Mental Models of Risk
- Ann BostromAnn BostromEvans School of Public Policy, University of Washington
Mental models of health risks are the causal beliefs that comprise one’s inference engines for the interpretation and prediction of health and illness experiences and messages. Mental models of health risks can be parsed into a handful of common elements, including beliefs about causes, consequences, and cures as well as identifying information such as symptoms and timing. Mental models research deriving from a risk and decision analysis framework emphasizes exposure sources and pathways as part of causal thinking as well as how interventions may reduce or increase the risk. Mental models can be developed as a function of one’s goals or the problem in a specific context, rather than as coherent, stable knowledge structures in long-term memory. For this reason they can be piecemeal and inconsistent in the absence of expertise or experience with the risk. Derived often by analogy with more familiar risks, mental models of health risks can lead to effective health behaviors but also to costly inaction or misplaced action. Assessing mental models of hazardous processes can contribute to the design of effective risk communications by identifying the concrete information message recipients need to cope with health risks, thereby making or strengthening common-sense links between risk and action representations. Although a wide variety of research methods are used to investigate mental models, achieving this level of specificity requires attention to substantive details. Researchers are beginning to better understand the interactions between mental models of risk and their social, cultural, and physical contexts, but much remains to explore.
An Illustrative Example and Introduction to Mental Models of Risk
Mental models of risk are those knowledge structures that a person “runs” or simulates to solve risk problems and infer what will happen. People make sense of the world through their mental models (Gentner & Stevens, 1983), which are internal representations of external realities (Bartlett, 1932; Craik, 1943; Rouse & Morris, 1986).
Pfiesteria piscicida presents an interesting example of the power of mental models of risk. First identified in 1988 (Paolisso & Chamber, 2001), Pfiesteria piscicida is a microscopic marine organism that in one of its 24 known life forms attacks fish by releasing a neurotoxin in the water in their presence. It can also, however, be present in the air in the immediate vicinity of the water, making it possible for humans to be exposed to Pfiesteria without contact with fish or the water (Kempton & Falk, 2000). Reports of Pfiesteria piscicida in 1997 were followed by tens of millions of dollars of lost seafood sales in Maryland and North Carolina, primarily for species and fish from areas unaffected by Pfiesteria. Subsequently, careful study of mental models of Pfiesteria piscicida demonstrated that people adhered widely to the belief that Pfiesteria harms the environment and so could indirectly harm people and that Pfiesteria could also harm people if they ate seafood, neither of which belief was espoused by biologists at the time (Kempton & Falk, 2000). The study authors concluded that mental models of Pfiesteria were derived from one of four preexisting cultural models—pollution, toxics, diseases of fish, or parasites in fish—which in turn led to behavioral responses such as not buying fish as well as to an underappreciation of the risk from breathing aerosoled neurotoxins over waters during Pfiesteria outbreaks (Kempton & Falk, 2000; see also Paolisso & Chambers, 2001).
As the case of Pfiesteria piscicida illustrates, when individuals detect symptoms or receive information about a health risk, they process that information through their mental models, which are their preexisting beliefs of how the world works. More generally, mental models of health risks are the causal beliefs we use to interpret, describe, explain, and predict health risks. The concept of small-scale models the mind creates to anticipate events has been traced back to Kenneth Craik’s 1943 book The Nature of Explanation and to more recent research on mental models (Gentner & Stevens, 1983) that presents mental models as how humans understand the workings of physical systems. Johnson-Laird (1983) further generalized the concept, arguing that mental models are how humans solve all varieties of deductive reasoning problems. Related findings from Böhm and colleagues, as well as many others, suggest that causal thinking about a risk also underlies attitudes toward it (Böhm & Pfister, 2001).
