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When I’m 64 Measuring Psychological Mechanisms Committee on Aging Frontiers in Social Psychology, Personality, and Adult Developmental Psychology INTRODUCTION Most social research on aging attempts to characterize behavioral patterns in a population. The bulk of this research relies on information provided by samples of individuals through surveys or interviews. Thus, the accuracy and quality of self-reported information are critical for the interpretation of social science findings in multiple disciplines. Psychological research has revealed problems with a heavy reliance on self-reported information and identified some solutions. In addition, psychologists have invested considerable efforts in developing methods that circumvent self-reports and reveal social and cognitive processes that may be out of conscious awareness. In doing so, psychological science has developed a number of measurement tools and analytic approaches that allow for relatively nuanced assessments of cognition, emotion, and behavior that are of use not only to psychologists, but also to sociologists, demographers, economists, and health professionals. In a nutshell, people are quite good at providing accurate information about concrete matters, like occupation or marital status. Self-reports are also well suited to assess explicit beliefs, like political opinions or values. However, psychological research has shown that survey responses can vary as a function of the context in which the questions are embedded and can even be influenced by extraneous—and seemingly innocuous—factors like the weather (Schwarz, this volume). Moreover, reliance on self-reported information presumes considered self-knowledge. In many cases, questions seek information that people do not have, even when they believe that they
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When I’m 64 do: why they exercise or don’t, whether they are racist or ageist, or whether they are more easily persuaded by certain kinds of messages than others. Thus, whereas self-reported information is well suited to answer certain questions, it is seriously limited in answering others. This paper briefly reviews some methodological and analytical approaches that hold significant promise for the field of aging research, including the measurement of implicit constructs and experience sampling. The measurement of change over time, which is essential to a deep understanding of aging, is also an area in which rapid progress is being made. This overview is followed by three short papers: two address measurement concerns and ways to minimize bias in self-reports; the third looks at the emerging use of neuroscience in social cognition research on aging. Breakthroughs in thought and theory often occur after improvements in measurement techniques and methodology are made; some of the latest developments discussed briefly here illustrate the potential for psychological research on aging. MEASURING IMPLICIT CONSTRUCTS Explicit constructs and processes are those that are subject to conscious awareness and direct self-report; implicit constructs and processes are not. There has been a recent explosion of interest in social psychology in the area of implicit social cognition, with the term being used in several different ways (see Petty, Wheeler, and Tormala, 2003). Some researchers are interested in implicit constructs such as attitudes, goals, and motives. For example, do people have evaluative predispositions of which they are unaware (e.g., an implicit attitude such as an unrecognized dislike of old people)? Other researchers are interested in implicit biases, in which people are perfectly aware of their attitudes or motives but do not know where they come from, or in implicit effects, in which people are aware of their attitudes or motives but do not know what effect those attitudes or motives have on their thoughts and actions. To examine implicit constructs and processes, social psychologists have developed a battery of implicit measures that do not call for conscious self-reports of the construct or process. The earliest such measures were in essence disguised self-reports (e.g., thematic apperception tests) or behavioral observations (e.g., how close one sat next to a stranger) from which researchers inferred an underlying attitude or motive. Recently, implicit measures based on reaction times have demonstrated considerable utility in predicting behaviors that could not be predicted by direct self-reports (e.g., Dovidio, Kawakami, and Gaertner, 2002). Furthermore, even when direct self-reports were useful in predicting behavior, implicit measures have been
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When I’m 64 shown to account for additional variance (e.g., Vargas, von Hippel, and Petty, 2004). Two measures have captured the bulk of recent research attention. One measure is based on priming procedures, which were developed initially by cognitive psychologists. With this measure, participants are presented with different stimuli (e.g., elderly or young faces—the primes) and then asked to indicate the evaluative meaning (i.e., good/bad) of various words (e.g., dirt or flower) as quickly as possible by pressing an appropriate response key on a computer. Reaction times for the classification of the words are assessed. To the extent that elderly faces facilitate responses to negative words or inhibit responses to positive words in comparison to young faces, one can infer that a negative attitude toward the elderly is automatically activated when the face appears (e.g., Fazio, Sanbonmatsu, Powell, and Kardes, 1996). The second measure is the implicit