Neural Components of Social Evaluation. William A. Cunningham 1, Marcia K. Johnson 1, J. Chris Gatenby 2, John C. Gore 12, & Mahzarin R.

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1 Social Evaluative Processing 1 Running Head: SOCIAL EVALUATIVE PROCESSING Neural Components of Social Evaluation William A. Cunningham 1, Marcia K. Johnson 1, J. Chris Gatenby 2, John C. Gore 12, & Mahzarin R. Banaji 3 1 Yale University, Department of Psychology 2 Yale Medical School, Department of Radiology 3 Harvard University, Department of Psychology William A. Cunningham Department of Psychology Yale University P.O. Box New Haven, CT william.cunningham@yale.edu

2 Social Evaluative Processing 2 Abstract Evaluative responses judgments along a good-bad dimension involve two seemingly dissociated processes: one that is relatively automatic and another that is more consciously controlled. In two studies, we used functional magnetic resonance imaging (fmri) to investigate the different brain systems that are associated with controlled relative to automatic evaluative processing and their relationship to each other. Participants made either evaluative (good-bad) or nonevaluative (past-present) judgments about famous names (e.g., Adolph Hitler, O.J. Simpson, George Washington, Bill Cosby). Evaluative processing resulted in greater medial and ventrolateral prefrontal activity than nonevaluative processing. Furthermore, ventrolateral prefrontal activity was greater for evaluations where participants were ambivalent. Regarding the automatic component, greater amygdala activity was observed for names rated as bad than those rated as good whether the task directly involved an evaluative judgment (good/bad) or not (past/present). Together, these findings provide evidence for (a) the idea of automatic evaluation, and the role of the amygdala in producing it, (b) the involvement of the medial prefrontal cortex in explicit evaluative judgments, and (c) the role of ventrolateral prefrontal cortex in processing evaluative ambivalence. Together, these findings help clarify the neural correlates of automatic and controlled evaluative processing. Evidence for the interaction between automatic and controlled evaluative processing is also discussed.

3 Social Evaluative Processing 3 Neural Components of Social Evaluation Arguably, among the most important cognitive processes are those involved in the constant evaluation of our physical and social environment, including inanimate objects, persons, and events. This seemingly ordinary act of assigning valence good and bad is crucial for survival, guiding behavior toward or away from an immediate significant object, or through the anticipation of future rewards and punishments in the development of goals. It is not surprising, then, that brain systems that underlie these processes have received considerable attention (see LeDoux, 1996). Yet, despite this interest, little attention has been devoted to decomposing neural activity into subcomponents of automatic and controlled evaluative judgment. Although the social cognitive conceptualization of attitudes has increasingly emphasized the dissociation of automatic and controlled evaluative processing, only recently have brain mechanisms involved in producing an evaluation begun to be investigated along the automatic-controlled dimension (see Cacioppo, Berntson, & Crites, 1996; LeDoux, 1996). In particular, although some brain regions have been associated with automatic evaluation, little attention has been devoted to the brain regions that may be involved in controlled evaluation, when people consciously reflect about whether a target is good or bad, friend or foe, to be approached or avoided. In this research, by manipulating the nature of judgments (evaluative or nonevaluative) performed on stimuli that clearly differ in valence, we examine the brain processes involved in controlled evaluation and contrast these with those involved in automatic aspects of evaluation. The Social Cognition of Evaluation The study of affect and emotion has always been an integral part of the study of social behavior. As early as 1935, Gordon Allport wrote about attitude as the most distinctive and important concept in American social psychology. Later, in their classic work on meaning in everyday language, Osgood, Suci, and Tannenbaum (1953) showed the importance of the evaluative dimension. Specifically, they demonstrated that evaluation explained most of the variance in ratings of word meaning, concluding that an evaluative factor is first in magnitude and in order of appearance (pg 44).

4 Social Evaluative Processing 4 More recently, indirect methods to tap evaluation have led to the discovery that evaluations can occur automatically, even in the absence of conscious intention or awareness. Building on latency-based semantic priming procedures, Fazio, Sanbonatsu, Powell, and Kardes (1986) found that binary good-bad responses to stimuli with strong positive and negative connotations (e.g., vomit, rainbow) were facilitated when preceded by brief presentations of evaluatively congruent stimuli. Participants were able to more quickly indicate that a target word (such as beautiful ) had a good meaning when preceded by a prime word with good meaning (such as triumph ) compared with a prime with bad meaning. Similarly, brief presentations of evaluatively negative words speeded up responses to subsequent negative words. This effect occurs even when the prime and target are not semantically associated, strongly suggesting an automatic evaluation of information. Extending these findings, Bargh Chaiken, Govender, and Pratto (1992) demonstrated the same evaluative facilitation effect for a task that required no explicit evaluation. Instead of having participants make evaluative (good-bad) judgments about target stimuli, Bargh et al. (1992) asked participants to pronounce evaluatively laden words that were preceded by evaluatively congruent or incongruent primes. Even when only pronouncing words, Bargh et al. (1992) found that the evaluative congruency of prime and target facilitated participants responses. Lastly, the automaticity of evaluation has been demonstrated in studies in which similar effects have been produced using subliminal primes (Draine & Greenwald, 1998) that more securely escape conscious reflection. It is of interest that another type of evaluation, evaluations that are consciously constructed, may not be based on the same representations as those that are activated automatically from the environment. Current models of attitude and the cognition-emotion relation more generally (Devine, 1989; Greenwald & Banaji, 1995; Johnson & Multhaup, 1992) propose that there are at least two dissociated systems that constitute the processes of evaluation. Evaluative/affective responses and experience are a function of both automatic/perceptual and controlled/reflective cognitive processes. Perceptual emotional processes are more primitive; they are relatively automatic responses to stimuli in the immediate environment. Reflective emotion is internally generated, typically involving controlled processing, and can operate in the

