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BIASES IN IMPRESSION FORMATION.
A DEMONSTRATION OF A BIVARIATE MODEL OF EVALUATION
DISSERTATION
Presented in Partial Fulfillment for the Requirements for
the Degree Doctor of Philosophy in the Graduate
School of the Ohio State University
By
Wendi L. Gardner, M.A.
The Ohio State University
1996
Dissetation Committee: Approved by
John Cacioppo, Adviser
Gary Berntson
Marilynn Brewer UMI Number: 9639240
UMI Microform 9639240 Copyright 1996, by UMI Company. All rights reserved.
This microform edition is protected against unauthorized copying under Title 17, United States Code.
UMI 300 North Zeeb Road Ann Arbor, MI 48103 i ABSTRACT
Research in the area of impression formation has consistently revealed systematic asymmetries in the impact of positive and negative attributes on the overall evaluation of a target. A positivity bias, such that neutral targets are evaluated in a favorable fashion, and a negativity bias, such that negative information carries greater weight in impressions, are hallmarks of these asymmetries. Many theories have been presented to explain both the positivity and negativity biases in impression formation, however all have implicitly assumed a single, bipolar, reciprocally activated evaluative dimension.
The current analysis proposes a general model of evaluation that can accomodate both the positivity and negativity biases that are frequently found in the impression formation literature. The bivariate model of evaluation challenges the traditional bipolar perspective by asserting that positivity and negativity are separable evaluative substrates, founded on distinct neural mechanisms, that can be reciprocally, independently, or jointly activated. In addition the activation functions for positivity and negativity are hypothesized to differ. For example, the model proposes a
"positivity offset" for the evaluative system, or in other words, predicts that the organism will respond positively without the presence of positive or negative information. The model also proposes a "negativity bias" within the evaluative system such that negative information is weighted more heavily than positive information, and thus, negativity often has greater impact on impressions and behavior. ii The current work was designed to test several hypotheses of the bivariate model through examining attitudes that were created towards fictional persons and animals during an impression formation task. Each of the studies was designed to test the predictions of the bivariate model as well as alternative explanations for the biases present in the impression formation literature.
The predictions of the bivariate model of evaluation were supported across all five studies. The positivity offset and negativity bias were evidenced despite varying the diagnosticity of the information presented, and were demonstrated for both human and non-human targets. Implications of these findings for other models of biases in impression formation, and for evaluative processing in general, are discussed. The bond that links your true family is not one o f mere blood, but o f respect andjoy in each other's life.
Richard Bach
Dedicated to all of the members of my family ACKNOWLEDGMENTS
I wish to thank my adviser, John Cacioppo, for the intellectual support, encouragement, and enthusiasm that made this research both possible and pleasurable.
I thank the members of the Social Cognition Research Group and the Social
Psychophysiology Lab Group for their discussions and suggestions for the research at it's early stages.
I am grateful to John Krosnick for the genesis of Study Three, as well as for stimulating conversations concerning the three-stage model of evaluation.
I also wish to thank Jennifer Welbourne, both for discussing various aspects of the reseach with me, and for her patience and assistance during the writing of the manuscript. VITA
July 30, 1968...... Born - Washington D.C.
1992...... B.A., Florida Atlantic University, Boca Raton, Florida
1994...... M.A., The Ohio State University Columbus, Ohio
1990 - present...... Graduate Research and Teaching Associate, The Ohio State University
PUBLICATIONS
Research Publications
1. Brewer, M.B. & Gardner, W.L. Who is this "we"? Levels of collective identity and self-representations. Journal of Personality and Social Psychology. 71. 83-93.
2. Crites, S.L., Cacioppo, J.T., Gardner, W.L. & Berntson, G.G. (1995). Bioelectrical echoes from evaluative categorization: II. A late positive brain potential that varies as a function of attitude registration rather than attitude report. Journal of Personality and Social Psychology. 68, 997-1013.
3. Gardner, W.L. & Cacioppo, J.T. (1995). Multi-gallon blood donors: Why do they give? Transfbsion. 10. 795-798.
4. Richardson, D.R., Hammock, G. S., Smith, S. M., Gardner, W.L., & Signo, M. (1994). Empathy as a cognitive inhibitor of interpersonal aggression. Aggressive Behavior. 20. 275-289.
vi 5. Cacioppo, J.T., Crites, S.L., Gardner, W.L., & Berntson, G.G. (1994). Bioelectrical echoes from evaluative categorization: I. A late positive brain potential that varies as a function of trait negativity and extremity. Journal of Personality and Social Psychology. 67. 115-125.
6. Cacioppo, J.T. & Gardner, W.L. (1993). What underlies medical donor attitudes and behavior? Health Psychology. 12. 269-271.
FIELDS OF STUDY
Major Field: Psychology TABLE OF CONTENTS
Page
Abstract...... ii
Dedication...... iv
Acknowledgments...... v
Vita...... vi
List of Tables...... x
List of Figures...... xi
Chapters:
1. Introduction...... 1
2. Study One...... 22
3. Study Two...... 30
4. Study Three...... 38
5. Studies Four and Five...... 44
6. General Discussion...... 50 Page
Bibliography 71
Appendices:
APPENDIX A: Illustrations and Figures...... 81
APPENDIX B: Behaviors Used in Each Study...... 92
APPENDIX C: Instructions and the BEAMs...... 105
APPENDIX D: Cover Story for Studies Three through Five...... 123 LIST OF TABLES
Table Page
1. Correlations among positivity, negativity, and ambivalence ..91
X LIST OF FIGURES
Figure Page
1. A bivariate model of evaluation...... 82
2. The activation functions of positivity and negativity...... 83
3. Changes in impressions of Sam in Study One...... 84
4. Changes in impressions of Sam in response to morality relevant behaviors in Study Two...... 85
5. Changes in impressions of Sam in response to ability relevant behaviors in Study Two...... 86
6. Changes in impressions of Sam after initially viewing non-diagnostic neutral behaviors in Study Three...... 87
7. Changes in impressions of the Aguaphore fish in Study Four...... 88
8. Changes in impressions of the Entophore insect in Study Five...... 89
9. A three-stage model of evaluative processing...... 90
xi CHAPTER 1
INTRODUCTION
Pleasantness and unpleasantness are quantitative variables so related to each other that they may be represented respectively by the positive and negative values o f a single algebraic variable.
Beebe-Center (1932, p. 7)
The custom of finding an arithmetic average o f liking that includes both positive and negative ratings. . . may well be the mixing o f apples and cabbages.
Jordan (1965, p.322)
The existence of asymmetry in the integration of positive and negative information during impression formation has been met with equal parts interest and irritation by researchers in the area of person perception. In the beginning, any imperfect correlation between ratings of like and dislike, pleasantness and unpleasantness, was considered mere methodological artifact that could be overcome with careful attention to psychometrics (Beebe-Center, 1932)1. The differentiation of
positivity and negativity did not attain theoretical significance until the empirical
battles fought over an additive (Asch, 1946) versus averaging (Anderson, 1965)
model of impression formation. Studies conducted during this time revealed a
consistent asymmetry in the impact of positive and negative traits on the overall
evaluation of a target that neither model could accommodate. In the half century
since, both positivity and negativity biases in impression formation have been the
subjects of extensive research, although very rarely have the two been considered
together.