association test (Greenwald, McGhee, and Schwartz, 1998). It assesses the strength of association between a target concept (e.g., the elderly) and an attribute dimension (e.g., good/bad) by examining the speed with which participants can use two response keys to categorize words (or pictures) when each key is assigned a dual meaning (e.g., elderly/good versus young/bad). If people are classifying young (e.g., spring break) and old (e.g., retirement) terms or positive (e.g., flower) and negative (e.g., dirt) words, the question is whether it is easier to do so when elderly is paired with good or with bad on the response keys. The relative pattern of reaction times to the categorization task is informative with respect to whether the category of elderly is more closely associated with good or bad. Both the priming measure and the implicit association test have been used successfully in research on prejudice toward a wide variety of social groups (see recent reviews by Blair, 2002; Fazio and Olson, 2003). Interestingly, although implicit and explicit measures typically produce the same pattern of evaluations for common objects (e.g., ice cream), implicit measures for evaluations of prejudice have not correlated very well with more traditional explicit measures. There are at least two popular explanations for this. One relies on the idea that the implicit measures are accurate and the explicit measures are misleading. For example, a person might not want to report prejudice toward a minority group for fear of social rejection. A second possibility, however, is that both attitudes are valid (Wilson, Lindsey, and Schooler, 2000). Indeed, some research has demonstrated that each type of measure can predict different behaviors. Explicit measures tend to predict deliberative behaviors better than implicit measures, while implicit measures tend to predict spontaneous behaviors better than explicit ones (e.g., Dovidio, Kawakami, Johnson, Johnson, and
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When I’m 64 Howard, 1997). The same explanation applies for measures of implicit and explicit motives (e.g., McClelland, Koestner, and Weinberger, 1989). In addition to using implicit measures, social psychologists have also used various experimental procedures to invoke implicit processes. Perhaps the most notable in this regard is the work on priming. In this work, researchers attempt to subtly activate various concepts (e.g., stereotypes of the elderly) and examine their effects on behavior. For example, participants have been asked to unscramble sentences with elderly content (e.g., retired Florida Ted to) and have been exposed to a subliminal presentation of elderly-related words (e.g., wrinkled, retire; see Bargh and Chartrand, 2000, for a review). Subtly activating stereotypes—whether about oneself or others—can influence behavior (see Steele, Spencer, and Aronson, 2002; Wheeler and Petty, 2001). In one study, for example (Dijksterhuis, Aarts, Bargh, and van Knippenberg, 2000), researchers assessed the extent to which people automatically associated the elderly with forgetfulness. People who made a strong association between the elderly and forgetfulness showed memory impairment themselves when they were primed with the elderly category. Many challenges remain in this area. For example, measurement of implicit constructs in multigroup studies of culture, ethnicity, and race is an especially difficult methodological problem. Methods must be developed for conducting multigroup comparisons, contrasting results about implicit constructs both within and between group studies, improving the reliability and validity of existing measures, and promoting the development of new constructs for understanding culture, ethnicity, and race in older adults. EXPERIENCE SAMPLING Much of what is known about aging comes from surveys that assess constructs of interest by administering a single item (or a handful of items) in the form of questionnaires administered years apart. Data are usually characterized by age differences in measures of central tendencies (e.g., means, medians, etc.). Although the approach has generated important information about both stability and change over time (see reviews by Costa and McCrae, 1997; Ryff, Kwan, and Singer, 2001), recent research suggests that assessing daily experiences further strengthens predictions about social behavior and allows for closer examination of links between constructs and experiences (Almeida and Kessler, 1998; Kahneman, Krueger, Schkade, Schwarz, and Stone, 2004). However, there are at least two major types of intraindividual variability: within-person changes over relatively long periods of time that may or may not be reversible, such as those seen in development; and within-person changes that are more fluid, such as mood states (see Nesselroade,
