hence the purpose of this manuscript is to consolidate eight of these manifestations in one place.
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2 Month XXXX - Human Factors 1. Effort in Learning
Cognitive load theory, an application of attention to education, presents learning as a multitasking environment and hence draws explicitly on the concept of effort sharing between tasks (Paas & van Gog, 2009). In particular, in this application, the three “tasks” competing for a common pool of effort are those focused on the extraneous load of a learning environment (e.g., unwanted distractions, clumsy interfaces in computer-based instruction), those allocated to the intrinsic load of the task to be mastered (increasing with task complexity, like long division compared to addition), and those resources that are directly allocated to learning (more than performing)—the concept of germane load. A particular target of investing germane load resources is in the working memory required to rehearse material, to chunk it, and to relate it to information already in memory, all leading to better learning and retention. And working memory is quite resource intensive in its demands (Baddeley, 2007; Engle, 2002; Wickens, Hollands, et al., 2013). Extraneous load is unwanted, and the learning environment should be designed to avoid it. But the distinction between intrinsic load, where effort demands may be inevitable because the task is complex, and germane load, where effort investment is desirable, is crucial because it dictates techniques or strategies such as part-task training and adaptive-difficulty training for reducing intrinsic load early in training. Thus sufficient resources are available for germane load (Wickens, Hutchins, Carolan, & Cumming, 2013). Such effort-reducing strategies, however, may have unfortunate “spin-off” effects that can lead to negative transfer, thereby offsetting the effort-reducing benefits (Hutchins, Wickens, Carolan, & Cumming, 2013; Wickens, Hollands, et al., 2013; Wickens, Hutchins, et al., 2013). For example, reducing effort by training in parts can prevent learning of necessary timesharing skills. Another application of effort to germane load in learning is seen in the known benefits of test taking in learning, compared to passive reading of material (Roediger & Karpicke, 2006). Test taking is certainly more effortful, but this effort investment is beneficial. The role of effort
investment in learning is closely related to the generation effect (McNamara & Healy, 2000; Slamecka & Graf, 1978). Here it is found that the active generation of responses produces better retention of the consequences of those responses than does passive witnessing of another agent providing the same information. Generating responses requires effort (Wickens, 2008a, 2008b), but this effort is productive to learning. We return to this issue in our final application. 2. Effort in Looking
Recently researchers have developed a model of visual scanning, SEEV, that predicts where people will look in relatively large-scale work spaces, such as the cockpit, operating room, or driving environment (Steelman-Allen, McCarley, & Wickens, 2011; Wickens, in press). Accordingly, visual scanning is driven by three “attractive forces”: toward sources (e.g., displays) that are salient (the S), where information is expected (the E) because its bandwidth or event rate is high, and where information is valuable (V) for the task(s) at hand. However, the movement of visual attention is also inhibited by the effort (the other E) to move the eyes longer distances. Scans are “cheap” but not “free,” particularly when those of longer distance require coupled head movements (see Application 5). Within the SEEV model, this inhibitory role of effort can be approximated by the distance between sequentially viewed displays, as such a sequence is otherwise predicted from salience, expectancy, and value of individual displays. But scans to widely separated locations simply may not take place at all: an “implicit decision” not to seek information there. The role of effort in inhibiting longer scans is invoked in the proximity compatibility principle of display design (Wickens & Carswell, 1995), which dictates that information sources that need to be integrated should be located close together. Here effort makes an appearance as a dual-task modulator between the two tasks: (a) comprehension performance of integrating information across multiple displays (for example, comparing a required drug dosage with an actual reading of administration amount on an infusion pump) and (b) the access of or search
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Effort in Human Factors
for those displays necessary to compare them. Underlying the principle is the multitask competition for effort (resources) between the task of maintaining information in working memory from the first accessed source, while searching for or accessing the second source, in order that they be compared; both working memory and search will compete for effort, and if the search is too effortful and/or the information in the first source is complex or long (demanding more effort for retention), then performance in integrating information will suffer. 3. Effort in Task Switching
In multiple resource theory (Wickens, 2008a), the effort or resource demand of a task has been invoked as one of three factors that inhibit concurrent multitask performance. However, sometimes in high-workload situations, with high time pressure, concurrent performance is simply impossible, and people regress to a sequential mode of multitasking, requiring attention switching and therefore implicit decisions to continue to perform some tasks while shedding or ignoring others. In recent models of sequential multitasking (Gutzwiller, Wickens, & Clegg, 2014; Kurzban, Duckworth, Kable, & Myers, 2013; Wickens, Santamaria, & Sebok, 2013), effort plays a different role from that in concurrent processing (Wickens, 2008a). First, because switching itself is effortful (resource-demanding executive control), high workload inhibits switching in general (cognitive tunneling). Second, people tend to switch to easier, rather than more difficult, effortful tasks, other task attributes being equal (Gutzwiller et al., 2014). Interlude
In the aforementioned applications, effort has been more of a background player that may subtly or subconsciously influence multitask performance (via resource competition, via implicit decisions to shed or switch tasks, looking at one place versus another, or instructional effectiveness). The remaining five applications address the role of effort in the more explicit choice between two distinct courses of action. As such, and in the context of risky decision models, effort is often represented as a negative attribute for decisions. This trade-off between effort and risk in decision making underlies all five of the application
3
Figure 1. Representation of effort in the choice between a risky and a sure-thing option.
areas that follow and can be represented as in Figure 1, a classic “decision tree” for decision analysis. On the lower limb is an option that typically represents a certain, but high-effort, outcome. On the upper limb is a risky, but lower-effort, path with two possible outcomes whose probabilities and utilities define the expected value of the upper path. Importantly in this representation, both the upper and lower limbs have subjective utilities, and the tendency to choose one or the other reflects the balance between the utility of effort expenditure (or conservation) and the expected utility of the risky outcome. 4. Effort in the Choice of When to Stop Searching
Whereas visual search models are often focused on the performance time to complete a search, some models also invoke the concept of effort—when the expected gain in finding a target is no longer worth the “effort” of continuing the search (Wolfe, Horowitz, & Kenner, 2005). Here effort is often expressed in the currency of time (an issue I address later). Thus, for example, terminating the search may avail resources available for other tasks, a good thing. However, this choice to terminate will also mean that the searched for item (a lost wallet or an existing tumor) will not be found, and this failure to find clearly has risks with negative consequences (credit cards are used, or the tumor metastasizes). 5. Information Access Effort: The Choice of Whether to Seek Information in the World or Rely on Memory
This application explicitly describes the effort of information access (Gray & Fu, 2004). Sometimes people decide to use their long-term
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4 Month XXXX - Human Factors
Figure 2. Information access effort.