The current analysis proposes a general model of evaluation that can
accommodate both the positivity and negativity biases that are frequently found in the
domain of person perception. In brief, this model outlines a bivariate rather than
bipolar conceptualization of evaluation which suggests (1) that the positive and
negative evaluative processes underlying impressions and attitudes are distinguishable
(stochastically and functionally independent), and therefore can be reciprocally
activated, independently activated or coactivated and (2) that positivity and negativity
are characterized by distinct activation functions, including an initial positivity offset
and a complementary negativity bias (Cacioppo & Berntson, 1994; Cacioppo,
Gardner, & Berntson, in press). To appreciate how the bivariate model differs from
'This perspective on positive-negative asymmetries continues today. Recently, the National Election Survey committee deleted questions that asked respondents to assess a candidate's attributes, because it was found that assessments of opposite traits (e.g. wise-foolish, likable-dislikable) were often not strongly negatively correlated (Krosnick, personal communication). 2 other models in its explanation of positivity and negativity biases, we begin with a
review of the current theories of biases in impression formation.
Negativity Biases in Impression Formation
The evidence for the greater impact of negative over positive information in
the impressions and evaluations of others was first reviewed by Kanouse & Hanson
(1972). Noting that perceived potential costs often outweigh potential benefits in
evaluations of both persons and events, Kanouse & Hanson (1972) proposed that
people seek to maximize potential outcomes, and that this maximization would be
best served by attending and responding preferentially to negative information. They
pointed out that negative attributes are more likely to cancel out the benefits of
positive attributes than vice versa (e.g. exquisite spicing cannot compensate for
rancid soupstock, a companion's sharp wit may not compensate for interpersonal insensitivity). Further, some negative events can make future positive opportunities impossible, but positive events cannot make future losses impossible (e.g. a gambler
stripped of all of his money loses future opportunities for gain, however a gambler who doubles her money does not find herself safe from the risk of future losses).
Finally, they reviewed the attributional literature (Jones & Thibaut, 1958; Jones &
Davis, 1965) arguing that the demands of current society make the display of negative personal attributes (e.g. unkindness) so non-normative as to be impactful, whereas the corresponding positive attribute (e.g. kindness) is so commonplace it may not even be noticed. Thus, for all of these reasons they proposed that negative information about a person is more frequently noticed and responded to than positive information about a person.
3 Of these various explanations of the negativity bias, it was the focus on negative behavior as non-normative or unexpected that received the greatest attention. Jones and McGillis (1976) focused on the costliness of negative behavior in a society in which such behavior is non-normative. For example, a person behaving in an unfriendly fashion at a party may face future social rejection. Jones & McGillis
(1976) proposed that perceivers take this type of cost to the actor into account when making attributions for a behavior, and that when actors risk social censure in performing a negative behavior perceivers are much more likely to make a dispositional attribution to the person's character. Thus, lively partygoers may easily be perceived as acting in accordance with situational demands, but our unfriendly partygoer, by bucking the social standards, ensures that others will view him as a truly unfriendly soul. Jones and McGillis (1976), then, suggest that the heavier weight given to negative behaviors in impressions of others is an expected outcome of attributional processing; perceivers recognize the possible costs to the individual in performing such non-normative behavior, and make a more confident attribution of the behavior to dispositional characteristics.
Fiske (1980) agreed with Jones & McGillis (1976) that negative behavior in a society that generally disapproves of such behavior is novel, however, she proposed that this novelty alone could account for the negativity bias. Because rare or novel information attracts greater attention and receives more cognitive processing time, novel information (e.g. negative behavior) should naturally be weighted more heavily in the impressions of others than less novel information (e.g. positive behaviors), even without complex attributional processing. In two studies, Fiske (1980) provided evidence that negative behaviors attract greater attention (longer looking times at 4 slides that portrayed such behavior), a finding other researchers have replicated
(Hansen & Hansen, 1988; Pratto & John, 1991). However, evidence exists that calls into question the novelty interpretation of the negativity bias. Several researchers have attempted to assess the impact of novelty and negativity independently, either in perfectly crossed designs (Abelson & Kanouse, 1966) or through partialling out the variance accounted for by novelty statistically (Feldman, 1968; Kinder, 1971). All of these studies found that negative information received greater weight in impressions of a target, even after novelty, frequency, or "surprisingness" was taken into consideration. Thus, mere novelty remains insufficient to account for the negativity bias.
Reeder & Brewer (1979) and Skowronski & Carlston (1987, 1989) also assumed that negative behaviors are capable of attracting greater attention and processing; however, in these models the negativity bias is due to informational content or diagnosticity, rather than novelty. Because people are aware of the imperfect correspondence between displayed behavior and underlying disposition, they are presumed to attend selectively to those behaviors that provide greater diagnosticity in implying dispositional attributes. The diagnosticity of a behavior may be determined by both its valence and its applicability to one of two fundamental dimensions of social judgment, "Morality" and "Ability" relevance.
Reeder & Brewer (1979) presented a two stage model of attributional processing that is proposed to take place during impression formation. During the first stage, observers are posited to classify the behavior itself in terms of its implications for a particular level of an attribute; for example, the behavior "Sally brought cookies to welcome her new neighbors" might be classified as a highly kind 5 behavior. Next, observers embark on the task of classifying the person who
performed the behavior, the relative kindness of Sally herself. At this second stage,
observers are guided by their dispositional schemas: preconceptions or knowledge
about the relationship between disposition and behavior. A dispositional schema
might be considered a little like a personal theory that explains how certain levels of
an attribute (e.g. kind) are probabilistically associated with the displays of various
behaviors (e.g. baking cookies for neighbors). Importantly, Reeder and Brewer
(1979) proposed that for morality or ability relevant attributions, the schemas
observers hold are hierarchically restrictive2.
A hierarchically restrictive schema is one in which dispositional classifications
at one extreme of a dimension (e.g. extremely unkind) are behaviorally unrestricted whereas at the other extreme (e.g. extremely kind) the behaviors are restricted. For example, an extremely unkind person is not expected to display only extremely unkind behaviors, it is probable instead that such a person would display a mix of kind and unkind behaviors. However, observers expect an extremely kind person to behave only in an extremely kind fashion; the extremely kind person is thus restricted in his or her behavior. Perceivers are proposed to possess hierarchically restricted schemas for both morality and ability relevant domains, but which endpoint restricts behavior and the resulting diagnosticity of negative or positive behaviors changes depending on the domain.
2Reeder & Brewer's (1979) model of dispositional schemata also included partially and fully restricted schemas, but it is the discussion of hierarchically restricted schemata for the assessment of morality and ability relevant attributes that is most pertinent for the explanation of negativity biases. 6 For morality relevant domains (such as honesty) extremelynegative behaviors provide a higher level of diagnosticity for dispositional classifications. For example, a truly dishonest person may still display an honest behavior on occasion (e.g. giving back extra change in a supermarket) but a truly honest person never commits dishonest behavior (e.g. purposefully writing a bad check). This restriction results in negative behaviors lending greater confidence to an immorality categorization than equally extreme positive behaviors will lend to a categorization of morality. Thus, for these types of morality relevant behaviors, negative information (pertaining to dishonesty) is more informative, and will therefore carry more weight in the resulting impression.