Elicitation and analysis of mental models of physical processes—such as how vaccines work, how electricity works, or how the human circulatory system works—has for several decades been a focus of studies in science education, cognitive anthropology, and cognitive psychology (e.g., de Leeuw, 1993; Downs, de Bruin, & Fischhoff, 2008; Gentner & Stevens, 1983; Holland & Quinn, 1987; Kempton, 1986; Kempton, Boster, & Hartley, 1996; Mishra & Brewer, 2003; Nersessian, 1992) as well as health (Jungermann, Schütz, & Thüring, 1988; Meyer, Leventhal, & Gutmann, 1985). Research on mental models of risk builds on these studies by examining mental models of hazardous processes and assessing what information the message recipient would need to mitigate the risk induced by those hazards (Bostrom, Fischhoff, & Morgan, 1992; Bruine de Bruin & Bostrom, 2013; Morgan, Fischhoff, Bostrom, & Atman, 2002).
People make sense of illness and disease through their prior mental models as well, as demonstrated in studies of folk models of illness (Greenhalgh, Helman, & Chowdhury, 1998), common-sense representations of illness (Lau & Hartman, 1983), the common-sense model of illness representation (e.g., Godoy-Izquierdo, López-Chicheri, López-Torrecillas, Vélez, & Godoy, 2007; Leventhal et al., 1997; Leventhal, Meyer, & Nerenz, 1980), and in mental models studies of health risks (e.g., Silverman et al., 2001), including risks from health technologies such as vaccines (e.g., Bostrom & Atkinson, 2007; Downs et al., 2008) and environmental health risks (e.g., Bostrom & Fischhoff, 2001; Cox et al., 2003; Riley, 2014; Severtson, Baumann, & Brown, 2008). Among these, one of the most prolific bodies of research addressing mental models of health risks is that on the common-sense model (CSM) of illness representation, stemming from research by the Leventhals and their colleagues (e.g., Meyer, Leventhal, & Gutmann, 1985). They describe a common-sense model of illness as a “model of the processes underlying the commonsense management of health threats in everyday life” (Leventhal, Leventhal, & Breland, 2011).
Parsing Mental Models of Risk
Risk researchers have classified hazardous processes into causal stages, from exposure source and pathways through harmful consequences or effects, with effects conditional on sensitivity or vulnerability and dose (e.g., Fischhoff, Hohenemser, Kasperson, & Kates, 1978; Morgan, 1993). Further, risk can potentially be mitigated by interventions in the hazardous process at any point, for example, by preventing exposure to the source or by treating the consequences to reduce their severity. Research characterizing mental models of hazardous processes has built on this classification scheme (Bostrom et al., 1992; Morgan, Fischhoff, Bostrom, Lave, & Atman, 1992). In such studies of environmental health risks, concepts expressed by participants reflect how people identify the hazard (e.g., radon is radioactive), what they think is the source or the cause of the hazard (e.g., granite or other radium-bearing rock underneath a house or uranium mill tailings), the pathways for being exposed to the mechanisms producing the effects (e.g., exposure pathways such as breathing radon gas that has been trapped in a house), what the effects of such exposures are (i.e., consequences such as lung cancer), along with beliefs about how to control or mitigate the risk (e.g., install and use sub-slab ventilation to remove radon gas). Illustrating the specificity and causal links evident in specific mental models, one of the homeowners interviewed about radon for the Bostrom et al. (1992) study reported they had removed their wallpaper, thinking that it must be contaminated with radiation and pose a radiation risk; this is not a risk factor for radon, which decays rapidly, with a half-life of just under four days. Almost none of those interviewed for the study mentioned decay.
As illustrated by this example, identity beliefs about environmental health risks may not stem directly from symptoms or features that a person can directly detect or experience, though there is some indication that direct experiences and detectable symptoms are prioritized in identifying risks. For example, in a study of mental models of skin cancer risks, among the causal factors that received the highest risk rankings were sun exposure without using sunscreen and pollution or hazards in the environment (Cameron, 2008). In another study about workers’ mental models of the risks of exposure to perchloroethylene (as used in dry-cleaning) and rosin-based solder flux (used in electronics), workers perceived acute risks from perchloroethylene, such as headaches and dizziness (Cox et al., 2003). These workers were unlikely to experience (or mention) the potentially more serious but also more imperceptible chronic risks that experts cited, including depression, liver and kidney damage, impaired memory, as well as potential reproductive effects and carcinogenicity, and did not appear to include cumulative low-level exposures in their mental models of the risk (Cox et al., 2003). Further, their perceptions of solder flux as a risk were allied primarily with visible smoke from soldering; they believed that solder flux presented a low risk and that the hazard could be avoided by moving one’s head to the side (Cox et al., 2003).