memory to retrieve important information, rather than “looking it up” on a display (or in a book). In many situations, the effort of relying upon memory—knowledge in the head, to use Norman’s (1988) classic term—is relatively low (particularly, the physical effort). In contrast, the effort to “look it up,” or rely upon knowledge in the world (displays or paper), may vary greatly. In part, this variance in information access effort can be represented as in Figure 2 (Wickens, 1993), a function that also underlies effort in visual scanning, as in Application 2 earlier. To the left is the effort of eye movement to locate an information item: Scans are cheap, but not free, and their cost does grow slightly with greater distance (e.g., visual angle from a start point to an information source). In the center, when neck movements are involved, the effort is both greater (the head has greater inertia than the eyeball) and grows more steeply with greater distance. To the right, either trunk rotation is required (as in checking the “blind spot” behind the driver) or actual walking (as getting up from one’s desk to pull a book from the shelf behind). Sometimes one decides that this effort expenditure is not worth it, particularly when one is “pretty sure” of the accuracy of memory (e.g., “I’m pretty sure the publication date was 2006”). This decision nicely links physical ergonomics “below the neck” with cognitive ergonomics “above the neck” (Mehta & Parasuraman, 2014). However, and critically within the riskydecision framework of Figure 1, memory is
imperfect: It has a chance of being wrong, and correct versus incorrect memory retrieval of the information represents the two limbs of the upper (risky) path of the decision tree. The negative consequences of memory being wrong in the upper half may vary greatly depending on the circumstances. In one application of this model, researchers found that health care professionals, in assembling patient information to prepare for a “handoff” of the patient to an incoming shift, would often decide to use memory (the upper limb) rather than charts (the lower limb), even as the former was error prone (Yang et al., 2013). More critically, however, this decision tendency was substantially and significantly amplified if the patient information was a 5-m walk away instead of displayed on a screen directly in front of them. Information access effort inhibited the decision to seek knowledge in the world in favor of the less reliable memory and hence increased risk tolerance. Here again, one sees the role of effort in linking ergonomics above and below the neck. 6. Choosing Decision Strategies: Effort in Choosing How to Choose
Bettman, Johnson, and Payne (1990) have put forward a contingency model of decision strategies in which the choice of which strategy to employ is based, in part, on trading off the cognitive effort of the strategy against the
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Effort in Human Factors
5
anticipated accuracy of its employment, in a manner not unlike the choice between memory and world information in Application 5 earlier. Effort here is quantified by the number of mental operations required for the decision. Quite consistent with this contingency model is the representation of the choice between heuristics and algorithms in a variety of decisions (Gigerenzer & Todd, 1999; Gilovich, Griffin, & Kahneman, 2002; Kahneman, 2011). Heuristics are generally considered low-effort “quick and dirty” means of making a fairly easy decision, which will be correct most of the time. However, compared to more effort-demanding algorithms (like comparing all possible alternatives on all possible outcomes), heuristics may sometimes depart from “optimal” (i.e., the best possible outcome). Naturally, however, there are many occasions in which limited time simply does not allow the deployment of optimal decision algorithms (a pilot in an emergency; Orasanu & Strauch, 1994), and so the conservation of effort (here, effort is time) is itself a positive variable to be considered in the utility of the overall choice to use the heuristic; and the application of heuristics, given time or resource limits, may well be considered to be optimal (Wickens, Hollands, et al., 2013).
compliance means to direct people toward the lower limb of the Figure 1 decision tree: teaching risks (and even “scare stories”) to increase the perceived probability of the negative outcomes at the top versus reducing the cost of compliance. Because people do not generally think well about probabilities (Kahneman, 2011; Wickens, Hollands, et al., 2013; Young, Wogalter, & Brelsford, 1992) and are often biased (and overconfident) in their personal risk estimations (“It can’t happen to me”; Kahneman, 2011), greater effectiveness is typically achieved by approaches to the lower limb: to reduce the effort or cost of compliance. An important extension of this safety choice application, of critical interest to the health care community, is the decision by many patients recovering from surgery or other trauma to avoid (compliance with) rehabilitation and instead to choose the risky upper path that the body will recover in its absence. Although rehabilitation itself can be both physically and mentally effortful, patients will benefit from other effort-reducing techniques developed by health care practitioners to mitigate the effort of the overall rehabilitation experience.