For ability relevant domains (such as intelligence or athleticism), on the other hand, the opposite endpoint is restricted and therefore extremelypositive behaviors are diagnostic. As an example, even highly intelligent people occasionally perform unintelligent behaviors (e.g. having trouble figuring out the tip in a restaurant) but it is unlikely that a truly unintelligent person could perform intelligent behaviors (e.g. figuring out how to fix a broken toaster). In ability relevant domains, then, positive behaviors should be weighted more heavily in the impression.
Skowronski & Carlston (1987, 1989) used similar logic in explaining positivity and negativity biases in impression formation. They proposed that behaviors differ in "cue-diagnosticity" for a given social category (e.g. intelligence) and followed the Reeder & Brewer (1979) model in determining whether positive or negative behaviors would be diagnostic of morality or ability relevant attributes. They tested and found the predicted asymmetry in trait attributions: in morality relevant domains, such as assessing a target's honesty, negative information carried greater 7 weight, but the opposite was true in assessing ability relevant attributes, such as intelligence.
Importantly, the bulk of the research investigating the diagnosticity account of negativity and positivity biases in impression formation utilized trait attribution, rather than evaluation, as the primary dependent variable. This approach is different enough from the work of prior researchers documenting the negativity bias (who manipulated combinations of positive and negative traits and studied subsequent evaluation) to question whether the same pattern predicted by diagnosticity would hold in general evaluations of a target as well.
The aforementioned explanations of the negativity bias in terms of novelty or diagnosticity all share an implicit assumption that learned attributional mechanisms underlie greater processing of negative information. After all, before we can recognize non-normative behavior, we must first become cognizant of the norms. In contrast, Peeters & Czipanski (1990) proposed a pragmatic or "behavioral-adaptive" approach that bases the negativity bias in evolutionary pressures rather than learned attributional processes. They propose that the environment generally has greater potential for harm than good. Further, harmful interactions with the environment can be irreversible (e.g. the naive ingestion of a poisonous toadstool) or at the very least, more punishing than positive interactions are rewarding (e.g. the discovery of an edible mushroom). Therefore, asymmetries in the environmental affordances that made up our evolutionary history could have resulted in an adaptive sensitivity to negative cues in the environment, particularly when those cues have relevance for personal well-being. Thus, Peeters & Czipanski (1990) would explain the greater negativity bias in judging the morality versus ability relevant attributes of targets in 8 terms of threat to the observers; a lack of morality implies potential harm to others, whereas a lack of ability is more harmful for the targets themselves.
Positivity Biases in Impression Formation
With the exception of the positivity bias for ability relevant attributions
(Reeder & Brewer, 1979; Skowronski & Carlston, 1987; Skowronski & Carlston,
1989), the term "bias" in the impression formation literature has meant something different when it has referred to positive rather than negative biases. For negativity biases, the term "bias" has been used to refer to the increased weight of negative over equally extreme positive information. For the positivity biases, however, the "bias" has been used to refer to the persistent finding that in the absence of information, or given information that is neither positive nor negative, targets are judged in a generally positive rather than neutral fashion3.
Positivity biases were originally treated as nothing more than methodological artifact. For example, Bruner & Tagiuri (1954) proposed that a "leniency" bias could result from social pressures in the experimental setting. Given that leniency and tolerance are generally socially preferred, experimental participants may be expected to express positive evaluations of others as a form of impression management.
Although plausible, this hypothesis was tested by Rook, Sears, Kinder & Lau (1978),
3The term "positivity bias" has also been used by the McGuires (McGuire & McGuire, 1992; McGuire & McGuire, 1996) to refer to the greater consideration given to attributes a stimulus possesses as compared to attributes a stimulus lacks. This form of "positivity bias" will not be discussed in the present manuscript, as it concerns simply the presence or absence of features rather than their evaluative components. 9 who manipulated the self-presentational concerns of their participants and found that no technique eliminated the positivity bias, nor did any increase its magnitude.
The most documented theoretical perspective of the positivity bias concerns basic differences in our expectations of others. Boucher and Osgood (1969) presented a "Pollyanna hypothesis" that proposed a trend toward optimism in our general expectations of other humans' attributes. Kaplan (1973) found that 80% of all participants in a study rating "people in general" displayed this optimism by giving positive trait attributions and ratings. Further evidence was provided by Adams-
Weber (1979) and Benjafield (1985) both of whom showed that research participants asked to assign valenced adjectives to an unknown target assigned them in accordance with the "golden section ratio" of roughly 38% negative to 62% positive.
This ratio approximates an optimal figure-ground relationship, with positives representing the more frequently expected attributes4.
Matlin and Stang (1978) argued that this pervasive positive expectation of other people can even be found in the words used most commonly to describe others, by reviewing evidence for a positive bias in the English language. They note that lexical marking (e.g. the use of "un" or "dis" added onto a word to change its meaning) is much more common in negative than positive adjectives (e.g. wwkind, dishonest). This lexical marking implies that positive qualities are expected, and indeed form the basis of our social categories, whereas negative qualities are defined often by the absence or reversal of the expected positivity.
4The figure-ground relationship of expected positive to negative attributes was also cited by Fiske (1980) to support the relative novelty of negative, relative to positive, information. 10 Just as the negativity bias in person perception was proposed to arise from
attributional mechanisms, the expectation of positive behavior and qualities in an
unknown individual can also be subjected to an attributional analysis. Jones & Davis'
(1965) observation that society rewards positive behaviors and punishes negative
behaviors, leads to the expectation that most people will engage in positive behaviors
most of the time. As such, it is unsurprising that observers could logically expect
positive behaviors and dispositions in the absence of evidence to the contrary.
Peeters and his colleagues (Peeters, 1971; Peeters & Czipanski, 1990) also
proposed an expectancy explanation of the positivity bias. However, this expectancy
is thought to exist because of its adaptiveness for survival. Early humans who formed
positive hypotheses about reality would be more likely to interact with the
environment, and this tendency would become part of humans' evolutionary heritage.
Thus, positive expectancies about unknown others would be but one manifestation of
a more general positive expectancy about the world at large.
Sears (1983) proposed a different theoretical basis than the expectancy models
for the positivity bias in impression formation. He renamed the positivity bias the
positivity "bonus" and explained it in terms of Byrne's (1971) research demonstrating
the positive effects of similarity on attraction and liking. Given the basic similarity
human observers share with any other human target, Sears (1983) proposed that the
positivity bias found in impression formation is a result of feelings of liking expressed for any other human being.
Sears' (1983) also predicted that this positivity "bonus" would be added to all judgments made about a person, and that "persons should normally be somewhat protected from being evaluated very negatively, even given the input of unfavorable 11 information, because of the perceived similarity felt toward them" (p. 236). Although he noted the inconsistency of this prediction with the well-documented negativity bias in impression formation, he proposed that this could be due to the nature of the targets; much impression formation research involves the evaluation of "hypothetical" targets, whereas his research required participants to rate "real" people. It is important to note that the positivity biases that were first documented by Kaplan
(1973) and Adams-Weber (1979) were found with hypothetical others, and further, that negativity biases have been found in the evaluations of real targets such as political figures (Lau, 1982; Klein, 1996). Thus, this distinction between evaluating
"real" and "hypothetical" targets does not seem to systematically account for the existence of positivity or negativity biases in impressions.
