ENG 1101 TU Adventures of Huckleberry Finn Novel Discussion

ns and feelings in guiding decision making and behavior. Perhaps the most basic form of feeling is affect, the sense (not necessarily conscious) that something is good or bad. Affective responses occur rapidly and automatically – note how quickly you sense the feelings associated with the word “treasure” or the word “hate.” A large research literature in psychology documents the importance of affect in conveying meaning upon information and motivating behavior (Barrett & Salovey, 2002; Clark & Fiske, 1982; Forgas, 2000; Le Doux, 1996; Mowrer, 1960; Tomkins, 1962, 1963; Zajonc, 1980). Without affect, information lacks meaning and won’t be used in judgment and decision making (Loewenstein, Weber, Hsee, & Welch, 2001; Slovic, Finucane, Peters, & MacGregor, 2002). Affect plays a central role in what have come to be known as “dual-process theories” of thinking. As Seymour Epstein (1994) has observed: “There is no dearth of evidence in every day life that people apprehend reality in two fundamentally different ways, one variously labeled intuitive, automatic, natural, non-verbal, narrative, and experiential, and the other analytical, deliberative, verbal, and rational” (p. 710). Table 2, adapted from Epstein, further compares these two systems, which Stanovich and West (2000) labeled System 1 and System 2. One of the characteristics of the experiential system is its affective basis. Although analysis is certainly important in many decisionmaking circumstances, reliance on affect and emotion is generally a quicker, easier, and more efficient way to navigate in a complex, uncertain and sometimes dangerous world. Many theorists have given affect a direct and primary role in motivating behavior. Epstein’s (1994) view on this is as follows: The experiential system is assumed to be intimately associated with the experience of affect, . . . which refer[s] to subtle feelings of which people are often unaware. When a person responds to an emotionally significant event . . . The experiential system automatically searches its memory banks for related events, including their emotional accompaniments. . . . If the activated feelings are pleasant, they motivate actions and thoughts anticipated to reproduce the feelings. If the feelings are unpleasant, they motivate actions and thoughts anticipated to avoid the feelings. (p. 716) Underlying the role of affect in the experiential system is the importance of images, to which positive or negative feelings become attached. Images in this system include not only visual images, important as these may be, but words, sounds, smells, memories, and products of our imagination. In his Nobel Prize Address, Daniel Kahneman notes that the operating characteristics of System 1 are similar to those of human perceptual processes (Kahneman, 2003). He points out that one of the functions of System 2 is to monitor the quality of the intuitive impressions formed by System 1. Kahneman and Frederick (2002) suggest that this monitoring is typically rather lax and allows many intuitive judgments to be expressed in behavior, including some that are erroneous. This point has important implications that will be discussed later. In addition to positive and negative affect, more nuanced feelings such as empathy, sympathy, compassion, sadness, pity, and distress have been found to be critical for motivating people to help others (Coke, Batson, & McDavis, 1978; Eisenberg & Miller, 1987). As Batson (1990, p. 339) put it, “… considerable research suggests that we are more likely to help someone in need when we `feel for’ that person …” Table 2. Two modes of thinking: Comparison of experiential and analytic systems (adapted from Epstein, 1994, Copyright 1991, with permission from Guilford). System 1: Experiential System System 2: Analytic System Affective: pleasure-pain oriented Logical: reason oriented (what is sensible) Connections by association Connections by logical assessment Behavior mediated by feelings from past experiences Behavior mediated by conscious appraisal of events Encodes reality in images, metaphors, and narratives Encodes reality in abstract symbols, words, and numbers More rapid processing: oriented toward immediate action Slower processing: oriented toward delayed action Self-evidently valid: “experiencing is believing” Requires justification via logic and evidence One last important psychological element in this story is attention. Just as feelings are necessary for motivating helping, attention is necessary for feelings. Research shows that attention magnifies emotional responses to stimuli that are already emotionally charged (Fenske & Raymond, 2006; Villeumier, Armony, & Dolan, 2003). The psychological story can be summarized by the diagram in Figure 1. Research to be described in this paper demonstrates that imagery and feeling are lacking when large losses of life are represented simply as numbers or statistics. Other research shows that attention is greater for individuals and loses focus and intensity when targeted at groups of people (Hamilton & Sherman, 1996; Susskind, Maurer, Thakkar, Hamilton, & Sherman, 1999). The foibles of imagery and attention impact feelings in a manner that can help explain apathy toward genocide. Although the model sketched in Figure 1 could incorporate elements of System 1 thinking, System 2 thinking, or both, a careful analysis by Haidt (2001, p. 818; see also Hume, 1777/1960 for an earlier version of this argument) gives priority to System 1. Haidt argues that moral intuitions (akin to System 1) precede moral judgments. Specifically, he asserts that … moral intuition can be defined as the sudden appearance in consciousness of a moral judgment, including an affective valence (good-bad, like-dislike) without any conscious awareness of having gone through steps of searching, weighing evidence, or inferring a conclusion. Moral intuition is therefore … akin to aesthetic judgment. One sees or hears about a social event and one instantly feels approval or disapproval. Figure 1: Imagery and attention produce feelings that motivate helping behavior. 