7. Effort in Choosing to Behave Safely
One function of automation is to reduce the effort of performing or supervising the automated task so that the human has resources available for other tasks. But if automation is making decisions and carrying out actions, this directly invokes the generation effect described in Application 1. There, the absence of human action leads to the loss of learning. Here, that absence leads to the loss of a more short-term retention of the changes in state of the dynamic system under human supervision, that is, a loss of situation awareness (Durso, Rawson, & Girotto, 2007; Endsley, 1995; Wickens, 2008b). In the context of Figure 1, sometimes people choose to depend on automation’s advice that all is well, that a particular condition exists, or that procedures should be followed. This is a loweffort option, but it is risky because automation may be in error, with potentially disastrous consequences (Onnasch, Wickens, Li, & Manzey,
The choice to behave safely, whether at the workplace (wearing safety glasses) or in transportation (wearing seatbelts, driving the speed limits), is also easily represented in the context of Figure 1. The risks in the upper limb are those incurred if an accident does occur (that could have been prevented by choosing the lower limb). The effort costs of the lower limb (a “sure thing” to prevent the accident) are typically referred to as the “cost of compliance.” This cost may be a physical effort, such as locating or donning a safety device; a cognitive effort, such as reading through poorly written (but essential) safety and warning instructions (Wogalter, 2012); or even a “hedonic effort” of tolerating the discomfort of some safety constraints (poorly fitting safety glasses). One important implication of this representation of safe behavior lies in the two different
8. Effort in the Choice to Use Automation
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6 Month XXXX - Human Factors Table 1: Summary of Eight Manifestations of Effort Effort in
Along With
Affects
1. Cognitive load theory and instruction 2. Looking and scanning
Negative transfer
Performance (transfer)
Salience, expectancy, value
3. Task switching
Salience, priority, interest
4. Visual searching 5. Information access
Expected value Memory, expected value
6. Decision strategies
Expected value
7. Safety compliance choices 8. Automation use
Expected value Expected value
Implicit decision of where and whether to look, and information integration performance Implicit decision of what to do, what to shed, and multitask performance When to stop Explicit decision of whether to seek knowledge in the world Decision of how to decide (using heuristics versus algorithms) Decision of whether to comply Decision of whether to depend on automation
2014). Depending on automation here leads to phenomena of complacency and the automation bias (Parasuraman & Manzey, 2010). Thus investing the effort necessary to independently check on the “raw data” underlying automation’s decisions can mitigate the unfortunate circumstances. Conclusion
In conclusion, Table 1 depicts the eight manifestations of effort in human factors. In most of these, effort is typically a co-actor (and sometimes just a “supporting actor”) along with one or more other factors. Nevertheless, despite lack of “stardom,” its prevalence across all suggests its tremendous importance in our profession, particularly in the less mature area of decision and choice behavior. In this treatment, I have focused on how effort, and the human tendency to conserve it as a “precious resource” (Kahneman, 1973, 2011), often acts to compromise human–system interaction. In this broad sense, however, there is a wisdom, and sometimes even an optimality, in effort-conserving behaviors, but this optimality varies between applications. Furthermore, effort conservation is far from the rule in human behavior; people often seek challenges, both physical and cognitive. The concept of “engagement” (Matthews et al., 2010) is one that often invokes the hedonistic pleasure of investing
cognitive effort, for example, in germane load for learning (1) or in cell phone conversations in vehicles (7). Furthermore, prolonged periods of boredom and underload (effort free) are themselves maladaptive and are self-avoided when possible by engaging in behaviors such as mind wandering (He, Becic, Lee, & McCarley, 2011). I also recognize the sometimes ill-defined nature of the effort concept. In some circumstances, such as visual search, it can simply be expressed as time, a feature that is highly tractable for measurement (and hence modeling). But this is an incomplete metric for effort, as the volumes of research on mental workload measurement via physiology and subjective techniques have indicated (e.g., Gawron, 2008; Parasuraman & Rizzo, 2007; Tsang & Wilson, 1997; Vidulich & Tsang, 2012; Wickens & Tsang, in press). Indeed, one of the greatest challenges for our research community is to harness these nontime metrics and develop predictive models of the sorts of choices that underlie most of the applications discussed earlier and their vital role in safety. Effort may not be the star, but surely it is one of the most important supporting actors. Key Points •• Effort is ubiquitous in human factors. •• Effort constrains performance but also influences human decision making.
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Effort in Human Factors •• Many decisions trade off effort reduction against expected value of risky options. •• These implicit or explicit decisions are highlighted in eight application areas.
References Baddeley, A. D. (2007). Working memory, thought and action. Oxford, UK: Oxford University Press. Bettman, J. R., Johnson, E. J., & Payne, J. (1990). A componential analysis of cognitive effort and choice. Organizational Behavior and Human Performance, 45, 111–139. Durso, F. T., Rawson, K. A., & Girotto, S. (2007). Comprehension and situation awareness. In F. T. Durso, R. S. Nickerson, S. Dumais, S. Lewandowsky, & T. J. Perfect (Eds.), Handbook of applied cognition (2nd ed., pp. 163–194). Hoboken, NJ: Wiley. Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors, 37, 32–64. Engle, R. W. (2002). Working memory capacity as executive attention. Current Directions in Psychological Science, 11(1), 19–23. Gawron, V. J. (2008). Human performance, workload, and situational awareness measures handbook (2nd ed.). Boca Raton, FL: CRC Press. Gigerenzer, G., & Todd, P. (1999). Simple heuristics that make us smart. New York, NY: Oxford University Press Gilovich, T., Griffin, D., & Kahneman, D. (Eds.). Heuristics and biases: The psychology of intuitive judgment. New York, NY: Cambridge University Press. Gray, W. D., & Fu, W. T. (2004). Soft constraints in interactive behavior: The case of ignoring perfect knowledge in-the-world for imperfect knowledge in-the-head. Cognitive Science, 28, 359–382. Gutzwiller, R., Wickens, C., & Clegg, B. (2014). Workload overload modeling: An experiment with MATB II to inform a computational model of task management. In Proceedings of the Human Factors and Ergonomics Society 58th Annual Meeting (pp. 849–853). Santa Monica, CA: Human Factors and Ergonomics Society. Hancock, P. A., & Meshkati, N. (1988). Human mental workload. Amsterdam, Netherlands: North Holland. He, J., Becic, E., Lee, Y-C., & McCarley, J. (2011). Mind wandering behind the wheel: Performance and oculomotor correlates. Human Factors, 53, 13–21. Hutchins, S., Wickens, C., Carolan, T., & Cumming, J. (2013). The influence of cognitive load on transfer with error prevention training methods: A meta-analysis. Human Factors, 55, 854–874. James, W. (1890). Principles of psychology. New York, NY: Holt. Kahneman, D. (1973). Attention and effort. Englewood Cliffs, NJ: Prentice Hall. Kahneman, D. (2011). Thinking fast and slow New York, NY: Farrar, Strauss and Giroux. Kurzban, R., Duckworth, A. J., Kable, J., & Myers, J. (2013). An opportunity cost model of subjective effort and task performance. Behavioral and Brain Science, 36, 661–726. Matthews, G., Warm, J., Reinerman-Jones, L., Langheim, L., Washburn, D., & Tripp, L. (2010). Task engagement, cerebral blood flow velocity, and diagnostic monitoring for sustained attention. Journal of Experimental Psychology: Applied, 16, 187–203. McNamara, D., & Healy, A. (2000). A procedural explanation for the generation effect for simple and difficult multiplication