5 Social Evaluative Processing 5 absence of immediate stimuli. Evidence for dissociations in evaluative processing comes from numerous studies investigating attitudes toward social groups and individual members of those groups. When attitudes toward social groups are measured with both self-report measures and latency based measures, self-reported attitudes toward certain social groups (e.g., African- Americans, the elderly, foreigners) are substantially more positive than are found on the latency based measures (Cunningham, Nezlek, & Banaji, 2002; Devine, 1989; Fazio, Jackson, Dunton, & Williams, 1995; Rudman, Greenwald, Mellot, & Schwartz, 1999). The Neuroscience of Evaluation Typically explored in the context of fear conditioning, numerous animal studies have shown that the amygdala a small structure found in both hemispheres of the medial temporal lobe is critical for learning to pair negative outcomes with conditioned stimuli and the expression of such associations (see LeDoux, 2000 for a review). Recent work using neuroimaging has replicated the finding of a link between amygdala function and fear conditioning in humans (LaBar, Gatenby, Gore, LeDoux, & Phelps, 1998; Phelps, et al., 2001). Interestingly, it appears that the amygdala is not only involved in associative learning of rewards and punishments, but also in more abstract, conceptual representations of fear and threat. For instance, Phelps et al. (2001) told participants that they might receive mild shocks when particular visual stimuli were presented. Although no actual shock was administered, Phelps et al. found amygdala activity to stimuli verbally suggested to be threatening. Even more generally, amygdala activation was found to presented words with threatening connotations compared with neutral words (Isenberg, et al., 1999). Besides fear conditioning, the amygdala also appears to be involved in more general evaluation and valence detection. Imaging data have shown that the amygdala responds more strongly to unpleasant odors (Zald & Pardo, 1997), tastes (Zald, Lee, Fluegal, & Pardo, 1998), emotional pictures (Morris, et al., 1996), and words (Tabert et al., 2001) than to corresponding pleasant or neutral items. Further, patients with bilateral amygdala damage have difficulty judging emotional expressions, especially negative emotions such as fear, anger, and disgust (Adolphs, et al., 1999; Calder, Kreane, Manes, Antoun, & Young, 2000).

6 Social Evaluative Processing 6 Current neuroimaging work suggests that the amygdala is involved in the more automatic processes of evaluation. For example, conscious awareness of a valenced stimulus does not appear to be necessary to produce amygdala activation. In conceptual replications of previous research on supraliminal emotional face processing, Morris, Ohman, and Dolan (1998) and Whalen, et al. (1998) demonstrated that subliminal presentations of emotional faces (angry and fearful, respectively) led to significant amygdala activation. Moreover, in a study of automatic race bias, Phelps et al. (2000) demonstrated that automatic behavioral measures of racial evaluation (i.e., the Implicit Association Test and startle eye blink) correlated with amygdala activation to Black faces relative to White faces, whereas a self-report measure of racial bias (i.e., the Modern Racism Scale) did not. In contrast, the amygdala does not appear to be involved in other, more controlled, aspects of evaluation. For instance, while patients with bilateral amygdala damage show deficits in automatic visceral fear conditioning, they are able to accurately report the valence of objects (Adolphs et al., 1999; Bechara et al., 1995). Thus non-amygdala processes allow patients with amygdala damage to learn about, judge, evaluate, and report valence. Although less understood, several prefrontal regions appear to play a role in these additional affective/evaluative processes (Damasio, 1994). At a general level, it appears that the prefrontal cortex (PFC) is involved in higher-order cognition, involving executive control, deliberative, conscious, or reflective processing (e.g., Stuss & Benson, 1984). Structures in the PFC are necessary to learn complex associations, and may be necessary for an awareness of emotional feeling states (Lane, et al., 1998). Neuroimaging experiments in which participants are asked to reflectively generate (Teasdale, et al., 1999), monitor (Henson, Rugg, Shallice, Josephs, & Dolan, 1999), anticipate (Porro, et al., 2002), attribute (Paradiso, et al., 1999) or report on (Gusnard, Akbudak, Shilman, & Raichle, 2001) emotional states typically show heightened prefrontal, especially medial prefrontal, activation. Prefrontal contributions to affective evaluation likely allow for flexible evaluations that can integrate information regarding current memories for past events, anticipation of future events (including rewards and punishments), context, higher order motivations, and personal