A Bipolar versus Bivariate Model o f Evaluative Space
All of the above explanations of positive and negative biases in impression formation share the implicit assumption of a single bipolar evaluative dimension. In all of the models, it is assumed that positive and negative information is integrated into a bipolar evaluation through a single evaluative mechanism that "averages" the implications of each of the component pieces of information, positive information moving the evaluation to the positive side of the continuum and negative information doing the reverse (Anderson, 1974, 1981). Asymmetries in integration are seen as a result of negative information acquiring a more extreme rating in these averages merely as a result of its rarity, diagnosticity, or adaptive utility (Skowronski &
Carlston, 1989). Given that a bipolar perspective of evaluation has dominated measurement and theory in the domain of attitudes since the very beginnings of the
12 field, it is not surprising that researchers wishing to examine evaluation in the context of impression formation would also draw upon this perspective.
Louis Thurstone, the pioneer of attitude measurement, was a psychophysicist who likened the scaling of attitudes to the scaling of perceptual experiences such as temperature or luminosity (Thurstone, 1928). As such, a bipolar perspective of evaluation resulted, with positivity and negativity serving as two endpoints along an evaluative continuum. Further, just as the transfer functions for heat and cold or bright and dim are mirror images of one another, positivity and negativity were also conceptualized to operate in a symmetric and reciprocal fashion. Thus, an increase in positivity was assumed to result in an equal decrease in negativity and vice versa.
This assumption did not go unquestioned over the years. Miller's (1959) work on approach/avoidance conflict posited that approach and avoidance motivations were distinct and capable of coactivation, thus challenging the bipolar perspective of evaluation. Similarly, Scott's (1968) and Kaplan's (1972) initial forays into the area of ambivalence posited that, for some attitude objects, feelings of positivity and negativity exist concurrently. In both of these lines of research then, coactivation of positivity and negativity was proposed to be possible, although significantly rarer than reciprocal activation. Despite these early challenges however, the bipolar perspective of evaluation continued to dominate.
More recent physiological and psychological evidence continues to point out the inadequacies of a single bipolar perspective of evaluation. For example, substantial neurophysiological evidence supports the seperability of positive and negative evaluation. Distinct neural mechanisms are thought to underlie approach and withdrawal behaviors in animals (Berridge & Grill, 1983; Gray, 1991; LeDoux, 13 1995), as well as positive and negative affective states in humans (Davidson, 1992;
George, Ketter, Parekh, Horwitz, Herscovitch, & Post, 1995).
In addition, psychological data illustrates the functional independence of
positive and negative responses to a stimulus. Katz and Hass (1988) reported that the
negative and positive components of white students' attitudes toward African
Americans could be primed independently. Similarly, Ikegami (1993) demonstrated
that negative, but not positive responses to an ambiguous behavioral stimulus could
be affected by a negative priming manipulation. Further, Goldstein and Strube (1994)
showed that a negative event caused an increase of negative affect but did not
decrease positive affect; likewise, positive events affected only endorsements of
positive affect without changing negative affect.
Finally, a resurgence of research concerning affective conflict or ambivalence reiterates the psychological importance of coactivated states (Thompson, Zanna &
Griffin, 1995 for review). The field of stereotyping and prejudice, for example, contains several theories of racial attitudes that are characterized by the existence of affective conflict (Patchen, Davidson, Hofman & Brown, 1977; Katz, Wackenhut, &
Hass, 1986; Gaertner & Dovidio, 1986; Devine, 1989; Monteith, Devine & Zuwerink,
1993). In a recent review of these models, Monteith (1996) presented research that showed that this type of "conflicted prejudice" was distinguishable from "plain" or single-dimension prejudice both on a stochastic basis and because of the distinct affective consequences of conflicted prejudice. Thus, a formulation of evaluation that does not allow for the non-reciprocal activation of positivity and negativity seems insufficient to account for a wide variety of empirical findings.
14 Both the physiological and psychological evidence thus suggests the existence
of separable positive and negative evaluative substrates that can be independently
activated and coactivated as well as reciprocally activated. Recently, Cacioppo and
his colleagues have proposed a bivariate model of evaluation that suggests separate
evaluative systems underlie positive and negative evaluations (Cacioppo & Berntson,
1994; Cacioppo, Gardner, & Berntson, in press). The bivariate model of evaluation,
by proposing separate positive and negative evaluative systems, allows for all possible
combinations of positive and negative activation: reciprocal activation, independent
activation of positivity and negativity, coactivation, and coinhibition (Cacioppo &
Berntson, 1994; Cacioppo, Gardner, & Berntson, in press; See Figure 1).
The bivariate model of evaluation is not alone in hypothesizing a two
dimensional representation of positivity and negativity; researchers in the area of
emotion have also argued that differences in the antecedents and consequences of
positive and negative affect imply that they are qualitatively distinct psychological
states rather than endpoints on a single continuum (Watson, Clark & Tellegen, 1988;
Frijda, 1988; Carver & Scheier, 1990; Taylor, 1991)5. The bivariate model of
evaluation is unique, however, in hypothesizing that the positive and negative
evaluative systems have distinct activation functions. Unlike other truly bipolar
constructs such as bright and dim in which the transfer functions are essentially
identical, the activation functions of positivity and negativity are not necessarily
5Despite the existence of this perspective in emotion research, the proposal of positivity and negativity as distinguishable constructs has not been well accepted in the domain of attitudes (see Eagly & Chaiken, 1993, for review).
15 mirror images of one another, but are instead thought to differ in fundamental ways.
For example, the model proposes a "positivity offset" in the evaluative system, or in other words, that an organism will respond positively even in the absence of positive information, as long as no negative information is presented. This positivity offset is presumed to be adaptive in that it fosters exploratory behavior even in novel settings.
The model also proposes a "negativity bias" within the evaluative system such that negative information is weighted more heavily than positive information, and thus negativity often has greater impact on evaluations and behavior. This increased sensitivity to negative information is presumed to be the protective counterpart of the positivity offset; the organism may explore, but is sensitive to even small amounts of threat (See Figure 2)6.
An easy way to conceptualize the differences between the traditional bipolar and the bivariate model of evaluation is to imagine the evaluative system as a stereo with one speaker representing positive output and the other representing negative output. The bipolar perspective views these two speakers as dependent, connected as if with a balance knob such that increasing the output of one symmetrically decreases the output of the other. The bivariate model, on the other hand, proposes something more akin to separate volume controls for each speaker. Thus, positivity and negativity can be reciprocally activated, but they aren't constrained to reciprocity,
6In this paper, differences inoffset in the transfer functions for positivity and negativity refer to differences in intercept (e.g. output at zero input), whereas differences inbias refer to differences in slope. The term "positivity offset" is preferred to "positivity bias" because it refers to the positive motivational output at zero input and it avoids the confusion that could ensue if positivity bias (rather than offset) referred to differences in the intercept and negativity bias referred to differences in slope (See Figure 2). 16 they can also be increased or decreased independently, and can both be at high levels
— representing ambivalence. The positivity offset in this system means that even with
no positive input, there will be a small positive output, as if with both volume
controls turned completely off, there still would be output from the positive speaker.