7 problems and answers. Journal of Memory and Language, 43, 652–679. Mehta, R., & Parasuraman, R. (2014). Effects of mental fatigue on the development of physical fatigue: A neurorergonomics approach. Human Factors, 56, 645–656. Moray, N. (1979). Mental workload: Its theory and measurement. New York, NY: Plenum Press. Norman, D. A. (1988). The psychology of everyday things. New York, NY: Basic Books. Onnasch, L., Wickens, C., Li, H., & Manzey, D. (2014). Human performance consequences of stages and levels of automation: An integrated meta-analysis. Human Factors, 56, 476–488. Orasanu, J., & Strauch, B. (1994). Temporal factors in aviation decision making. In Proceedings of the Human Factors and Ergonomics Society 38th Annual Meeting (pp. 935–939). Santa Monica, CA: Human Factors and Ergonomics Society. Paas, F., & van Gog, T. (2009). Principles for designing effective and efficient training of complex cognitive skills. In F. Durso (Ed.), Reviews of human factors and ergonomics (Vol. 5, pp. 166–194). Santa Monica, CA: Human Factors and Ergonomics Society. Parasuraman, R., & Manzey, D. (2010). Complacency and bias in human use of automation. Human Factors, 52, 381–410. Parasuraman, R., & Rizzo, M. (2007). Neuroergonomics: The brain at work. New York, NY: Oxford University Press. Roediger, H., & Karpicke, J. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17, 249–255. Slamecka, N. J., & Graf, P. (1978). The generation effect: Delineation of a phenomenon. Journal of Experimental Psychology: Human Learning, Memory, and Cognition, 4, 592–604. Steelman-Allen, K., McCarley, J., & Wickens, C. D. (2011). Modeling the control of attention in visual work spaces. Human Factors, 53, 142–153. Tsang, P. S., & Wilson, G. (1997). Mental workload measurement and analysis. In G. Salvendy (Ed.), Handbook of human factors and ergonomics (2nd ed., pp. 417–449). New York, NY: Wiley. Vidulich, M. A., & Tsang, P. (2012). Mental workload and situation awareness. In G. Salvendy (Ed.), Handbook of human factors and ergonomics (4th ed., pp. 243–273). Hoboken, NJ: Wiley. Wickens, C. D. (1993). Cognitive factors in display design. Journal of the Washington Academy of Sciences, 83, 179–201. Wickens, C. D. (2008a). Multiple resources and mental workload. Human Factors, 50, 449–455. Wickens, C. D. (2008b). Situation awareness: Review of Mica Endsley’s articles on situation awareness. Human Factors, 50, 397–403. Wickens, C. D. (in press). Noticing events in the visual workplace: The SEEV and NSEEV models. In R. Hoffman & R. Parasuraman (Eds.), Handbook of applied perception. Cambridge, UK: Cambridge University Press. Wickens, C. D., & Carswell, C. M. (1995). The proximity compatibility principle: Its psychological foundation and relevance to display design. Human Factors, 37, 473–494. Wickens, C. D., Hollands, J., Banbury, S., & Parasuraman, R. (2013). Engineering psychology and human performance (4th ed.). Upper Saddle River, NJ: Pearson. Wickens, C. D., Hutchins, S., Carolan, T., & Cumming, J. (2013). Effectiveness of part task training and increasing difficulty training strategies: A meta-analysis approach. Human Factors, 55, 461–470. Wickens, C. D., Santamaria, A., & Sebok, A. (2013). A computational model of task overload management and task switching.
Downloaded from hfs.sagepub.com at UNIV OF UTAH SALT LAKE CITY on November 30, 2014
8 Month XXXX - Human Factors In Proceedings of the Human Factors and Ergonomics Society 57th Annual Meeting (pp. 163–164). Santa Monica, CA: Human Factors and Ergonomics Society. Wickens, C. D., & Tsang, P. (in press). Workload. In D. Boehm-Davis, F. Durso, & J. Lee (Eds.), Handbook of human–systems integration. Washington, DC: American Psychological Association. Wogalter, M. (2012) Warnings and Hazard Communications. In G. Salvendy (Ed.), Handbook of human factors and ergonomics (pp. 243–268). Hoboken, NJ: Wiley. Wolfe, J. M., Horowitz, T. S., & Kenner, N. M. (2005). Rare items often missed in visual searches. Nature, 435, 439–440. Yang, Y., Wickens, C., Park, T., Siah, K., Fong, L., & Yin, S. (2013). The effect of information access cost and overconfidence bias on junior doctors’ pre-handover performance. In Proceedings of the Human Factors and Ergonomics Society 57th Annual Meeting (pp. 1755–1759). Santa Monica, CAA: Human Factors and Ergonomics Society. Young, S. L., Wogalter, M. S., & Brelsford, J. W. (1992). Relative contribution of likelihood and severity of injury to risk
perceptions. In Proceedings of the Human Factors and Ergonomics Society 36th Annual Meeting (pp. 1014–1018). Santa Monica, CA: Human Factors and Ergonomics Society.
Christopher D. Wickens is a senior scientist at Alion Science Corporation, Micro Analysis and Design Operations, in Boulder, Colorado; a professor emeritus at the University of Illinois at Urbana-Champaign; and visiting professor at Colorado State University. He received his PhD in psychology from the University of Michigan in 1974.
Date received: April 24, 2014 Date accepted: October 6, 2014
Effort in Human Factors Performance and Decision Making Christopher D. Wickens Human Factors: The Journal of the Human Factors and Ergonomics Society published online 30 October 2014 DOI: 10.1177/0018720814558419 The online version of this article can be found at: http://hfs.sagepub.com/content/early/2014/10/30/0018720814558419 A more recent version of this article was published on - Nov 13, 2014
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558419
HFSXXX10.1177/0018720814558419Human FactorsEffort in Human Factors
At the Forefront of HF/E
Effort in Human Factors Performance and Decision Making Christopher D. Wickens, Alion Science and Technology, Boulder, Colorado
Objective: The aim of this study was to demonstrate the importance of effort in human factors. Background: Effort has made its appearance in several diverse formats and applications. Eight of these are integrated in the current writing related to learning, looking, task switching, visual search termination, information access, choosing decision strategies, and behaving safely. Method: This is based upon a literature review. Results: The common elements of these different effort applications are highlighted, particularly, their manifestations in either implicit or explicit expected value decisions. Conclusions: There is a need to show how the metrics of effort and workload assessment influence decisions in human factors, particularly, those related to safety. Keywords: decision making, effort, mental workload, multitasking, safety
Address correspondence to Chris Wickens, Alion Science and Technology, 4949 Pearl E. Circle, Boulder, CO 80301, USA; e-mail: [email protected]. HUMAN FACTORS Vol. XX, No. X, Month XXXX, pp. 1–8 DOI: 10.1177/0018720814558419 Copyright © 2014, Human Factors and Ergonomics Society.