7 Social Evaluative Processing 7 values with information provided by more automatic perceptual evaluative systems (e.g., Johnson & Multhaup, 1992). Moreover, these systems likely are necessary when evaluations of objects must be updated as new information is learned. Patients with damage to the ventromedial prefrontal cortex, although normal on many standard cognitive neuropsychological tests, have difficulty with tasks that require higher order affective processing. For instance, patients have difficulty organizing future goal directed behavior, have diminished capacity to respond to punishment, present unrealistically positive evaluations of self, and display inappropriate emotional reactions (Damasio, Tranel, & Damasio, 1990). 1 An Integration Research investigating the neural systems involved in evaluation has either been restricted to explorations of the automatic components of evaluation, or has not explicitly tried to disentangle the automatic from the controlled processes of evaluation and attitude. However, one line of research supports the idea that the amygdala and other subcortical structures are likely to be involved in automatic, unconscious evaluative processing and some of the prefrontal contributions to evaluation may reflect more deliberate and controlled processing. Using a gambling task, Bechara, Damasio, Damasio, and Lee (1999) demonstrated that damage to the amygdala resulted in a lack of skin conductance when receiving a reward or punishment, or while selecting stimuli with positive or negative consequences. In contrast, damage to the ventromedial prefrontal cortex resulted in a lack of skin conductance only when selecting stimuli that have positive or negative consequences. This result suggests that the ventromedial prefrontal cortex is involved in the anticipatory function of evaluation. That is, whereas the amygdala is involved in the reaction to the valence of perceptual stimuli, the ventromedial prefrontal cortex may be involved in reflective processes of evaluation, especially when immediate rewards and punishments are not present. For the most part, research examining evaluative processing has relied on stimulus driven designs, where the neural activity to distinct positive versus negative stimuli is compared. A key weakness of such designs is that they do not allow investigators to distinguish the extent to which automatic and controlled processes are involved in judgments of the stimuli. Without

8 Social Evaluative Processing 8 manipulating evaluative strategies it is not possible to discern whether the differences in activity reflect intentional evaluative processes under the participants conscious control or automatic evaluative processes that would have occurred regardless of task. In the one fmri study (Zyseet, Huber, Ferstl, & von Cramon, 2002) that compared the activity during controlled evaluative versus nonevaluative judgments, the stimuli used were not identical between conditions. When the same stimuli do not serve in both conditions and are therefore not equated, it is unclear whether the effects obtained reflect processes of controlled evaluation, or differences in other processes (such as automatic evaluation) that might be elicited by the differences inherent in stimuli. Using ERP, Crites and Cacioppo (1998) compared evaluative (good-bad) and nonevaluative (vegetable or not) judgments of identical stimuli and found difference signals for the tasks, suggesting that different neural components are involved in explicit evaluative compared with nonevaluative judgments when stimuli are held constant. The present studies attempt to systematically distinguish brain activity associated with deliberate, intentional social evaluative judgment from those associated with more automatic social evaluative judgment. In two studies, we used identical stimuli for evaluative and nonevaluative judgment conditions: famous names (e.g. Adolph Hilter, Bill Cosby) were selected for stimuli that could easily either be judged to be good or bad (evaluative judgment) or a historical or present day figure (past or present, nonevaluative judgment). By holding stimuli constant and by using tasks that require differential operations to be performed on them evaluative and non-evaluative brain activation associated with the automatic component of evaluation should be observed in both the evaluative and nonevaluative conditions. Thus, differences in these conditions primarily reflect differences in controlled evaluative processing. Moreover, using logic similar to Bargh et al. (1992), we can also examine automatic evaluative processing. Using a nonevaluative task allows us to see whether differences in activation are associated with good and bad stimuli even when participants are not deliberately engaged in evaluation. Brain activity reflecting the difference in processing good versus bad when consciously evaluating and especially when not is taken as activity associated with relatively automatic evaluation.

9 Social Evaluative Processing 9 In sum, the present research was designed to answer four primary questions. First, what brain systems are involved in conscious, deliberate acts of evaluation? Second, when examining the activity associated with the processing of valence, how are these areas modulated by task? Presumably, areas of activation that differentiate good and bad items regardless of task represent more automatic aspects of evaluation. Third, can the act of controlled evaluation influence areas of activity, such as the amygdala, that are thought to be involved in the automatic processing of evaluation? Finally, can neural markers of behavioral ambivalence in evaluation (i.e., high positive and negative evaluation of the same target) be detected? Methods Two experiments were conducted to examine the neural correlates of automatic and controlled social evaluation. Because these studies use new stimuli/task combinations and because the above questions have not been directly answered in prior research, we conducted two experiments that largely replicated each other. The main distinction was the presentation order of judgments. In the first experiment, participants responded to stimuli in blocks of similar judgment types (several evaluative or non-evaluative trials in succession). In the second experiment, judgments were randomly ordered using an event-related design. Because the two experiments are close replications of one another, with the second providing additional information not available in the first, the methods and results of both will be presented jointly. Both studies were approved by the Human Investigation Committee of the Yale Medical School. Study 1: Blocked Conditions To investigate the neural substrates underlying controlled evaluative relative to nonevaluative processing, fourteen participants were shown a series of famous names during fmri. In the evaluative condition, participants indicated whether names referred to good or bad people. In the non-evaluative condition, participants indicated whether names referred to historical figures (past) or current (present) figures. For example, Adolph Hilter would be classified by most if not all participants as bad in the evaluation task and as past in the nonevaluative task. Likewise, Bill Cosby would be classified by most people as good in the evaluative task and as present in the non-evaluative task.