4 Affect, analysis, and the value of human lives How should we value the saving of human lives? If we believe that every human life is of equal value (a view likely endorsed by System 2 thinking), the value of saving N lives is N times the value of saving one life, as represented by the linear function in Figure 2. Figure 2: A normative model for valuing the saving of human lives. Every human life is of equal value. Figure 3: Another normative model: Large losses threaten the viability of the group or society (as with genocide). An argument can also be made for a model in which large losses of life are disproportionately more serious because they threaten the social fabric and viability of a community as depicted in Figure 3. How do we actually value humans lives? I shall present evidence in support of two descriptive models linked to affect and System 1 thinking that reflect values for lifesaving profoundly different from the normative models shown in Figures 1 and 2. Both of these models are instructive with regard to apathy toward genocide. 4.1 The psychophysical model Affect is a remarkable mechanism that enabled humans to survive the long course of evolution. Before there were sophisticated analytic tools such as probability theory, scientific risk assessment, and cost/benefit calculus, humans used their senses, honed by experience, to determine whether the animal lurking in the bushes was safe to approach or the murky water in the pond was safe to drink. Simply put, System 1 thinking evolved to protect individuals and their small family and community groups from present, visible, immediate dangers. This affective system did not evolve to help us respond to distant, mass murder. As a result, System 1 thinking responds to large-scale atrocities in ways that are less than desirable. Fundamental qualities of human behavior are, of course, recognized by others besides scientists. American writer Annie Dillard, cleverly demonstrates the limitation of our affective system as she seeks to help us understand the humanity of the Chinese nation: “There are 1,198,500,000 people alive now in China. To get a feel for what this means, simply take yourself – in all your singularity, importance, complexity, and love – and multiply by 1,198,500,000. See? Nothing to it” (Dillard, 1999, p. 47, italics added). We quickly recognize that Dillard is joking when she asserts “nothing to it.” We know, as she does, that we are incapable of feeling the humanity behind the number 1,198,500,000. The circuitry in our brain is not up to this task. This same incapacity is echoed by Nobel prize winning biochemist Albert Szent Gyorgi as he struggles to comprehend the possible consequences of nuclear war: “I am deeply moved if I see one man suffering and would risk my life for him. Then I talk impersonally about the possible pulverization of our big cities, with a hundred million dead. I am unable to multiply one man’s suffering by a hundred million.” There is considerable evidence that our affective responses and the resulting value we place on saving human lives may follow the same sort of “psychophysical function” that characterizes our diminished sensitivity to a wide range of perceptual and cognitive entities – brightness, loudness, heaviness, and money – as their underlying magnitudes increase. What psychological principles lie behind this insensitivity? In the 19 century, E. H. Weber and Gustav Fechner discovered a fundamental psychophysical principle that describes how we perceive changes in our environment. They found that people’s ability to detect changes in a physical stimulus rapidly decreases as the magnitude of the stimulus increases (Weber, 1834; Fechner, 1860). What is known today as “Weber’s law” states that in order for a change in a stimulus to become just noticeable, a fixed percentage must be added. Thus, perceived difference is a relative matter. To a small stimulus, only a small amount must be added to be noticeable. To a large stimulus, a large amount must be added. Fechner proposed a logarithmic law to model this nonlinear growth of sensation. Numerous empirical studies by S. S. Stevens (1975) have demonstrated that the growth of sensory magnitude () is best fit by a power function of the stimulus magnitude ,  = , where the exponent  is typically less than one for measurements of phenomena such as loudness, brightness, and even the value of money (Galanter, 1962). For example, if the exponent is 0.5 as it is in some studies of perceived brightness, a light that is four times the intensity of another light will be judged only twice as bright. Figure 4: A psychophysical model describing how the saving of human lives may actually be valued. Our cognitive and perceptual systems seem to be designed to