Research on mental models of illness reflects similar conceptual structures. Studies conducted on the common-sense model of illness have identified five categories of beliefs: illness identity (symptoms), causes, consequences, control/cure, and temporality/timeline (Benyamini, 2011). Skin cancer may be identified as either “skin cancer” or “melanoma,” for example, associated with symptoms such as irregular moles, seen as caused by multiple sunburns and as resulting in painful surgery or death in late adulthood, with the possibility of prevention through use of sunscreen (Waters, McQueen, & Cameron, 2013). In extensions of research on the common-sense model of illness, it has been demonstrated that communications that create or strengthen common-sense links between risk and action representations increase action intentions for those who receive diagnostic information suggesting they are at high risk (Cameron, Marteau, Brown, Klein, & Sherman, 2012).
Illness causal attributions found in one study could be divided into three general categories: environmental, behavioral, and hidden causes (Shiloh, Rashuk-Rosenthal, & Benyamini, 2002). Environmental causes fell into more abstract (e.g., air pollution) and more concrete (e.g., germs) categories. Behavioral causes included substances like caffeine or drugs and lifestyles such as too little sleep. Hidden causes included genetic or biologic factors such as heredity or age, mystical factors such as fate or chance, and psychosocial factors like worries and stress. As is evident from these categories, the categorization of causal attributions is related to control; for example, concrete environmental causes were generally rated as more controllable than abstract environmental causes (Shiloh et al., 2002). Many of these causal attributions and their roles in mental models of health risks have been studied extensively, for example, heredity (e.g., Walter, Emery, Braithwaite, & Marteau, 2004) and genetics (e.g., Claassen et al., 2011; Shilo, 2006). Further, beliefs about cures and solutions tend to be consistent with causal beliefs (Ogden & Jubb, 2008).
Mental Model Coherence and Consistency
However, mental models can be piecemeal, inconsistent, and incomplete (Johnson-Laird, 2004; Norman, 1983), in part because they may be developed as a function of one’s goals or the problem in a specific context, rather than as coherent, stable knowledge structures in long-term memory (Kahneman & Tversky, 1982, Tversky, 1993; see also Evans, 2006; Leventhal et al., 2011). In interviews about smallpox vaccine and disease in the early 2000s, even highly educated interviewees were likely to begin their responses with statements that they really didn’t know much about either (Bostrom & Atkinson, 2007). Mental models studies with lay participants often reveal knowledge gaps, such as the decay rate of radon decays or the serious chronic risks of perchloroethylene, both discussed in the previous section (Cox et al., 2003; see Byram, Fischhoff, Embrey, Bruine de Bruin, & Thorne, 2001, for examples of knowledge gaps regarding breast implants). Mental models studies with lay participants also often reveal a lack of specificity and sometimes conflicting beliefs about specific risks, as illustrated by hypertension beliefs (described in the next section).
Like other mental models, mental models of hazardous processes tend to be more structured and coherent the greater a person’s expertise. Experts have more hierarchical, detailed, and structured mental models in general (Chi et al., 1981) and are less likely to infer parts of hazardous processes from analogous risks or to exhibit large gaps in their knowledge, for example, with regard to exposure processes (e.g., Bostrom et al., 1992; Lazrus, Morss, Demuth, Lazo, & Bostrom, 2016). They are also more likely to understand specifics that enable them to distinguish one risk from another. For example, mental models studies have found that laypersons may confuse or conflate risks that share common properties, such as smallpox and chickenpox, which are eliminated and rare, respectively, and both of which manifest with rashes such as corpuscles or spots (Bostrom & Atkinson, 2007). Another example of this kind of thinking is the confusion between stratospheric ozone depletion, or the ozone hole, and global warming (Bostrom et al., 1994; Read et al., 1994; Reynolds, Bostrom, Read, & Morgan, 2010), which appears to lead some people to infer that global warming will lead to an increase in the occurrence of skin cancer (Bostrom & Fischhoff, 2001).