10 Social Evaluative Processing 10 Participants Fourteen right-handed participants (mean age: 26 years; 8 females) consented to participate and were paid for their participation. Participants reported no abnormal neurological history and had normal or corrected to normal vision. fmri Parameters All imaging was conducted with a GE 1.5T (General Electric Medical Systems Signa, Milwaukee, Wisconsin) scanner. To cover the frontal lobe and the majority of the temporal and parietal lobes, fourteen parallel coronal slices (slice thickness: 6mm, skip = 2 mm) were prescribed perpendicular to the AC-PC line, with the fourth slice centered on the amygdala. Functional images were acquired from posterior to anterior using an interleaved single shot gradient echoplanar pulse sequence (TE = 60ms, TR = 1.5s, in-plane resolution = x mm, matrix size = 64 x 64, and FOV = 20 x 20 cm). Procedure During fmri, participants categorized famous names along one of two dimensions (good-bad or past-present) and indicated their categorization by making one of two button presses with their right hand. Using Psyscope for Macintosh (Cohen, MacWhinney, Flatt, & Provost, 1993), stimuli were forward projected using an LCD and overhead projector onto a clear screen at the base of the MRI bore. A prism mirror positioned over the participants eyes allowed them to view stimuli. All stimuli were presented in black letters against a white background. Each block began with either the cue good-bad, or past-present for 3 seconds, followed by a series of eight randomly presented names. Both first and last names were presented (see Appendix A for a complete list of stimuli). Each name appeared for 2 seconds, and was followed by a blank screen for 1 second. Participants were instructed to respond as quickly as possible, and before the next stimulus appeared. The 60 names used in the study were repeated in both conditions so that participants made a good-bad and past-present judgment on each name. Each run consisted of six of each block type, with block type randomly ordered. To synchronize stimulus presentations with functional scanning, all trials were initiated by a trigger sent by the MRI scanner at the beginning of each scan.

11 Social Evaluative Processing 11 Preprocessing Data were corrected for slice acquisition time and motion using SPM99, then coregistered to in-plane anatomical images and transformed to conform to the SPM99 default T1 MNI brain interpolated to 3x6x3 mm. Functional data were smoothed using a 9mm FWHM (fullwidth-half-maximum) kernel, and a high pass filter removed effects of scanner draft. Study 2: Event Related (Randomized Conditions) To better understand the processes involved in evaluative and nonevaluative judgments, Study 2 conceptually replicated Study 1 using a rapid event-related design. This design allowed us to randomly present trial types, and importantly to generate time courses of the neural activity for good and bad stimuli under evaluative and nonevaluative task processing. Participants Fifteen participants were paid for their participation. Participants reported no abnormal neurological history and had normal or corrected to normal vision. All participants provided informed consent. One participant was excluded for head motion greater than 2mm, and two additional participants were excluded for knowing the identity of fewer than 70% of the famous names presented. Twelve participants (mean age: 22 years; 6 females) remained for analysis. fmri Parameters To get whole brain functional coverage, twenty-six parallel axial slices (slice thickness: 4.5mm, no skip) were prescribed parallel to the AC-PC line, with the eleventh slice centered on the AC-PC line. Functional images were acquired from inferior to superior using a single shot gradient echoplanar pulse sequence (TE = 30ms, TR = 2s, in-plane resolution = 3.75 x 3.75 mm, matrix size = 64 x 64, and FOV = 24 x 24 cm). Procedure As in Study 1, participants were presented with a series of famous names and made either good-bad or past-present judgments during fmri. Stimulus presentation and response collection were as in Study 1, except that stimuli were presented in white letters against a black background. For each trial, a cue indicated whether the trial required a good-bad or a pastpresent response for 500ms, immediately followed by a famous name for 1.5 seconds. Between

12 Social Evaluative Processing 12 trials, a fixation cross remained on the screen for two to six seconds to space out trials. To synchronize stimulus presentations with functional scanning, all trials were initiated by a trigger sent by the MRI scanner at the beginning of each scan. Each of four runs contained 24 of each trial type (evaluative task: bad names, evaluative task: good names, nonevaluative task: bad names, nonevaluative task: good names). Because good-bad evaluations of target names are inherently subjective, after scanning, participants completed a questionnaire in which all names were rated along four dimensions. Participants indicated on a 0 to 9 scale the extent to which the target names represented good or bad individuals. Separate ratings of good and bad were obtained so that a measure of ambivalence could be obtained. In addition, they rated their degree of knowledge about each target person. Finally, participants indicated whether the person was a historical (past) or current (present day) figure. These ratings were used for an ideographic sorting the fmri data. An analysis of participant responses indicated a high degree of evaluative (good-bad) inter-rater consistency (α =.98). With this high degree of consistency, analyses using normative instead of idiographic ratings yielded nearly identical results. Preprocessing Data were corrected for slice acquisition time and motion using SPM99, then transformed to conform to the SPM99 default EPI MNI brain interpolated to 4x4x4 mm. Functional data were smoothed using a 8mm FWHM (full-width-half-maximum) kernel, and a high pass filter removed effects of scanner drift. To additionally condition the data, a low pass filter removed frequencies greater than 17Hz, a cutoff that represents the frequency after which hemodynamic signals as a function of experimental effects are no longer expected. Results and Discussion Data Analysis Study 1 Data in both studies were analyzed using the general linear model as implemented by SPM99 (Friston et al., 1995). To examine brain activity associated with evaluative (good-bad) judgments relative to nonevaluative (past-present) judgments, we constructed regressors that corresponded to the expected BOLD signal for the two condition types. Contrast t-maps for the