sensitize us to small changes in our environment, possibly at the expense of making us less able to detect and respond to large changes. As the psychophysical research indicates, constant increases in the magnitude of a stimulus typically evoke smaller and smaller changes in response. Applying this principle to the valuing of human life suggests that a form of psychophysical numbing may result from our inability to appreciate losses of life as they become larger (see Figure 4). The function in Figure 4 represents a value structure in which the importance of saving one life is great when it is the first, or only, life saved, but diminishes marginally as the total number of lives saved increases. Thus, psychologically, the importance of saving one life is diminished against the background of a larger threat – we will likely not “feel” much different, nor value the difference, between saving 87 lives and saving 88, if these prospects are presented to us separately. Kahneman and Tversky (1979) have incorporated this psychophysical principle of decreasing sensitivity into prospect theory, a descriptive account of decision making under uncertainty. A major element of prospect theory is the value function, which relates subjective value to actual gains or losses. When applied to human lives, the value function implies that the subjective value of saving a specific number of lives is greater for a smaller tragedy than for a larger one. Fetherstonhaugh, Slovic, Johnson, and Friedrich (1997) documented this potential for diminished sensitivity to the value of life – i.e., “psychophysical numbing” – by evaluating people’s willingness to fund various lifesaving medical treatments. In a study involving a hypothetical grant funding agency, respondents were asked to indicate the number of lives a medical research institute would have to save to merit receipt of a $10 million grant. Nearly two-thirds of the respondents raised their minimum benefit requirements to warrant funding when there was a larger at-risk population, with a median value of 9,000 lives needing to be saved when 15,000 were at risk, compared to a median of 100,000 lives needing to be saved out of 290,000 at risk. By implication, respondents saw saving 9,000 lives in the “smaller” population as more valuable than saving ten times as many lives in the largest. Several other studies in the domain of life-saving interventions have documented similar psychophysical numbing or proportional reasoning effects (Baron, 1997; Bartels & Burnett, 2006; Fetherstonhaugh et al., 1997; Friedrich et al., 1999; Jenni & Loewenstein, 1997; Ubel et al., 2001). For example, Fetherstonhaugh et al. (1997) also found that people were less willing to send aid that would save 1500 lives in Rwandan refugee camps as the size of the camps’ at-risk population increased. Friedrich et al. (1999) found that people required more lives to be saved to justify mandatory antilock brakes on new cars when the alleged size of the at-risk pool (annual braking-related deaths) increased. These diverse strategies of lifesaving demonstrate that the proportion of lives saved often carries more weight than the number of lives saved when people evaluate interventions. Thus, extrapolating from Fetherstonhaugh et al., one would expect that, in separate evaluations, there would be more support for saving 80% of 100 lives at risk than for saving 20% of 1,000 lives at risk. This is consistent with an affective (System 1) account, in which the number of lives saved conveys little affect but the proportion saved carries much feeling: 80% is clearly “good” and 20% is “poor.” Figure 5: Airport safety study: Saving a percentage of 150 lives receives higher support ratings than does saving 150 lives. Note. Bars describe mean responses to the question, “How much would you support the proposed measure to purchase the new equipment?” The response scale ranged from 0 (would not support at all) to 20 (very strong support; Slovic et al., 2002). Slovic, Finucane, Peters, and MacGregor (2004), drawing upon the finding that proportions appear to convey more feeling than do numbers of lives, predicted (and found) that college students, in a between-groups design, would more strongly support an airport-safety measure expected to save 98% of 150 lives at risk than a measure expected to save 150 lives. Saving 150 lives is diffusely good, and therefore somewhat hard to evaluate, whereas saving 98% of something is clearly very good because it is so close to the upper bound on the percentage scale, and hence is highly weighted in the support judgment. Subsequent reduction of the percentage of 150 lives that would be saved to 95%, 90%, and 85% led to reduced support for the safety measure but each of these percentage conditions still garnered a higher mean level of support than did the Save 150 Lives Condition (Figure 5). This research on psychophysical numbing is important because it demonstrates that feelings necessary for motivating lifesaving actions are not congruent with the normative models in Figures 2 and 3. The nonlinearity displayed in Figure 4 is consistent with the disregard of incremental loss of life against a background of a large tragedy. However it does not fully explain the utter collapse of compassion represented by apathy toward genocide because it implies that the response to initial loss of life will be strong and maintained as the losses increase. Evidence for a second descr