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When I’m 64 2001). Experience sampling also allows for an examination of variability across a week, a day, or an even shorter interval. Experience-sampling approaches are found primarily in the research literatures on personality, emotion, and human abilities (see, e.g., Carstensen, Pasupathi, Mayr, and Nesselroade, 2000; Fleeson, 2001; Hultsch, MacDonald, and Dixon, 2002; Schwartz, Neale, Marco, Shiffman, and Stone, 1999). Variability within an individual across relatively short intervals, such as a day or a week, has proven to be a powerful predictor of such outcomes as divorce and mortality, among others. The study of intraindividual variability has contributed importantly to our understanding of personality and emotions and the ways in which they change over time. For example, variability in reports about self-concept across a week-long sampling period predicts the intensity with which people experience both positive and negative emotions (Charles and Pasupathi, 2003). From a developmental perspective, we have also learned that phenomena apparent at the intraindividual level may not necessarily be reflected in mean changes in performance across a group or even within the same individual across time. In the study by Charles and Pasupathi (2003), for example, there was less variability in older adults than in younger adults. Similarly, Mroczek and Spiro (2003) report individual differences in the rate of age-related change of the personality traits of extraversion and neuroticism. Studies of the relationship between personality and intraindividual variability in emotional experience found that emotional variability over time is related to withdrawal from life (Eaton and Funder, 2001; Larsen, 2000), and the same research also shows that each of three different aspects of emotional experience (valence, intraindividual variability, and rate of change) correlate with different, specific personality variables (extraversion, repression, and fearfulness and hostility toward others, respectively), and that these relationships are not consistent across gender. Eid and Diener (1999), demonstrated that intraindividual variability in affect is distinct enough to be considered a unique trait, separate from measures of neuroticism and other personality factors and that variations in intraindividual organization of behavior variation across situations is reflected in distinctive profiles of situation-behavior relationships (Shoda, Mischel, and Wright, 1994). Intraindividual variability appears to be a rich resource for behavioral prediction. For example, the lability of self-esteem predicts vulnerability to depression (Butler, Hokanson, and Flynn, 1994). As Nesselroade (2001) points out, intraindividual methodologies allow for the integration of idiographic (concerning discrete or unique facts or events) and nomothetic (concerning the discovery of scientific laws) emphases in the study of behavior. An experience-sampling study that included the full adult age range
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When I’m 64 found that older adults experienced more complex combinations of emotions than younger adults and were more likely to experience positive and negative emotions simultaneously (Carstensen et al., 2000). Complexity of experience was related to superior regulation of emotion. Thus, information garnered from repeated sampling provides a richer account of the strategies people use that result in better or poorer self-regulation. Certain types of intraindividual variability may also be predictors of cognitive dysfunction and even death. For example, Hultsch, MacDonald, Hunter, Levy-Bencheton, and Strauss (2000) found that greater individual variability on tests of several cognitive domains characterized those older adults with mild dementia. Eizenman, Nesselroade, Featherman, and Rowe (1997) found that within-person variation over weekly measurements of perceived locus of control predicted mortality status 5 years later. Progress in analytical approaches to intraindividual variability has been rapid in recent years (see Nesselroade and Ram, 2004), pointing to new ways to model individual growth (i.e., multilevel model or random effects model) (Eid and Diener, 1999; Mroczek and Spiro, 2003) by parsing average trends and isolating individual variability, and also pointing to novel ways to understand dynamic change (McArdle and Hamagami, 2004; Boker and Bisconti, 2005; Boker, Neale, and Rausch, 2004). SOCIAL NEUROSCIENCE The addition of brain imaging techniques to the methodological arsenal has greatly increased the power of measurement approaches in psychology. Jointly using brain and behavioral data, scientists have advanced the understanding of the specific processes involved in behavioral responses. There has been a growing interdisciplinary effort by social psychologists and cognitive neuroscientists to use methods such as functional brain imaging (e.g., positron emission tomography [PET], functional magnetic resonance imaging [fMRI], and magnetoencephalogram [MEG]) to study social cognition (Adolphs, 2003; Heatherton, Macrae, and Kelley, 2004; Ochsner and Lieberman, 2001). The cognitive neuroscience of aging has benefited enormously from these methods and the social neuroscience of aging is expected to do the same. Rapid progress is already being made in identifying the neural basis of social cognition in younger populations. There have been three special issues of neuroscience journals dedicated to social neuroscience, and the Journal of Cognitive Neuroscience has added the topic to its core publication mission. The field is likely to grow rapidly as the methods of cognitive neuroscience provide social psychologists with new tools to understand human nature. Researchers have identified a number of brain regions that support social capacities, such as recognition of faces and their emotional