Introduction
The concept of effort has been well articulated in psychology since over a century ago, when William James (1890) stated, “If you ask how many ideas or things we can attend to at once, the answer is not very easily more than one, unless the processes are very habitual” (p. 409). In such a statement, he implicitly deploys a scale of effort. The concept of effort or resource demand is well known to human factors researchers as mental workload, in either single or multitasking circumstances (e.g., Hancock & Meshkati, 1988; Moray, 1979). In these multitasking circumstances, effort is one of three components in predicting task interference (along with how one allocates resources between tasks and whether the tasks use the same or different resources; Wickens, 2008a). But beyond these two applications, to mental workload and multitasking, what is less appreciated and researched in the human factors community are the consequences of high effort, not just to dual task performance, in which high effort has a negative connotation, but also to learning and decision performance, where the consequences may be positive as well. Learning and decision researchers have written about these issues in isolation from other applications of effort (e.g., Paas & van Gog, 2009; Kahneman, 2011), but my purpose here is to draw them together in a way that can be more fully appreciated by the human factors community. In prior human factors research, the effort concept has made appearances in both applied and basic theoretical contexts but often in a secondary role or as a “supporting cast” to other cognitive variables, as in the several examples I cite next. Such diffusion of its applications may somewhat mask its overall importance as an underlying construct in human factors applications to decision making; hence the purpose of this manuscript is to consolidate eight of these manifestations in one place.
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2 Month XXXX - Human Factors 1. Effort in Learning
Cognitive load theory, an application of attention to education, presents learning as a multitasking environment and hence draws explicitly on the concept of effort sharing between tasks (Paas & van Gog, 2009). In particular, in this application, the three “tasks” competing for a common pool of effort are those focused on the extraneous load of a learning environment (e.g., unwanted distractions, clumsy interfaces in computer-based instruction), those allocated to the intrinsic load of the task to be mastered (increasing with task complexity, like long division compared to addition), and those resources that are directly allocated to learning (more than performing)—the concept of germane load. A particular target of investing germane load resources is in the working memory required to rehearse material, to chunk it, and to relate it to information already in memory, all leading to better learning and retention. And working memory is quite resource intensive in its demands (Baddeley, 2007; Engle, 2002; Wickens, Hollands, et al., 2013). Extraneous load is unwanted, and the learning environment should be designed to avoid it. But the distinction between intrinsic load, where effort demands may be inevitable because the task is complex, and germane load, where effort investment is desirable, is crucial because it dictates techniques or strategies such as part-task training and adaptive-difficulty training for reducing intrinsic load early in training. Thus sufficient resources are available for germane load (Wickens, Hutchins, Carolan, & Cumming, 2013). Such effort-reducing strategies, however, may have unfortunate “spin-off” effects that can lead to negative transfer, thereby offsetting the effort-reducing benefits (Hutchins, Wickens, Carolan, & Cumming, 2013; Wickens, Hollands, et al., 2013; Wickens, Hutchins, et al., 2013). For example, reducing effort by training in parts can prevent learning of necessary timesharing skills. Another application of effort to germane load in learning is seen in the known benefits of test taking in learning, compared to passive reading of material (Roediger & Karpicke, 2006). Test taking is certainly more effortful, but this effort investment is beneficial. The role of effort
investment in learning is closely related to the generation effect (McNamara & Healy, 2000; Slamecka & Graf, 1978). Here it is found that the active generation of responses produces better retention of the consequences of those responses than does passive witnessing of another agent providing the same information. Generating responses requires effort (Wickens, 2008a, 2008b), but this effort is productive to learning. We return to this issue in our final application. 2. Effort in Looking
Recently researchers have developed a model of visual scanning, SEEV, that predicts where people will look in relatively large-scale work spaces, such as the cockpit, operating room, or driving environment (Steelman-Allen, McCarley, & Wickens, 2011; Wickens, in press). Accordingly, visual scanning is driven by three “attractive forces”: toward sources (e.g., displays) that are salient (the S), where information is expected (the E) because its bandwidth or event rate is high, and where information is valuable (V) for the task(s) at hand. However, the movement of visual attention is also inhibited by the effort (the other E) to move the eyes longer distances. Scans are “cheap” but not “free,” particularly when those of longer distance require coupled head movements (see Application 5). Within the SEEV model, this inhibitory role of effort can be approximated by the distance between sequentially viewed displays, as such a sequence is otherwise predicted from salience, expectancy, and value of individual displays. But scans to widely separated locations simply may not take place at all: an “implicit decision” not to seek information there. The role of effort in inhibiting longer scans is invoked in the proximity compatibility principle of display design (Wickens & Carswell, 1995), which dictates that information sources that need to be integrated should be located close together. Here effort makes an appearance as a dual-task modulator between the two tasks: (a) comprehension performance of integrating information across multiple displays (for example, comparing a required drug dosage with an actual reading of administration amount on an infusion pump) and (b) the access of or search
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Effort in Human Factors
for those displays necessary to compare them. Underlying the principle is the multitask competition for effort (resources) between the task of maintaining information in working memory from the first accessed source, while searching for or accessing the second source, in order that they be compared; both working memory and search will compete for effort, and if the search is too effortful and/or the information in the first source is complex or long (demanding more effort for retention), then performance in integrating information will suffer. 3. Effort in Task Switching
In multiple resource theory (Wickens, 2008a), the effort or resource demand of a task has been invoked as one of three factors that inhibit concurrent multitask performance. However, sometimes in high-workload situations, with high time pressure, concurrent performance is simply impossible, and people regress to a sequential mode of multitasking, requiring attention switching and therefore implicit decisions to continue to perform some tasks while shedding or ignoring others. In recent models of sequential multitasking (Gutzwiller, Wickens, & Clegg, 2014; Kurzban, Duckworth, Kable, & Myers, 2013; Wickens, Santamaria, & Sebok, 2013), effort plays a different role from that in concurrent processing (Wickens, 2008a). First, because switching itself is effortful (resource-demanding executive control), high workload inhibits switching in general (cognitive tunneling). Second, people tend to switch to easier, rather than more difficult, effortful tasks, other task attributes being equal (Gutzwiller et al., 2014). Interlude
In the aforementioned applications, effort has been more of a background player that may subtly or subconsciously influence multitask performance (via resource competition, via implicit decisions to shed or switch tasks, looking at one place versus another, or instructional effectiveness). The remaining five applications address the role of effort in the more explicit choice between two distinct courses of action. As such, and in the context of risky decision models, effort is often represented as a negative attribute for decisions. This trade-off between effort and risk in decision making underlies all five of the application
3
Figure 1. Representation of effort in the choice between a risky and a sure-thing option.