13 Social Evaluative Processing 13 two conditions were generated for each participant by comparing the estimated hemodynamic signal for the evaluative and nonevaluative conditions. The contrasts tested (a) greater activity for evaluative than nonevaluative judgments, and (b) greater activity for nonevaluative than evaluative judgments. Random effects composite t-maps were generated by averaging these individual participant t-maps. Regions of activation were defined as those areas in which 10 contiguous voxels were significant at p <.005 (uncorrected). Data Analysis Study 2 As in Study 1, a series of regressors 2 were constructed to examine brain activity to each of the four trial types: evaluative-bad, evaluative-good, nonevaluative-bad, nonevaluative-good. Whether a particular name was assigned as a good or bad trial was determined by the rating participants made after scanning. 3 Trials in which a participant did not know the person were dropped in these analyses. On average, fewer than 10% of names were dropped for any given participant. Two sets of contrast t-maps were generated for each participant by comparing the estimated hemodynamic signal for (a) the evaluative versus nonevaluative conditions, and (b) the good versus bad names. The first set of contrasts tested for task related differences that is, activity associated with the deliberate act of evaluation (or the converse). The second set tested for areas of activation that responded to the valence of the stimuli, regardless of task goals. Presumably, this second set of contrasts reflect relatively more automatic evaluative processing. Random effects composite t-maps were generated by averaging these individual participant t- maps. Regions of activation were defined as those areas in which 10 contiguous voxels were significant at p <.005 (uncorrected), or a priori regions identified in Study 1 with 5 contiguous voxels at p <.01 (uncorrected). Using the idiosyncratic ratings of names provided by participants, we conducted a second series of analyses. For some names, participants simultaneously indicated both positivity and negativity on the independent post scan ratings of good and bad (on 0 to 9 scales), a psychological state known as attitudinal ambivalence (Priester & Petty, 1996). For the purposes of analysis, we defined ambivalent names individually for each participant as names that exceeded a cutoff of 15 when the good rating was multiplied by the bad rating. Compared with

14 Social Evaluative Processing 14 the agreement ratings found for the evaluation ratings, the ambivalence ratings were more variable (α =.67). The BOLD effect for each of the four effects of interest (good-bad judgments for ambivalent names, good-bad judgments for nonambivalent names, past-present judgments of ambivalent names, and past-present judgments of nonambivalent names) were modeled with three regressors for each participant: a canonical hemodynamic response, and then two derivatives to model the onset and duration of the BOLD response. Random effect t-maps were generated using individual participant t-maps as input. Time lines for neural activity were generated using the SPM ROI toolbox (Poldrack et al., Regions of activation were defined functionally from each of the contrast images. Time lines were estimated for each of the four conditions for each of the functionally defined regions of interest. Time lines for each condition were adjusted for the residual effects of the other conditions and were subjected to a high pass filter to account for scanner drift. Analyses of response latencies indicated that participants responded more quickly during good-bad than past-present trials (1856ms vs. 1921ms), F(1,11) = 6.04, p <.05, and more quickly for good names than for bad names (1849ms vs.1927ms), F(1,11) = 9.32, p <.05. Response latencies for names rated ambivalently in the good-bad condition were found to be significantly longer than those rated as nonambivalent (2056ms vs. 1843ms), a pattern that was not observed for trials in the past-present condition (1996ms vs. 1950ms), F(1,9) = 20.48, p < Controlled Evaluation A primary contrast of interest was the evaluative > nonevaluative contrast, the contrast that compares controlled evaluative processing to controlled nonevaluative processing. As noted in the introduction, previous research has used different stimuli in the evaluative and nonevaluative conditions, making it difficult to determine whether obtained results are the function of automatic or controlled evaluation. As can be seen in Tables 1 and 2, several areas of activation differentiated evaluative from non-evaluative judgments. We will limit our discussion

15 Social Evaluative Processing 15 to three theoretically relevant regions that replicated across both studies: the medial prefrontal cortex, the ventrolateral prefrontal cortex, and the amygdala. Medial Prefrontal Cortex (mpfc): Whether examining the extent of activity, or the statistical significance of activity, the most dramatic difference between evaluative and nonevaluative processing in both studies occurred in the medial prefrontal cortex (Figure 1, p <.001, corrected). As can be seen in the time lines from Study 2 presented in Figure 1b, evaluative judgments resulted in increases in activity, whereas nonevaluative judgments resulted in significant decreases in activity in mpfc. Defining fixation trials as baseline mental activity, these activations can be understood as increases in processing relative to a default mental state, and deactivation as decreases in processing relative to a default mental state (see Gusnard et al., 2001). In a recent meta-analysis on the neuroimaging of emotion, Phan, Wager, Taylor, and Liberzon (2002) found that the medial prefrontal cortex was involved in many aspects of affective processing, regardless of the valence and sensory modality of the triggering stimulus. Interestingly though, the greatest percentage of studies finding medial prefrontal activation were those in which stimuli were imagined compared with those in which stimuli were presented (nearly 70% for reflectively generated emotion, compared with about 45% for perceptually generated emotion). Further evidence for a reflective rather than perceptual role of mpfc in affect/evaluation comes from work examining anticipatory anxiety (Simpson, Drevets, Snyder, Gusnard, & Raichle, 2001). Anxiety can be contrasted from fear in that fear often is exhibited in the presence of a threatening stimulus, whereas anxiety is exhibited in the anticipation of a threatening stimulus. That reflective processing activates this region more so than perceptual processing further implicates this region in controlled aspects of emotion and evaluation. It is unclear whether the activations observed in medial prefrontal cortex are specific to social evaluations, or whether they occur during evaluation regardless of domain. A review of imaging studies in which mpfc activation has been found indicates that these studies share a feature in addition to reflective aspects of emotion. Studies that require reflective social processing, broadly defined, also appear to be associated with mpfc activity. Most notably,