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When I’m 64 expressions, theory of mind, social emotions (e.g., empathy), judgments of attractiveness, cooperation, and so forth. It is crucial both for social psychologists to recognize that brain mechanisms are involved in producing social behaviors, and for neuroscientists to appreciate that brains function in social contexts that fundamentally constrain behavior. For example, studies in social cognitive neuroscience demonstrate that the category of “people” is given privileged status by the brain as it processes objects in its environment: that is, there is a distinct functional neuroanatomy for semantic judgments made about people that is distinct from similar judgments made about other objects (Mitchell, Heatherton, and Macrae, 2002). Social brain science is providing new insights into long-standing questions regarding social behavior. One such question concerns the mechanisms that support the self-reference memory enhancement effect, in which information encoded with reference to the self is better remembered than information encoded with reference to other people. This question remained unresolved because the competing theories made identical behavioral predictions (i.e., better memory performance for material related to self). A resolution came about when Kelley and colleagues (2002), using fMRI, found that an area of the medial prefrontal cortex was uniquely associated with self-referential processing. This team of researchers subsequently found that activity in this region predicted whether or not people remembered traits they had processed with reference to self (Macrae, Kelley, and Heatherton, 2004). Another exciting area of research reveals that different brain regions are involved in the anticipation and the experience of reward. Reward anticipation is likely involved in drug addiction, gambling, and other tasks in which people work to achieve a desired state (Knutson and Peterson, 2005). Experiments of this type characterize the emerging field of neuroeconomics. Knutson, Westdorp, Kaiser, and Hommer (2000) have devised a very creative procedure, referred to as the monetary incentive delay task, in which participants “play a game” during which they are led to anticipate winning or losing money. Using fMRI technology, the researchers have examined activation in the nucleus accumbens as volunteers play the “game.” Their initial findings suggest that while the medial caudate is activated proportionally in anticipation of both rewards and punishments and their magnitudes, the ventral striatal nucleus accumbens is activated proportionately only with the amount of an anticipated reward, not with a punishment (Knutson, Adams, Fong, and Hommer, 2001). While most psychological and pharmaceutical treatments for psychopathology focus on dampening negative emotions, this work lays the foundation for ways to modify behavior by recruiting positive emotional systems. Very few studies have applied social neuroscience to questions about aging, but intriguing results have emerged from those that have. For ex-
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When I’m 64 ample, one study finds that in older adults brain regions involved in storing emotion memories respond little to negative images but are activated in response to positive images (Mather et al., 2004). Cognitive aging researchers have also demonstrated neural compensation: on tasks that place a significant demand on controlled and deliberative processes, older adults show bilateral frontal activation while younger people show laterality (Park, Polk, Mikels, Taylor, and Marshuetz, 2001; Reuter-Lorenz, 2002). The investigation of the role of neural plasticity in social tasks may be particularly interesting. The literature is replete with examples showing that older adults arrange their environment to be supportive, to recruit other cognitive resources, and to use others in such tasks as collaborative problem solving (Dixon, 2000). It appears also that compensation occurs at a neural level, but this requires more investigation. REFERENCES Adolphs, R. (2003). Cognitive neuroscience of human social behavior. Nature Reviews Neuroscience, 4, 165-178. Almeida, D.M., and Kessler, R.C. (1998). Everyday stressors and gender differences in daily distress. Journal of Personality and Social Psychology, 75(3), 670-680. Bargh, J.A., and Chartrand, T.L. (2000). The mind in the middle: A practical guide to priming and automaticity research. In H.T. Reiss and C.M. Judd (Eds.), Handbook of research methods in social and personality psychology. New York: Cambridge University Press. Blair, I. (2002). The malleability of automatic stereotypes and prejudice. Personality and Social Psychology Review, 6, 242-261. Boker, S.M., and Bisconti, T.L. (2005). Dynamical systems modeling in aging research. In C.S. Bergeman and S.M. Boker (Eds.), Quantitative methodology in aging research. Mahwah, NJ: Lawrence Erlbaum. Boker, S.M., Neale, M.C., and Rausch, J. (2004). Latent differential equation modeling with multivariate multi-occasion indicators. In K. van Montfort, H. Oud, and A. Satorra (Eds.), Recent developments on structural equation models: Theory and applications (pp. 151-174). Amsterdam: Kluwer. Butler, A.C., Hokanson, J.E., and Flynn, H.A. (1994). A comparison of self-esteem lability and low trait self-esteem as vulnerability factors for depression. Journal of Personality and Social Psychology, 66(1), 166-177. Carstensen, L.L., Pasupathi, M., Mayr, U., and Nesselroade, J.R. (2000). Emotional experience in everyday life across the adult life span. Journal of Personality and Social Psychology, 79(4), 644-655. Charles, S.T., and Pasupathi, M. (2003). Age-related patterns of variability in self-descriptions: Implications for everyday affective experience. Psychology and Aging, 18, 524-536. Costa, P.T., and McCrae, R.R. (1997). Longitudinal stability of adult personality. In R. Hogan, J.A. Johnson, and S.R. Briggs (Eds.), Handbook of personality psychology (pp. 269-290). San Diego, CA: Academic Press. Dijksterhuis, A., Aarts, H., Bargh, J.A., and van Knippenberg, A. (2000). On the relation between associative strength and automatic behavior. Journal of Experimental Social Psychology, 36(5), 531-544.
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