areas that follow and can be represented as in Figure 1, a classic “decision tree” for decision analysis. On the lower limb is an option that typically represents a certain, but high-effort, outcome. On the upper limb is a risky, but lower-effort, path with two possible outcomes whose probabilities and utilities define the expected value of the upper path. Importantly in this representation, both the upper and lower limbs have subjective utilities, and the tendency to choose one or the other reflects the balance between the utility of effort expenditure (or conservation) and the expected utility of the risky outcome. 4. Effort in the Choice of When to Stop Searching
Whereas visual search models are often focused on the performance time to complete a search, some models also invoke the concept of effort—when the expected gain in finding a target is no longer worth the “effort” of continuing the search (Wolfe, Horowitz, & Kenner, 2005). Here effort is often expressed in the currency of time (an issue I address later). Thus, for example, terminating the search may avail resources available for other tasks, a good thing. However, this choice to terminate will also mean that the searched for item (a lost wallet or an existing tumor) will not be found, and this failure to find clearly has risks with negative consequences (credit cards are used, or the tumor metastasizes). 5. Information Access Effort: The Choice of Whether to Seek Information in the World or Rely on Memory
This application explicitly describes the effort of information access (Gray & Fu, 2004). Sometimes people decide to use their long-term
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4 Month XXXX - Human Factors
Figure 2. Information access effort.
memory to retrieve important information, rather than “looking it up” on a display (or in a book). In many situations, the effort of relying upon memory—knowledge in the head, to use Norman’s (1988) classic term—is relatively low (particularly, the physical effort). In contrast, the effort to “look it up,” or rely upon knowledge in the world (displays or paper), may vary greatly. In part, this variance in information access effort can be represented as in Figure 2 (Wickens, 1993), a function that also underlies effort in visual scanning, as in Application 2 earlier. To the left is the effort of eye movement to locate an information item: Scans are cheap, but not free, and their cost does grow slightly with greater distance (e.g., visual angle from a start point to an information source). In the center, when neck movements are involved, the effort is both greater (the head has greater inertia than the eyeball) and grows more steeply with greater distance. To the right, either trunk rotation is required (as in checking the “blind spot” behind the driver) or actual walking (as getting up from one’s desk to pull a book from the shelf behind). Sometimes one decides that this effort expenditure is not worth it, particularly when one is “pretty sure” of the accuracy of memory (e.g., “I’m pretty sure the publication date was 2006”). This decision nicely links physical ergonomics “below the neck” with cognitive ergonomics “above the neck” (Mehta & Parasuraman, 2014). However, and critically within the riskydecision framework of Figure 1, memory is
imperfect: It has a chance of being wrong, and correct versus incorrect memory retrieval of the information represents the two limbs of the upper (risky) path of the decision tree. The negative consequences of memory being wrong in the upper half may vary greatly depending on the circumstances. In one application of this model, researchers found that health care professionals, in assembling patient information to prepare for a “handoff” of the patient to an incoming shift, would often decide to use memory (the upper limb) rather than charts (the lower limb), even as the former was error prone (Yang et al., 2013). More critically, however, this decision tendency was substantially and significantly amplified if the patient information was a 5-m walk away instead of displayed on a screen directly in front of them. Information access effort inhibited the decision to seek knowledge in the world in favor of the less reliable memory and hence increased risk tolerance. Here again, one sees the role of effort in linking ergonomics above and below the neck. 6. Choosing Decision Strategies: Effort in Choosing How to Choose
Bettman, Johnson, and Payne (1990) have put forward a contingency model of decision strategies in which the choice of which strategy to employ is based, in part, on trading off the cognitive effort of the strategy against the
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Effort in Human Factors
5
anticipated accuracy of its employment, in a manner not unlike the choice between memory and world information in Application 5 earlier. Effort here is quantified by the number of mental operations required for the decision. Quite consistent with this contingency model is the representation of the choice between heuristics and algorithms in a variety of decisions (Gigerenzer & Todd, 1999; Gilovich, Griffin, & Kahneman, 2002; Kahneman, 2011). Heuristics are generally considered low-effort “quick and dirty” means of making a fairly easy decision, which will be correct most of the time. However, compared to more effort-demanding algorithms (like comparing all possible alternatives on all possible outcomes), heuristics may sometimes depart from “optimal” (i.e., the best possible outcome). Naturally, however, there are many occasions in which limited time simply does not allow the deployment of optimal decision algorithms (a pilot in an emergency; Orasanu & Strauch, 1994), and so the conservation of effort (here, effort is time) is itself a positive variable to be considered in the utility of the overall choice to use the heuristic; and the application of heuristics, given time or resource limits, may well be considered to be optimal (Wickens, Hollands, et al., 2013).
compliance means to direct people toward the lower limb of the Figure 1 decision tree: teaching risks (and even “scare stories”) to increase the perceived probability of the negative outcomes at the top versus reducing the cost of compliance. Because people do not generally think well about probabilities (Kahneman, 2011; Wickens, Hollands, et al., 2013; Young, Wogalter, & Brelsford, 1992) and are often biased (and overconfident) in their personal risk estimations (“It can’t happen to me”; Kahneman, 2011), greater effectiveness is typically achieved by approaches to the lower limb: to reduce the effort or cost of compliance. An important extension of this safety choice application, of critical interest to the health care community, is the decision by many patients recovering from surgery or other trauma to avoid (compliance with) rehabilitation and instead to choose the risky upper path that the body will recover in its absence. Although rehabilitation itself can be both physically and mentally effortful, patients will benefit from other effort-reducing techniques developed by health care practitioners to mitigate the effort of the overall rehabilitation experience.