16 Social Evaluative Processing 16 tasks that require making attributions of beliefs, intentions, and goals of others (Castelli et al., 2000; Gallagher et al., 2000; Goel et al., 1995) or self (S. C. Johnson et al. 2002; Kelley et al., 2000) have been shown to activate the mpfc relative to control tasks. Ventrolateral PFC (vlpfc): In addition to areas in the medial prefrontal cortex, areas of ventrolateral prefrontal cortex showed more activity for evaluative than nonevaluative judgments. As can be seen in Figures 2a and 2b, activation for the good-bad trials was significantly greater than for the past-present trials in vlpfc, a difference that could reflect memory selection. In memory retrieval and judgment, relevant information needs to be separated from irrelevant information. Consistent with a proposed inhibitory function of the prefrontal cortex (e.g., Hasher & Zacks, 1988; Shimamura, 1995), Thompson-Shill, D'Esposito, Aguirre, and Farah (1997) suggested that the vlpfc is involved in inhibiting irrelevant information during memory tasks. In the context of the present evaluative and nonevaluative tasks, the evaluative task may require more selection, or at least more complex selection, than does the nonevaluative task. For example, information regarding both good and bad behaviors of former President Bill Clinton may be stored in memory, and this information needs to be selected and weighed to make a binary good-bad judgment. Although both the evaluative and nonevaluative tasks require semantic processing to identify the stimulus person and activate relevant information to make a judgment, the evidence for past or present may involve fewer conflicting cues than the evidence for good or bad. If it is true that the ventrolateral prefrontal activation observed for the evaluative trials is a function of resolving multiple memory representations in evaluation, or the online weighing of evaluative information, then we should expect the greatest ventrolateral PFC activation to occur for trials in which the most ambivalent or evaluatively opposing judgments are made. Here, these ambivalent judgments would be ones in response to target names that represent those individuals who elicit both positive and negative evaluation. Fortunately, from the ratings obtained from each participant on independent good and bad dimensions, we can compute the subset of stimulus individuals who scored high on both positive and negative evaluation, to be compared to those who rated at one or the other extreme on the two

17 Social Evaluative Processing 17 dimensions.. As an example, names like Bill Clinton and Yassar Arafat fell into the high ambivalent category, whereas Adolf Hilter (only negative) and Mahatma Gandhi (only positive) fell into the unambivalent category. To test this prediction, we compared the activity for high ambivalent names relative to unambivalent names in both the good-bad and past-present judgment conditions. As can be seen in Figure 2c, activity in ventrolateral prefrontal cortex was indeed greatest for ambivalent names in the good-bad evaluative task. No difference was observed for the pastpresent nonevaluative task. This finding reinforces the idea that controlled evaluations are formed online from available memory representations, and that to form an evaluation, a weighing process assesses this information for relevance. For evaluative judgments, when there is little evaluative conflict, less of this processing is needed to form a judgment. Automatic Evaluation Amygdala: In previous work, the amygdala has been shown to be more sensitive to negative than positive information (Iseberg et al., 1999; Morris et al., 1996), though activation has been observed to positive information relative to neutral information as well (Hamann & Mao, 2002). As can be seen in the timelines from Study 2 presented in Figure 3b, we demonstrate both findings. Collapsing across tasks, left amygdala activity was observed for both good and bad names, but greater left amygdala activity was observed for bad names than good names. Importantly, as can be seen in Figure 3c, this difference between good and bad names was observed in both the good-bad (p <.05) and the past-present (p <.01) tasks. In other words, amygdala activation appeared whether or not participants consciously attended to the evaluative dimension, lending support to the idea that the amygdala is involved in the automatic processing of evaluation. Interestingly, our data suggest that consciously focusing on the nonevaluative aspects of a person may reduce amygdala activation, but only after several judgments have been made. Although we found no effect of judgment type in Study 2, we found greater left amygdala activity for evaluative blocks of trials than nonevaluative blocks of trials in Study 1. This apparent discrepancy can be resolved by plotting the average time courses of the amygdala for

18 Social Evaluative Processing 18 both the evaluative and nonevaluative blocks of Study 1. As can be seen in Figure 3a, for the first half of each block type, evaluative and nonevaluative judgments showed identical activation. It was only in the second half of the block that evaluative and nonevaluative blocks differed. Specifically, whereas the nonevaluative blocks showed the standard amygdala habituation effect (Breiter et al., 1996; Phelps et al., 2001; Wright et al., 2001), no such habituation was found for the evaluative blocks. Attention to nonevaluative aspects of stimuli may result in habituation whereas attention to evaluative aspects may prolong (and perhaps even increase) affective responses. In short, the amygdala is involved in the automatic processing of valence, but this automatic processing may not be impervious to conscious control. Nonevaluative Processing Although not the main focus of this paper, it is relevant to note that several regions showed greater activation in response to the nonevaluative compared to the evaluative task. That is, non-evaluative judgments did not simply produce lower activation than evaluative judgments. Most notably, several areas of dorsolateral prefrontal and parietal cortex showed greater activity associated with nonevaluative than evaluative judgments, areas that are associated with memory processing. It should be noted that the differences in these regions reflect differences in activation. That is, both evaluative and nonevaluative judgments involve processing in these areas, but nonevaluative processing recruited these to a greater extent. General Discussion This is among the first work that disentangles the neural components of controlled/reflective social evaluative processing from those associated with automatic/perceptual processing. This distinction is most readily observed in the differential patterns of activity in the medial prefrontal cortex and amygdala. Explicit good-bad evaluations of social stimuli activated, whereas past-present judgments of the same stimuli deactivated the medial prefrontal cortex. These results demonstrate a brain region that may underlie the ERP differences between evaluative and nonevaluative judgments of identical stimuli reported by Crites & Cacioppo (1998). Further support for this suggestion comes from two previous studies that required participants to make explicit good-bad judgments (Gusnard et al., 2001; Zysset et