The negativity bias, on the other hand, implies that given equal inputs, as if both
volume controls were increased by equal degrees, the output from the negative
speaker would increase faster than the positive speaker. Negative information has
greater impact because the activation function of negativity is characterized by a
steeper slope.
The bivariate model of evaluation is an overarching model of evaluative
processes, and although some of its theoretical origins lie in research concerning the
rudimentary evaluations of simple stimuli (Miller, 1959), it is presumed to generalize to sophisticated attitudes toward complex targets. One obvious area of applicability is in the area of impression formation; the differences in the activation functions of positivity and negativity may illuminate the biases that have been so commonly observed in the impression formation literature.
The positivity offset and the negativity bias in impression formation
A positivity offset in the evaluative system would predict positive responses to neutral stimuli, and thus could accommodate the positivity biases so prevalent in the impression formation literature. Importantly, the majority of the past explanations of positivity biases have been founded on positive expectancies or "optimism" (Boucher
& Osgood, 1969) caused either by the frequent positive interactions people face as a result of social pressures (Jones & Davis, 1965) or because of the evolutionary 17 significance of forming "positive hypotheses" about the environment for survival
(Peeters & Czipanski, 1990).
Although the bivariate model, like Peeters and Czipanski's (1990) "behavioral- adaptive" approach also asserts that a tendency to respond positively to neutral stimuli is likely a reflection of our evolutionary heritage, the positivity offset importantly predicts actual positive output rather than merely a positive expectation.
From this perspective, participants in past studies who evaluated an unknown or neutral target favorably were doing so not out of optimistic expectations, but because of actual positive feelings or responses toward the person7. In this way, the positivity offset is similar to what Sears (1983) termed the positivity "bonus." Sears' model of positivity biases predicted positive evaluations of neutral others because of the extra liking afforded to any human conspecific The positivity offset, however, is not hypothesized to arise solely from the effects of similarity on attraction; thus, unlike
Sears' (1983) model, it is not limited to human targets.
Similarly, the negativity bias is not thought to be limited to human targets, nor to situations which violate expectancies. Within the bivariate model, greater weight is given to negative information about a target as a direct result of the activation function of negativity within the evaluative system, not because of the novelty (Jones
& Davis, 1965; Fiske, 1980) or diagnosticity (Reeder & Brewer, 1979; Skowronski &
Carlston, 1987; Skowronski & Carlston, 1989) of that information. Novel or
7One particularly compelling demonstration of positive evaluative output towards neutral stimuli is the "mere exposure" effect in which repeated exposure to neutral objects or persons results in increased feelings of liking for and positive behavior towards those stimuli, even if the repeated exposure occurs outside of conscious awareness (Zajonc, 1968; Moreland & Zajonc, 1977). 18 diagnostic information may carry additional weight through other mechanisms, or moderate the extremity of impressions if novelty or diagnosticity is very high or very low, but the activation function of negativity alone is presumed sufficient to cause negative information to impact more heavily in an evaluation.
If we assume, then, that the evaluative system is bivariate rather than bipolar in nature, and that the separate evaluative substrates of positivity and negativity have distinct activation functions including a positivity offset and a negativity bias, six testable hypotheses can be generated within an impression formation domain:
1. Impressions of a neutral target should be more positive than negative as a result of the positivity offset.
2. These impressions should reflect true positive evaluative responses rather than merely a positive expectancy, and thus any subsequent negative information about the target should initiate a state of conflict or ambivalence8.
3. Negative information should have the greatest impact on impressions in general, because of the steeper slope of the activation function of negativity9.
4. Given equally positive and negative information, the resulting impression should be more negative then positive as a result of the negativity bias.
8If the positive ratings of a target are an expression of expectancy only, then no positive evaluative output has actually taken place but instead is merely expected to take place. Thus, subsequent negative information, although violating an expectancy and enhancing the impact of negative information, should engender no real evaluative conflict.
9Hypothesis three is expected to hold only in situations in which negativity is sufficiently activated. Atextremely low levels of negative activation, positive information may have the most impact because of the positivity offset (see Figure 1). 19 5. The negativity bias may be stronger for morality relevant information
because of the addition of processes assessing diagnosticity, but will not be limited to
morality relevant information because the negativity bias is not a result of
diagnosticity.
6. Neither the positivity offset nor the negativity bias should be limited to
impressions of human targets, because both are assumed to be features of a more
general evaluative system.
Overview o f the present research
Five studies were conducted to test the hypotheses of the bivariate model of
evaluation within the impression formation domain. In each study, two blocks of
information were presented about a target and participants rated their positive,
negative, and ambivalent responses to the target after each block of information. The
first block of information contained neutral information, the second block contained
neutral, positive, negative, or equally positive and negative information manipulated in
a between subjects fashion.
In each study, the positivity offset was expected to manifest itself in two ways.
First, the positivity offset was predicted to appear in the initial ratings of the target
after neutral information; reactions were expected to be more positive than negative.
The second prediction of the positivity offset dealt with changes in rated ambivalence.
Both the bipolar and the bivariate model predict that adding negative information to neutral information should increase negativity, however, the bipolar model does not predict that this addition of negativity will also increase ambivalence. The bivariate model, on the other hand, does predict such an increase, because the negative 20 reactions to a target based on the negative information should be in conflict with the initial positivity towards the target that results from the positivity offset. Thus, the addition of negative information to neutral information was predicted to increase both negativity and ambivalence if there was an existing positivity offset, but increase only negativity if no positivity offset was present.
This design also allowed two tests of the negativity bias in each study. First, if negative information truly is weighted more heavily in evaluations, than the amount of impression change after the second phase of information was expected to be greatest for the subjects receiving all negative information. Second, despite the fact that the mixed condition contained an equal number of positive and negative behaviors, the hypothesized negativity bias predicted more negative than positive impressions after the mixed behavior condition.
In Study One, participants evaluated a fictional person named Sam based on blocks of information about Sam's behavior. Study One was designed to replicate prior impression formation research and assess the presence of a positivity offset and negativity bias. Study Two was a replication and extension of Study One.
Participants again evaluated Sam, but the conditions of the study were designed to assess the generality of the negativity bias to both morality relevant and ability relevant domains. Study Three addressed whether diagnosticity may account for the positivity offset. In Study Three, participants were given neutral information about
Sam that was non-diagnostic in nature; the information was applicable to the entire human race. Studies Four and Five assessed the generality of the positivity offset and negativity bias in impressions of non-human targets. In Study Four participants read and responded to blocks of information about a fictional fish; in Study Five 21 participants read and responded to blocks of information about a fictional insect.
Thus, Studies Four and Five examined the tests of the bivariate model during evaluation of non-human targets, as well as evaluations of targets that were exemplars of generally neutral (fish) or generally negative (insect) categories.
22 CHAPTER 2
STUDY ONE
Study One was designed to test the assumptions of the bivari ate model
through examining attitudes that were created towards a fictional person during an
impression formation task. Participants received behavioral information about a
person named Sam in two phases, and reactions to Sam were assessed using unipolar
measures of positivity, negativity, and ambivalence after each wave of information.