Analogies, Similarity, Experience, and Expert Information as Sources of Mental Models
As noted by Lau and Hartman (1983, p. 184). “the first thing people probably do when they are feeling sick is to try to plug their illness into an existing schema.” The accumulation of small illnesses over the course of one’s life builds a body of experience that informs how an individual interprets new symptoms and illnesses. In the face of uncertainty, mental models of unknown or new health risks are hence likely to be inferred or assimilated from mental models of known or familiar risks (Bostrom, 2008; Lau & Hartman, 1983; Visschers et al., 2007). Prior illness experiences—both personal and familial—are an important source of knowledge and beliefs about an illness (Leventhal et al., 1980).
Doctors and other expert sources of medical information can and do inform people about illness (Hay, Coups, Ford, & DiBonaventura, 2009). For instance, linkage between use of sun protection and better health has grown over time among youth in Europe, as has the use of sun protection (Peacey, Steptoe, Sandennan, Wardle, & Sanderman, 2006). However, people may develop mental models that do not incorporate or integrate that information coherently or consistently but rather explain it to others in such a way as to accommodate inconsistencies with it. An example of this is the belief that one can experience and personally detect and experience symptoms of hypertension despite knowing and telling others that doctors say one can’t (Meyer et al., 1985). This is important because beliefs about symptoms are a key element of how people identify a risk, and such beliefs are predictive of protective action (Halm, Mora, & Leventhal, 2006; Leventhal et al., 2003; McCutchan, Wood, Edwards, Richards, & Brain, 2015), though their influence may be mediated by affective responses such as worry (Kiviniemi & Ellis, 2014). Further, mismatches between expected and experienced symptoms can result in clinically relevant delays in seeking treatment, for example, a person delaying seeking treatment for a heart attack (Horne, James, Petrie, Weinman, & Vincent, 2000).
Mental Models as a Basis for Health Risk Message Design
One way to increase the effectiveness of health messages is to design them with the user in mind, taking into account the contexts of and constraints on message recipients, including their prior knowledge, beliefs, and experiences, as reflected in their mental models. People’s existing mental models influence how they interpret hazards and health risk messages (Leventhal et al., 1997; Morgan et al., 2002, 1992). Hence, studying and assessing mental models of hazardous processes can contribute to the design of effective risk communications (Bostrom et al., 1992; Bruine de Bruin & Bostrom, 2013; Löfstedt, 1991; Morgan et al., 1992; Niewöhner, Cox, Gerrard, & Pidgeon, 2004; Riley, 2014). Understanding physician users’ mental models has been proposed as a way to design effective clinical guidelines as well (Patel, Arocha, Diermeier, Greenes, & Shortliffe, 2001). Seen as essential to understand as a basis for the design of effective science education (e.g., Mishra & Brewer, 2003), mental models are widely recognized as central in human decision-making and behavior (e.g., in the 2015 World Development report by the World Bank).
Determining the mental models of risk message recipients and subjecting draft communication materials to empirical evaluation enables message designers to assess how messages will be interpreted and select risk communication message content that will be effective (Fischhoff, Brewer, & Downs, 2011). To discover mental models requires analyzing decision-making and behaviors or using ethnographic or cognitive research approaches designed for this purpose (for additional examples and specifics, see Böhm & Pfister, 2001; Bruine de Bruin & Bostrom, 2013; Gentner, 2002; Gentner & Stevens, 1983).