19 Social Evaluative Processing 19 al., 2002). Although stimuli in the Zysset et al. study were not identical in the evaluative and nonevaluative conditions, making it difficult to draw out distinctions between automatic and controlled components, they did report activation in the mpfc when answering evaluative questions (e.g., I like Berlin ). In addition, Gusnard et al. found activation in the mpfc when participants reflected on whether a picture created pleasant or unpleasant feelings. In contrast, we found greater amygdala activation to bad names than to good names whether or not participants were asked to evaluate those names. Conceptually supporting Bargh et al. s (1992) behavioral work, we found that the amygdala was sensitive to valence whether participants were attending to this dimension or not. This finding is consistent with previous work on the automaticity of amygdala processing (see Morris et al., 1998; Whalen et al., 1998). The pattern of activation observed in the amygdala, in combination with the pattern observed in the medial prefrontal cortex, echoes and substantiates at the neural level, the proposed dissociation between automatic and controlled evaluative processing. This dissociation speaks to the nature of evaluative processing. First, that amygdala activity was observed in the evaluative condition as well as the non-evaluative condition indicates that automatic evaluative processes likely play a role in controlled evaluative processes. Prefrontal activation does not replace amygdala activation in controlled evaluation; instead the two are present simultaneously. The association between medial prefrontal cortex and amygdala processing is further demonstrated when looking at within participant correlations between these areas. Contrary to the idea of a complete separation of processes, we found that medial prefrontal activity and left amygdala activity are substantially correlated, r =.45. These medial prefrontal processes may integrate various current affective and memory sources in the service of building conscious evaluations, and one of these sources likely is the relatively more automatic evaluation provided by the amygdala. A relationship between amygdala and medial prefrontal activation may partially explain observed correlations between automatic and controlled attitude measures, and may provide additional support for claims that automatic processes may bias consciously formed evaluations (see Cunningham et al., 2002).

20 Social Evaluative Processing 20 In addition to the medial prefrontal cortex, another area of PFC, the ventrolateral prefrontal cortex, appears to play a role in controlled evaluative processing. The pattern of activity in this region provides support for suggestions that conscious evaluations reflect a constructed online process rather than a controlled activation of a stored memory representation (see Wilson & Hodges, 1992). When evaluatively opposing information was brought to bear on a judgment (the more equally and strongly positive and negative the target was), greater activity was observed in the ventrolateral prefrontal cortex. Interpreting our findings in terms of the suggestions provided by Thompson-Shill et al. (1997) regarding vlpfc, we suggest that complex evaluations are built online, activating various relevant information, whether positive or negative. Processes in the vlpfc contribute to determining the extent to which the information is pertinent to making good-bad judgments. Interestingly, it appears that what one is reflecting about may influence amygdala activity, although this influence may not be instantaneous but may require time to build up. We found no difference in amygdala processing for evaluative and nonevaluative judgments when participants made alternating judgments (Study 2), or in the first of several judgments of a particular type (the first half of the Study 1 blocks). Yet, after making many evaluative or nonevaluative judgments in a series (after about 15 seconds), amygdala activation in Study 1 differed between judgments such that greater activity was observed for evaluative judgments than for nonevaluative judgments. This suggests that conscious attention to nonevaluative features of a stimulus may influence automatic evaluation, but only by maintaining such a set over time. From Figure 3a, it appears that attending to valence sustains the evaluative function of the amygdala during evaluative blocks and that repeated efforts to focus away from that dimension in nonevaluative blocks habituates amygdala activation. That a controlled attentional focus toward nonevaluation leads to reductions in amygdala processing, but only after repeated nonevaluative judgments, has implications for the control of unwanted automatic evaluations, such as those targeting social groups known to elicit automatic prejudice (see Macrae, Bodenhausen, Milne, Thorn, & Castelli, 1997). A single attempt to focus attention away from evaluation to another nonevaluative feature may result in increased

21 Social Evaluative Processing 21 prejudicial behavior. The amygdala time lines from Study 2 show a negative automatic evaluation regardless of focus, and without bringing to bear conscious values and attitudes, this automatic evaluation may guide behavior. Yet, a strategy for focusing on nonevaluative features may become effective after some time. Continuously focusing attention away from evaluation may reduce the sensitivity of the amygdala, and may reduce automatic attitude effects. In sum, we found that separate brain systems were involved in the controlled/reflective aspects of evaluation as compared with those involved in the automatic/perceptual aspects, supporting theoretical distinctions that have been proposed largely on behavioral observations (e.g., Devine, 1989; Greenwald & Banaji, 1995; Johnson & Multhaup, 1992). We provide both additional support for the suggestion that the amygdala is involved in automatic evaluation, and provide evidence for distinct prefrontal contributions to controlled evaluation. Moreover, these data suggest that there is a connection between automatic and controlled evaluative processes (see also Cunningham et al., 2002). The fact that (a) amygdala processing was observed during both evaluative and nonevaluative judgments, and that (b) there was a correlation between medial prefrontal and amygdala activation, suggests that the output of a consciously generated evaluation may have elements of both automatic and controlled processes (as does consciously experienced memory, e.g., see Jacoby et al., 1992). This interrelation is additionally demonstrated in that controlled processing was shown to affect amygdala activation. After multiple trials, the amygdala was shown to habituate during nonevaluative trials whereas no such evaluation was observed for the evaluative trials. Together, these findings illustrate the ways that social psychology, cognitive psychology, and neuroscience may mutually help clarify the concept of evaluation that is central to the brain, mind and behavioral sciences.