During the first information phase, all participants read six neutral behaviors. Four
randomly assigned information conditions constituted the second phase: (1) six more
neutral behaviors (2) six positive behaviors (3) six negative behaviors and (4) a mixed
valence condition containing three negative and three positive behaviors, randomly
ordered. The order of the unipolar scales and scale presentation were
counterbalanced to result in a 4 Valence of Second Phase of Information (Neutral,
Positive, Negative, or Mixed) X 2 Form Order (A first or B first) X 4 Subscale Order
(Positive first/Ambivalence second, Positive first/Ambivalence last, Negative
23 first/Ambivalence second or Negative first/Ambivalence last10) between subjects design.
METHOD
Participants
Eighty undergraduate participants were recruited from the Ohio State
University research pool and received partial course credit for their participation.
Participants were randomly assigned to the experimental conditions and were tested in groups of 4 to 12.
Procedure
Behavioral information about a fictional person named Sam was presented in two phases and reactions to Sam were assessed using unipolar measures of positivity, negativity, and ambivalence after each wave of information. During the first information phase, all participants received six neutral behaviors (e.g., "Sam weighed the vegetables before buying them."). Four randomly assigned information conditions constituted the second phase: (1) six more neutral behaviors, (2) six positive behaviors (e.g. "Sam planned a surprise birthday party for an elderly neighbor"), (3) six negative behaviors (e.g., "Sam laughed at a coworker whose pet had died"), and
(4) 3 negative and 3 positive behaviors, randomly ordered. The behavioral items were
10The counterbalancing within form had one exception; the ambivalence scale was never presented first. Pilot testing had revealed that participants had difficulty in reporting ambivalence if they had not already reported positive or negative responses. 24 equally balanced between ability (pertaining to intelligence) and morality (pertaining to kindness or honesty) relevant behaviors11. Participants received booklets that contained one behavior on each page, and were timed through the booklet by the experimenter, spending seven seconds on each page.
After both the first and the second phases of information, participants reported their attitude toward Sam using the BEAMs (Bivariate Evaluations and Ambivalence
Measures; Cacioppo, Snydersmith, Crites, & Gardner; cf. Cacioppo, Gardner, &
Berntson, in press), unipolar scales designed to assess positivity, negativity, and ambivalence toward any target separately through using scale items that describe positive, negative or ambivalent evaluations (e.g. pleasant, unlikable, jumbled).
Participants reported their agreement with each item using a five point scale with endpoints labeled "not at all" and "extremely." During the generation of the BEAMs, two forms of the positivity and negativity subscales were created from 16 antonym pairs (e.g. positive/negative) such that the antonyms from the positivity subscale of
Form A make up the negativity subscale of Form B. Each positivity and negativity subscale had 8 items, The ambivalence subscale consisted of 9 items. The presentation order of the form of the BEAMs (Form A or B presented after the first or the second phase of information) was counterbalanced, as was the order of the positivity, negativity, and ambivalence subscales within each form. n The behaviors were selected from a pool of 80 behaviors that were pretested using 54 participants from the same population earlier in the quarter. Behaviors were pretested using a 1 (negative) to 9 (positive) scale with the midpoint (5) labeled "neutral." Behaviors that were selected to represent the Positive valence category were moderately positive (M= 7.82), those representing the Negative valence category were moderately negative (M= 2.24) and those that were neutral were at the midpoint of the scale (M = 4.88). See APPENDIX B.
25 Participants were encouraged to consider their positive and negative reactions towards Sam separately. The positivity subscale was preceded with instructions to the participant that read, in part:12
The purpose of this questionnaire is to assess the make up of your POSITIVE reactions to Sam. You should think carefully about ONLY your POSITIVE reactions to this person. Then rate the extent to which each of the adjectives that are listed on the next page describes your positive reactions. When using the adjectives to rate your positive reactions, you should think carefully about ONLY your positive reactions to Sam. That is, try to separate your positive feelings about Sam from any negative feelings you might have. Even then, not every positive adjective will be a good descriptor of your positive feelings, therefore, circle the scale description that most closely matches the relationship between your positive reactions and the adjective.
The negativity subscale was preceded with identical instructions referring to negative instead of positive reactions. The ambivalence subscale was preceded with instructions that read in part:
The purpose of this questionnaire is to assess the CONFIGURATION of your reactions to Sam. You should think carefully about the configuration of your positive and negative reactions to this person. Then rate the extent to which each of the adjectives that are listed on the next page describes this configuration. When using the adjectives to rate the configuration of your reactions, you should think carefully about the relationship between your positive and negative reactions. Then, try to decide how closely each of the adjectives describes this configuration. Not every adjective will be a good descriptor of the configuration between your positive and negative feelings, therefore, circle the scale description that most closely matches the relationship between the configuration of your reactions and the adjective.
12The full instructions and the BEAMs subscales are contained in APPENDIX C. 26 Filler scales (e.g. collecting demographic information, need for cognition, etc.) were
inserted between each subscale to further minimize carryover (Thompson et. al.,
1995). Finally, after participants rated Sam using the BEAMs after the second phase
of information, they also reported their attitudes toward Sam using five bipolar
semantic differentials.
RESULTS1314
Data from one participant was dropped because of failure to follow
instructions, thus the resulting analyses were performed on data from 79 participants.
Support for the positivity offset was revealed by within subject paired t-tests
examining the ratings after the first phase of information. Consistent with
expectations, a positivity offset was found in participants’ ratings such that reactions
to Sam were significantly more positive (M = 2.93) than negative (M = 1.76), t (78)
=11.46, p < .0 1 15
13Neither the Form order, nor the Subscale Order produced any significant main effects or interactions. Thus, the results reported here are collapsed across these factors.
14In this study and subsequent studies, slight changes in degrees of freedom reflect the presence of missing data.
15Empirical evidence argues for the equivalency of the subscales (Cacioppo, Crites, Snydersmith, & Gardner, in preparation). For example, past research in which the evaluation of tuition increases (a negative topic) was reported using the BEAMs showed no such offset. Thus, the effect reported here does not reflect differences between the positive and negative subscales themselves 27 Four separate indices were then computed for each participant to assess
impression change between the first and second ratings of Sam: (1) a difference score
between positivity after phase one and positivity after phase two, to illustrate the
direction and amount of change in positivity, (2) a difference score computed on the
two negativity ratings, (3) a difference score computed on the two ambivalence
ratings, and (4) an index of total change computed by the average of the absolute
values of each of the three difference scores. One-way ANOVAs with follow up
planned comparisons were conducted to examine these indices of impression change.
Both the positivity offset and the negativity bias were revealed in the direction and
magnitude of change in ratings of Sam16 (see Figure 3). The second prediction of the
positivity offset was supported by a planned contrast conducted on ambivalence
change; adding negative information caused an increase in ambivalence that was
significantly greater than adding positive or neutral information, t (74) = 4.96, p <
.01. Further, as predicted by the negativity bias, the changes in impressions of Sam
were larger in conditions in which participants received negative information
(negative or mixed) than for those conditions in which no negative behaviors were presented (positive or neutral). This pattern was seen across positive impression change (t (75) = -6.31, p < .01), negative impression change, (t (74) = 8.36, p < 01), and ambivalence change (t (74) = 4.75, p < ,01)17. Recall that absolute impression
16Results in all of the studies were also tested with ANCOVA to ensure that the differences in change that were seen could not be due to the initial values of the ratings of Sam, which were entered as covariates. The pattern of results did not change.