7. Effort in Choosing to Behave Safely
One function of automation is to reduce the effort of performing or supervising the automated task so that the human has resources available for other tasks. But if automation is making decisions and carrying out actions, this directly invokes the generation effect described in Application 1. There, the absence of human action leads to the loss of learning. Here, that absence leads to the loss of a more short-term retention of the changes in state of the dynamic system under human supervision, that is, a loss of situation awareness (Durso, Rawson, & Girotto, 2007; Endsley, 1995; Wickens, 2008b). In the context of Figure 1, sometimes people choose to depend on automation’s advice that all is well, that a particular condition exists, or that procedures should be followed. This is a loweffort option, but it is risky because automation may be in error, with potentially disastrous consequences (Onnasch, Wickens, Li, & Manzey,
The choice to behave safely, whether at the workplace (wearing safety glasses) or in transportation (wearing seatbelts, driving the speed limits), is also easily represented in the context of Figure 1. The risks in the upper limb are those incurred if an accident does occur (that could have been prevented by choosing the lower limb). The effort costs of the lower limb (a “sure thing” to prevent the accident) are typically referred to as the “cost of compliance.” This cost may be a physical effort, such as locating or donning a safety device; a cognitive effort, such as reading through poorly written (but essential) safety and warning instructions (Wogalter, 2012); or even a “hedonic effort” of tolerating the discomfort of some safety constraints (poorly fitting safety glasses). One important implication of this representation of safe behavior lies in the two different
8. Effort in the Choice to Use Automation
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6 Month XXXX - Human Factors Table 1: Summary of Eight Manifestations of Effort Effort in
Along With
Affects
1. Cognitive load theory and instruction 2. Looking and scanning
Negative transfer
Performance (transfer)
Salience, expectancy, value
3. Task switching
Salience, priority, interest
4. Visual searching 5. Information access
Expected value Memory, expected value
6. Decision strategies
Expected value
7. Safety compliance choices 8. Automation use
Expected value Expected value
Implicit decision of where and whether to look, and information integration performance Implicit decision of what to do, what to shed, and multitask performance When to stop Explicit decision of whether to seek knowledge in the world Decision of how to decide (using heuristics versus algorithms) Decision of whether to comply Decision of whether to depend on automation
2014). Depending on automation here leads to phenomena of complacency and the automation bias (Parasuraman & Manzey, 2010). Thus investing the effort necessary to independently check on the “raw data” underlying automation’s decisions can mitigate the unfortunate circumstances. Conclusion
In conclusion, Table 1 depicts the eight manifestations of effort in human factors. In most of these, effort is typically a co-actor (and sometimes just a “supporting actor”) along with one or more other factors. Nevertheless, despite lack of “stardom,” its prevalence across all suggests its tremendous importance in our profession, particularly in the less mature area of decision and choice behavior. In this treatment, I have focused on how effort, and the human tendency to conserve it as a “precious resource” (Kahneman, 1973, 2011), often acts to compromise human–system interaction. In this broad sense, however, there is a wisdom, and sometimes even an optimality, in effort-conserving behaviors, but this optimality varies between applications. Furthermore, effort conservation is far from the rule in human behavior; people often seek challenges, both physical and cognitive. The concept of “engagement” (Matthews et al., 2010) is one that often invokes the hedonistic pleasure of investing
cognitive effort, for example, in germane load for learning (1) or in cell phone conversations in vehicles (7). Furthermore, prolonged periods of boredom and underload (effort free) are themselves maladaptive and are self-avoided when possible by engaging in behaviors such as mind wandering (He, Becic, Lee, & McCarley, 2011). I also recognize the sometimes ill-defined nature of the effort concept. In some circumstances, such as visual search, it can simply be expressed as time, a feature that is highly tractable for measurement (and hence modeling). But this is an incomplete metric for effort, as the volumes of research on mental workload measurement via physiology and subjective techniques have indicated (e.g., Gawron, 2008; Parasuraman & Rizzo, 2007; Tsang & Wilson, 1997; Vidulich & Tsang, 2012; Wickens & Tsang, in press). Indeed, one of the greatest challenges for our research community is to harness these nontime metrics and develop predictive models of the sorts of choices that underlie most of the applications discussed earlier and their vital role in safety. Effort may not be the star, but surely it is one of the most important supporting actors. Key Points •• Effort is ubiquitous in human factors. •• Effort constrains performance but also influences human decision making.
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Effort in Human Factors •• Many decisions trade off effort reduction against expected value of risky options. •• These implicit or explicit decisions are highlighted in eight application areas.
References Baddeley, A. D. (2007). Working memory, thought and action. Oxford, UK: Oxford University Press. Bettman, J. R., Johnson, E. J., & Payne, J. (1990). A componential analysis of cognitive effort and choice. Organizational Behavior and Human Performance, 45, 111–139. Durso, F. T., Rawson, K. A., & Girotto, S. (2007). Comprehension and situation awareness. In F. T. Durso, R. S. Nickerson, S. Dumais, S. Lewandowsky, & T. J. Perfect (Eds.), Handbook of applied cognition (2nd ed., pp. 163–194). Hoboken, NJ: Wiley. Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors, 37, 32–64. Engle, R. W. (2002). Working memory capacity as executive attention. Current Directions in Psychological Science, 11(1), 19–23. Gawron, V. J. (2008). Human performance, workload, and situational awareness measures handbook (2nd ed.). Boca Raton, FL: CRC Press. Gigerenzer, G., & Todd, P. (1999). Simple heuristics that make us smart. New York, NY: Oxford University Press Gilovich, T., Griffin, D., & Kahneman, D. (Eds.). Heuristics and biases: The psychology of intuitive judgment. New York, NY: Cambridge University Press. Gray, W. D., & Fu, W. T. (2004). Soft constraints in interactive behavior: The case of ignoring perfect knowledge in-the-world for imperfect knowledge in-the-head. Cognitive Science, 28, 359–382. Gutzwiller, R., Wickens, C., & Clegg, B. (2014). Workload overload modeling: An experiment with MATB II to inform a computational model of task management. In Proceedings of the Human Factors and Ergonomics Society 58th Annual Meeting (pp. 849–853). Santa Monica, CA: Human Factors and Ergonomics Society. Hancock, P. A., & Meshkati, N. (1988). Human mental workload. Amsterdam, Netherlands: North Holland. He, J., Becic, E., Lee, Y-C., & McCarley, J. (2011). Mind wandering behind the wheel: Performance and oculomotor correlates. Human Factors, 53, 13–21. Hutchins, S., Wickens, C., Carolan, T., & Cumming, J. (2013). The influence of cognitive load on transfer with error prevention training methods: A meta-analysis. Human Factors, 55, 854–874. James, W. (1890). Principles of psychology. New York, NY: Holt. Kahneman, D. (1973). Attention and effort. Englewood Cliffs, NJ: Prentice Hall. Kahneman, D. (2011). Thinking fast and slow New York, NY: Farrar, Strauss and Giroux. Kurzban, R., Duckworth, A. J., Kable, J., & Myers, J. (2013). An opportunity cost model of subjective effort and task performance. Behavioral and Brain Science, 36, 661–726. Matthews, G., Warm, J., Reinerman-Jones, L., Langheim, L., Washburn, D., & Tripp, L. (2010). Task engagement, cerebral blood flow velocity, and diagnostic monitoring for sustained attention. Journal of Experimental Psychology: Applied, 16, 187–203. McNamara, D., & Healy, A. (2000). A procedural explanation for the generation effect for simple and difficult multiplication
7 problems and answers. Journal of Memory and Language, 43, 652–679. Mehta, R., & Parasuraman, R. (2014). Effects of mental fatigue on the development of physical fatigue: A neurorergonomics approach. Human Factors, 56, 645–656. Moray, N. (1979). Mental workload: Its theory and measurement. New York, NY: Plenum Press. Norman, D. A. (1988). The psychology of everyday things. New York, NY: Basic Books. Onnasch, L., Wickens, C., Li, H., & Manzey, D. (2014). Human performance consequences of stages and levels of automation: An integrated meta-analysis. Human Factors, 56, 476–488. Orasanu, J., & Strauch, B. (1994). Temporal factors in aviation decision making. In Proceedings of the Human Factors and Ergonomics Society 38th Annual Meeting (pp. 935–939). Santa Monica, CA: Human Factors and Ergonomics Society. Paas, F., & van Gog, T. (2009). Principles for designing effective and efficient training of complex cognitive skills. In F. Durso (Ed.), Reviews of human factors and ergonomics (Vol. 5, pp. 166–194). Santa Monica, CA: Human Factors and Ergonomics Society. Parasuraman, R., & Manzey, D. (2010). Complacency and bias in human use of automation. Human Factors, 52, 381–410. Parasuraman, R., & Rizzo, M. (2007). Neuroergonomics: The brain at work. New York, NY: Oxford University Press. Roediger, H., & Karpicke, J. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17, 249–255. Slamecka, N. J., & Graf, P. (1978). The generation effect: Delineation of a phenomenon. Journal of Experimental Psychology: Human Learning, Memory, and Cognition, 4, 592–604. Steelman-Allen, K., McCarley, J., & Wickens, C. D. (2011). Modeling the control of attention in visual work spaces. Human Factors, 53, 142–153. Tsang, P. S., & Wilson, G. (1997). Mental workload measurement and analysis. In G. Salvendy (Ed.), Handbook of human factors and ergonomics (2nd ed., pp. 417–449). New York, NY: Wiley. Vidulich, M. A., & Tsang, P. (2012). Mental workload and situation awareness. In G. Salvendy (Ed.), Handbook of human factors and ergonomics (4th ed., pp. 243–273). Hoboken, NJ: Wiley. Wickens, C. D. (1993). Cognitive factors in display design. Journal of the Washington Academy of Sciences, 83, 179–201. Wickens, C. D. (2008a). Multiple resources and mental workload. Human Factors, 50, 449–455. Wickens, C. D. (2008b). Situation awareness: Review of Mica Endsley’s articles on situation awareness. Human Factors, 50, 397–403. Wickens, C. D. (in press). Noticing events in the visual workplace: The SEEV and NSEEV models. In R. Hoffman & R. Parasuraman (Eds.), Handbook of applied perception. Cambridge, UK: Cambridge University Press. Wickens, C. D., & Carswell, C. M. (1995). The proximity compatibility principle: Its psychological foundation and relevance to display design. Human Factors, 37, 473–494. Wickens, C. D., Hollands, J., Banbury, S., & Parasuraman, R. (2013). Engineering psychology and human performance (4th ed.). Upper Saddle River, NJ: Pearson. Wickens, C. D., Hutchins, S., Carolan, T., & Cumming, J. (2013). Effectiveness of part task training and increasing difficulty training strategies: A meta-analysis approach. Human Factors, 55, 461–470. Wickens, C. D., Santamaria, A., & Sebok, A. (2013). A computational model of task overload management and task switching.
Downloaded from hfs.sagepub.com at UNIV OF UTAH SALT LAKE CITY on November 30, 2014
8 Month XXXX - Human Factors In Proceedings of the Human Factors and Ergonomics Society 57th Annual Meeting (pp. 163–164). Santa Monica, CA: Human Factors and Ergonomics Society. Wickens, C. D., & Tsang, P. (in press). Workload. In D. Boehm-Davis, F. Durso, & J. Lee (Eds.), Handbook of human–systems integration. Washington, DC: American Psychological Association. Wogalter, M. (2012) Warnings and Hazard Communications. In G. Salvendy (Ed.), Handbook of human factors and ergonomics (pp. 243–268). Hoboken, NJ: Wiley. Wolfe, J. M., Horowitz, T. S., & Kenner, N. M. (2005). Rare items often missed in visual searches. Nature, 435, 439–440. Yang, Y., Wickens, C., Park, T., Siah, K., Fong, L., & Yin, S. (2013). The effect of information access cost and overconfidence bias on junior doctors’ pre-handover performance. In Proceedings of the Human Factors and Ergonomics Society 57th Annual Meeting (pp. 1755–1759). Santa Monica, CAA: Human Factors and Ergonomics Society. Young, S. L., Wogalter, M. S., & Brelsford, J. W. (1992). Relative contribution of likelihood and severity of injury to risk
perceptions. In Proceedings of the Human Factors and Ergonomics Society 36th Annual Meeting (pp. 1014–1018). Santa Monica, CA: Human Factors and Ergonomics Society.
Christopher D. Wickens is a senior scientist at Alion Science Corporation, Micro Analysis and Design Operations, in Boulder, Colorado; a professor emeritus at the University of Illinois at Urbana-Champaign; and visiting professor at Colorado State University. He received his PhD in psychology from the University of Michigan in 1974.
Date received: April 24, 2014 Date accepted: October 6, 2014