Methods of Investigating Mental Models of Risk
A wide range of methods are employed to elicit and characterize mental models of risks, spanning ethnographic approaches to interviewing and observation, more structured self-report approaches, such as survey methods, and experimental approaches to observing and assessing judgment and decision-making processes (e.g., Bruine de Bruin & Bostrom, 2013; Gentner & Stevens, 1983; Lau et al., 1989; Meyer et al., 1985; Morgan et al., 2002; Wood, Bostrom, Bridges, & Linkov, 2012). Although only a subset of these are rooted in formal decision analysis or human-centered design principles (e.g., Morgan et al., 2002), many reflect either underlying theories of culture, such as risk and culture (Douglas & Wildavsky, 1982) or culture as consensus (Romney, Weller, & Batchelder, 1986), and theories of categorization (e.g., Rosch et al., 1976) and cognition, such as bounded rationality (Kahneman, 2003; Simon, 1950, 1972). These types of approaches rely initially on ethnographic and psychologically informed approaches to eliciting mental models, such semi-structured interviews (e.g., Bruine de Bruin & Bostrom, 2013; Morgan et al., 2002) or think-aloud protocols (Ericsson & Simon, 1993). More structured surveys or tasks may be used in addition for convergent validation or to check the reliability of initial findings from such open-ended approaches.
In their discussion of how people behave in response to illness and how they might be regulating their behaviors, Leventhal et al. (1980) reference three principles governing behavior, each of which references how individuals understand objective events. The first principle is that both how individuals understand objective events and their emotions determine their goals for coping. The second is that these goals must be specified both with regard to the representation of danger and with regard to plans for action. The third is that very concrete information is central in these representations and plans (Leventhal et al., 1980, p 13). In contrast to this emphasis on the specific, much research on common-sense models of illness has in more recent years been conducted with the aid of a standardized survey tool, in particular the Illness Perception Questionnaire (Moss-Morris et al., 2002; Weinman, Petrie, Moss-Morris, & Horne, 1996). In this survey relatively abstract survey items are employed to assess concepts that are common across mental models of illness, such as (generic) beliefs about the consequences—that is, the risks—posed by an illness, as well as beliefs about what caused the illness and whether it can be controlled. Exemplifying the general nature of these questions is this item about consequences: “My illness is a serious condition.” Questions about personal control are expressed as generic self-efficacy statements, such as: “There is a lot which I can do to control my symptoms” (Moss-Morris et al., 2002). As is evident, the items do not reference specific symptoms or conditions.
The tension between more in-depth and substantive approaches to mental models, which assess the specifics of risk mental models, and generic survey approaches is captured by an exchange regarding the results of meta-analyses of research that largely employs versions of the illness perception questionnaire; some researchers interpreted the meta-analyses as demonstrating that the CSM is not useful for understanding adherence to medication. Phillips, Leventhal, and Burns (2017) wrote in their response to this interpretation:
To a great extent, the deficits of the Illness Perception Questionnaire were built into its design; it was created to generically assess illness representations. Thus the items meet psychometric criteria for internal consistency, by ignoring the unique biomedical processes and experiences associated with specific chronic conditions. Therefore, the Illness Perception Questionnaire will be adequate at best, in predicting what a general measure of illness representations should predict; it is not sufficient for predicting adherence to prescribed treatment for specific conditions. (p. 374)
This lack of specificity with regard to mental models and causal beliefs in research on health risks is also evident in other widely used surveys. For example, in the U.S. Health Information National Trends Survey (HINTS), while some specific questions about preventive activities were asked in 2005, questions about cancer causality beliefs in more recent HINTS surveys are generic: “It seems like everything causes cancer” and “Cancer is most often caused by a person's behavior or lifestyle” (Buster, You, Fouad, & Elmets, 2012; Cantor et al., 2009).
To better understand mental models of risk will require both more concerted efforts to understand the specific contents and structures of people’s beliefs and thinking as well as interdisciplinary research on the larger cultural, social, and physical contexts in which those beliefs are shaped and evolve and which they in turn influence (Atran, Medin, & Ross, 2005; NAS, 2017). For example, mental models can be culturally specific (e.g., Riley, 2014), especially when there is great uncertainty or crisis may be susceptible to or precipitate rumors (see, for example, the discussion of sense-making on social media in Starbird et al., 2016). Future research to examine the role of mental models in social sense-making processes will likely eventually be facilitated by improved technologies for voice recognition and translation and advances in analytical approaches to content analysis and natural language processing that enable investigation of specific mental models on a larger scale.
Support from the U.S. National Science Foundation (#1430781) for research informing this article is gratefully acknowledged.
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