17The One-way ANOVAs were also significant for each index of change: positive change F (3,77) = 25.45, p <.01, negative change F (3,77) = 31.03, p < .01, 28 change was hypothesized to be largest for the condition in which negative information was presented in the second block. This hypothesis was supported by a planned contrast conducted on total change scores(74) (t = 5.91,P<01).
Finally, further evidence for the negativity bias was seen in the ratings of Sam after the mixed information condition. After receiving equal amounts of positive and negative information, ratings of Sam were more negative (M =2.94) than positive
(M= 2.00), t (19) = -3.67,p < .01. In contrast, participants who received all neutral information about Sam (a combination that the pretested ratings of the behaviors suggests should be evaluatively equivalent to the mixed information condition), rated
Sam as more positive (M = 2.39)than negative (M = 2.10), t (18) = 2.93,p < .05.
DISCUSSION
The bivariate model of evaluation thus received initial empirical support in
Study One. In particular, the hypothesized activation functions of positivity and negativity were strongly supported by the current findings. Consistent with expectations, a positivity offset was found in participants' ratings of Sam after the first wave of neutral information such that reactions to Sam were significantly more positive than they were negative. Both the positivity offset and the negativity bias were further supported in the changes in the evaluations of Sam after the second wave ambivalence change F (3,77) = 11.45, p <.01,and total change F(3,77) = 12.90, p <
. 01 . 29 of information. As expected if a positivity offset was indeed present, the addition of negative information resulted in an increase in ambivalence that was significantly greater than any increase in ambivalence after adding positive or neutral information.
Further, as predicted by the negativity bias, the magnitude of change in evaluation of
Sam was largest in the negative information condition. Finally, additional evidence for the negativity bias was seen in the mixed information condition; given mixed information, participants' evaluations of Sam were more negative than positive.
In addition to demonstrating the positivity offset and negativity bias, Study
One provided an opportunity to conduct exploratory analyses to investigate the relationship between positivity, negativity and ambivalence. Correlations among the initial ratings of Sam after the first phase of information were examined18. A large negative correlation between positivity and negativity would indicate the reciprocal activation of the two that the bipolar model demands. Conversely, a small or non significant correlation would challenge the bipolar model's assumption of reciprocal activation, but would instead imply that positivity and negativity may sometimes be activated independently, as the bivariate model allows. Examination of the correlations after the first phase of information revealed that positivity and negativity were uncorrelated, r= -.04, p > .70, providing greater support for nonreciprocal activation of positivity and negativity. In sum, the pattern of results demonstrated in
18The correlations after the second phase of information were also examined, separated by condition. However, the number of participants in each condition was small, and thus these analyses do not provide as reliable a test as the ratings of Sam after the first phase of information. Correlational analyses investigating condition were performed across all five studies in a combined dataset; these results are presented in Table One and discussed in Chapter Six. 30 Study One suggest that positivity and negativity may be distinguishable evaluative substrates with distinct activation functions.
31 CHAPTER 3
STUDY TWO
The results of Study One were promising in their support of the predictions of the bivariate model, however the demonstration of the negativity bias remained open to an alternative explanation — one of diagnosticity. Recall that Skowronski and
Carlston (1987, 1989) used diagnosticity to explain negativity, positivity, and extremity biases in impression formation. In short, they argued that impressions are driven by social categorization processes and that these categorizations depend upon the diagnosticity of a given behavior in exemplifying a trait category (e.g. intelligent).
The diagnosticity of a behavior is determined by both the valence of the behavior and the applicability of the behavior to either the morality or the ability domain. Both of these domains represent hierarchically restricted schemata (Reeder & Brewer, 1979) but in opposite fashion. In the morality domain, behaviors at the high end of the continuum are restricted, moral people being unable to exhibit immoral acts, but immoral people free to display either moral or immoral behavior. Thus, in morality relevant domains, negative behaviors provide a higher level of diagnosticity and thus should receive greater weight in the impression. Ability relevant domains are restricted in the reverse fashion; people high in ability can exhibit behavior high or low in ability, but low ability targets can only exhibit low ability behaviors. For this 32 reason,positive behaviors are more diagnostic of ability, and should be weighted more heavily in the impression.
Recall that in Study One an equal number of ability relevant and morality relevant behaviors were presented, and strong evidence for a negativity bias was found. Although this evidence suggests that the negativity bias exists in both domains, it is possible that morality relevant and ability relevant behaviors themselves may carry unequal weight in impression formation. Carolien Martijn and her colleagues (Martijn, Spears, Van der Pligt & Jakobs, 1992) found that, when both morality relevant and ability relevant traits (e.g. unkind, intelligent) were presented together, the morality relevant traits were dominant in determining impressions of a target. Therefore, the current finding that a negativity bias was evidenced even when both morality and ability relevant behaviors were used could have been driven by the morality relevant behaviors alone. If negative morality relevant behaviors are more diagnostic than positive morality relevant behaviors, and morality relevant domains carry more weight in impression formation, then the negativity bias found in Study
One could be explained sufficiently by diagnosticity.
For this reason, a second study was run to examine the predictions of the bivariate model for both morality relevant and ability relevant domains. The methodology was identical to that in Study One, except that half of the participants read only morality relevant behaviors (pertaining to honesty and kindness) and half of the participants read only ability relevant behaviors (pertaining to intelligence).
33 Participants once again received neutral information about Sam in the first phase19 and
completed the BEAMs after each phase. Thus, the experiment was a 2 Domain
(Morality or Ability relevant) X 3 Valence of Second Phase of Information (Positive,
Negative, or Mixed) between subjects design, with 1 All Neutral Information floating
control20.
The predictions for Study Two are identical to those for Study One. The
positivity offset should be evident in the ratings of Sam after the first phase of neutral
information, as well as in the increase in ambivalence for those participants who
receive negative information in the second phase. As for the negativity bias, if the
negativity bias found in Study One was due solely to diagnosticity, than it should be
present for the participants in the morality relevant condition, but absent for those in
the ability relevant condition. If, however, the negativity bias is a result of a more
general evaluative principle, then even those participants in the ability relevant
conditions should show evidence of negative behaviors receiving greater weight.
Specifically, absolute impression change should be highest for those who received
negative information in the second phase, and the impressions of Sam in the mixed
information condition should be more negative than positive.
19The neutral behaviors were pretested to ensure that they were considered non diagnostic of kindness, honesty, or intelligence. All participants read the same neutral behaviors.
20Because variations of Form Order and Subscale Order showed no effects in Study One, these factors were dropped in Study Two for ease of administration. 34 METHOD
Participants
Eighty-three undergraduate participants were recruited from the Ohio State
University research pool and received partial course credit for their participation.
Participants were randomly assigned to the experimental conditions and were tested in groups of 4 to 12.
Procedure
The procedures for Study Two were identical to those for Study One, with the exception that half of the participants were randomly assigned to read all morality relevant behaviors, and half were assigned to read all ability relevant behaviors. The items on the BEAMs, as well as the filler scales, were identical to those in Study One.