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30 Social Evaluative Processing 30 1 It is important to note that, although the prefrontal cortex is likely involved in the controlled aspects of evaluation, it is likely that areas of PFC also respond to evaluation at an automatic level. For instance, Kawasaki, Adolphs et al. (2001) demonstrated using single neuron recordings that valence is detected in ventral areas of the human prefrontal cortex within 160ms of stimulus presentation, a delay that suggests automatic evaluative processing. 2 Two regressors were used for each trial type: a canonical hemodynamic response, and then a derivatives to model the onset of the BOLD response 3 Idiosyncratic name ratings of were not collected for the first two participants. Names were assigned to condition for these participants using button presses recorded for good-bad trials recorded during fmri scanning. In addition, these participants were not included in the analyses of ambivalence.

31 Social Evaluative Processing 31 Table 1: Significant areas of BOLD signal (Study 1) Good-Bad > Past-Present Size Area BA R/L T x y z 904 Medial Prefrontal 9 R Medial Prefrontal 9 L Medial Prefrontal 10 L Ventrolateral Prefrontal 47 L Ventrolateral Prefrontal 47 L Ventrolateral Prefrontal 47 R Amygdala L Amygdala R Superior Temporal Sulcus L Superior Temporal Sulcus R Superior Temporal 22 L Superior Temporal 22 R Insula R Insula R Inferior Temporal 21 R Hypothalmus L Hippocampus R Past-Present > Good-Bad Size Area BA R/L T x y z 31 Dorsolateral Frontal 46 L Dorsolateral Frontal 6 L Dorsolateral Frontal 8 R Dorsolateral Frontal 9 R Dorsolateral Frontal 6 R Note: Tables show local maxima > 8.00mm apart per cluster. BA= Brodmann s Area; R/F = Right or Left hemisphere; T =Maximal t-statistic for the statistical difference; x, y, & z: the 3D coordinates of the activation

32 Social Evaluative Processing 32 Table 2: Significant areas of BOLD signal (Study 2) Contrast 1: Evaluative > Nonevaluative Size Area BA R/L T x y z 396 Medial Prefrontal 9 L Medial Prefrontal 9 L Ventrolateral Prefrontal 47 R Ventrolateral Prefrontal 47 R Ventrolateral Prefrontal 10 R Ventrolateral Prefrontal 47 L Ventrolateral Prefrontal 47 L Inferior Temporal 20 L Brain Stem Cerebellum R Contrast 2: Nonevaluative > Evaluative Size Area BA R/L T x y z 355 Dorsolateral Prefrontal 46 L Dorsolateral Prefrontal 6 R Dorsolateral Prefrontal 9 R Dorsolateral Prefrontal 8 R Posterior Cingulate 31 R Superior Parietal 7 R Middle Temporal 21 L Superior Temporal 22 L Inferior Temporal 37 R Inferior Temporal 37 L Inferior Temporal 37 L Cuneus 19 R Occipital 17 L Contrast 3: Bad Names > Good Names Size Area BA R/L T x y z 7 Amygdala L Ventrolateral Prefrontal 45 R Ventrolateral Prefrontal 47 L Anterior Cingulate 32 L Insula R Substantia Nigra R Posterior Cingulate 31 L Cerebellum R Note: Tables show local maxima > 8.00mm apart per cluster. BA= Brodmann s Area; R/F = Right or Left hemisphere; T =Maximal t-statistic for the statistical difference; x, y, & z: the 3D coordinates of the activation

33 Social Evaluative Processing 33 Figure 1: Activation in dorsal medial prefrontal cortex for good-bad > past-present a: Blocked Design (Study 1) Dorsal Medial Prefrontal Cortex b: Event Related Design (Study 2) Dorsal Medial Prefrontal Cortex % Signal Change Good-Bad vs. Past-Present Seconds Good-Bad Past-Present

34 Social Evaluative Processing 34 Figure 2: Activation in ventrolateral prefrontal cortex for good-bad > past-present a: Blocked Design (Study 1) Ventrolateral Prefrontal Cortex b: Event Related Design (Study 2) Ventrolateral Prefrontal Cortex % Signal Change Good-Bad vs. Past-Present Seconds Good-Bad Past-Present c: High vs. Low Ambivalent Names (Study 2) % Signal Change Good-Bad Trials Seconds % Signal Change Past-Present Trials Seconds NonAmb Ambivalent NonAmb Ambivalent

35 Social Evaluative Processing 35 Figure 3: Activations the amygdala to judgment task and stimulus valance a: Blocked Design (Study 1) Left Amygdala % Signal Change Good-Bad vs. Past-Present Seconds b: Event Related Design (Study 2) Good-Bad Past-Present Left Amygdala % Signal Change Bad vs. Good Seconds Bad Good c: Valence effects in both the past-present and good-bad tasks (Study 2) % Signal Change Good-Bad Judgments Seconds % Signal Change Past-Present Judgments Seconds Bad Good Bad Good