RESULTS
Within subject paired t-tests were again conducted on participants ratings of
Sam after the first phase of neutral information21. The positivity offset found in Study
One was again evidenced in participants’ ratings such that reactions to Sam were
21Recall that all participants read the same six neutral behaviors in the first phase of the experiment, none of which were considered morality or ability relevant. Thus, this analysis was performed without Domain as a factor.
35 significantly more positive(M “ 2.79) than negative(M = 1 -35), t (83)=12.09, p <
. 01 .
The four indices of impression change described in Study One were computed
for each participant in Study Two. A 2 Domain (Morality or Ability relevant) X 3
Valence of Second Phase of Information (Positive, Negative, or Mixed) between
subjects ANOVA was then conducted to examine the predictions of the bivariate
model for impression change22. Results revealed valence main effects for each of the
indices of change: positivity change, (Mpos. = -93, Mneg = -1-20, Mmix.= "-56 ),
F (2,68) = 32.84,p <.01, negativity change, (Mp0S =-.34, Mneg = 1.82,
Mmix.= 142), F (2,68) = 38.04, p <.01, ambivalence change, (Mpos.=--58,
M neg.~ 15,Mm i x -31), F (2,68) = 13.23, p <.01, and total change, (MpOS,= .69,
Mneg.= 1 -26, Mmix.= -94 ), F (2,68) = 8.39, p <.01. Follow up pairwise
comparisons using Scheffe tests revealed that for the positivity, negativity, and
ambivalence change scores, the changes in impression after receiving positive
behaviors in the second information phase were in the opposite direction and
significantly different than the changes in impression caused by either the negative or
22Participants in the neutral condition had to be excluded from this analysis as there was only one neutral condition in which the behaviors presented were neither morality nor ability relevant. Follow up planned comparisons revealed that the neutral condition was equivalent to the positive condition in all comparisons except for ambivalence change after reading ability relevant information. Ambivalence decreased to a significantly greater extent for participants who received positive ability relevant behaviors in the second information phase (M = -.60) than for those participants who received neutral behaviors in the second phase (M = .02). Ambivalence change in the neutral condition was in turn significantly smaller than for the negative behavior (M = -.25) or mixed behavior I'M = .23) conditions.
36 the mixed behaviors conditions, which did not differ. Total change, on the other hand, was significantly higher for the negative condition than for the positive or mixed information conditions, which did not differ.
Analyses of Domain (Morality, Ability) replicated prior research in the impression formation literature. First, a main effect for Domain was found for negativity change, such that increases in negativity were generally larger for the
Morality (M = 1.17) than the Ability(M = -77) domain. Second, a similar pattern of results was found for total change, Morality(M = 1.08), Ability (M = -85), probably driven by the enhanced amount of negativity change for the morality relevant condition. Most important for the present inquiry, none of these findings were qualified by a Valence X Domain interaction23, (see Figures 4 & 5).
For the second test of the positivity offset, the conditions in which participants received all negative behaviors in the second block of information were contrasted with the conditions in which positive or neutral behaviors comprised second blocks and effects on ambivalence change were examined. The evidence for the positivity offset found in Study One was replicated, the addition of negative information to neutral information caused an increase in ambivalence that was significantly greater than adding positive or neutral information, t (77) = 3.16, p < .01.
23 Although no Domain X Valence interaction was found, the graphs of impression change are plotted separately for the Morality (Figure Four) and Ability (Figure Five) relevant conditions. In this way, the similarity of patterns across Domains can be seen. In addition, the planned contrasts that provided critical tests of the positivity offset and negativity bias were also conducted separately within each Domain. Because the pattern of results was identical for these tests, we report the contrasts performed on the full dataset here. 37 As predicted by the negativity bias, total impression change was significantly larger for those participants who received negative(M = 1.26) behaviors than for those receiving mixed (M= .94) or positive (M= .69) behaviors in the second information phase, t(77) = 4.37, p < .01 . Finally, as an additional test o f the negativity bias, the ratings o f Sam after the mixed information conditions were examined. After receiving equal amounts of positive and negative information, ratings of Sam were more negative then positive (Mneg_=2.80, Mgps_= 2-26), t (23) = -2.16,p< .05.
DISCUSSION
The results of Study Two replicated and extended those of Study One. Once again, the predictions of the positivity offset were supported both in initial ratings of
Sam after neutral information, and in the increase in ambivalence after the addition of negative information. More importantly, the results of Study Two suggest that the negativity bias is not merely a product of cue-diagnosticity. Although greater negativity was found for participants who received morality relevant behaviors, a negativity bias among those who read only ability relevant behaviors was also demonstrated. Negative information resulted in the greatest amount of total impression change across both morality and ability relevant domains; no Valence X
Domain interaction was found. Further, in both mixed information conditions, participants' evaluations of Sam were more negative than positive.
The main effect of Domain that showed larger negative impression changes in the morality relevant domain suggests that evaluations are indeed affected by cue- 38 diagnosticity. It is important to note, however, that the lack of interaction implies that the asymmetrical transfer functions of positivity and negativity are evidenced in evaluations across both types of behaviors. Thus, the results of Study Two do not negate the findings of Skowronski and Carlston (1987) but rather suggest that evaluations are not a result of cue-diagnosticity alone. Instead, the results of Study
Two imply that both diagnosticity and the evaluative impact of the information play a role in impression formation. Consistent with a negativity bias, negative information of both types had greater impact. Skowronski and Carlston (1987) themselves reported memory results that seemed inconsistent with the impact of cue diagnosticity, but do speak to the greater impact of negativity. Although their primary dependent variables were trait categorizations, they also tested participants' memory for the behaviors. This memory data revealed that although positive ability relevant behaviors were more diagnostic of intelligence than negative behaviors were of unintelligence, the negative behaviors proved to be more memorable to participants. Skowronski and Carlston (1987) proposed that the greater memorability of the negative behaviors suggested greater attention to or increased processing of these negative behaviors, a conclusion consistent with the psychological mechanisms proposed both here and in the prior literature to underlie the negativity bias.
In Study One, exploratory analyses of the correlations among positivity, negativity, and ambivalence ratings after the first block of information were supportive of a bivariate rather than bipolar conceptualization of evaluation. The correlations among the subscales in Study Two replicated these findings. In Study
Two, as in Study One, the correlation between positivity and negativity was found to be non-significant (r = -.07). Further, the finding that the relationship between 39 positivity and ambivalence (r = .21, n.s.1 was smaller than the relationship between negativity and ambivalence (r = .35, p < .01) was reproduced in this dataset as well.
These results are consistent with the bivariate conceptualization of positivity and negativity as distinct components of evaluation, and further demonstrate the effect of the differential transfer functions, particularly the positivity offset, on evaluative conflict.
40 CHAPTER 4
STUDY THREE
The results of Study Two provided evidence that diagnosticity alone could not
account for the negativity bias. However, is it possible that diagnosticity could have
possibly accounted for the positivity offset? Although the neutral behaviors used in
Studies One and Two were diagnostic of neither kindness nor intelligence, they could be perceived to be in some sense implying at least a threshold amount of both of these qualities. For example, the fact that Sam is in a grocery store weighing vegetables may arouse no particular attribution of kindness or intelligence, but it may rule out the attribution that Sam is extremely immoral (imprisoned for heinous crimes) or extremely unintelligent (institutionalized for severe retardation)24. Thus, a third study was conducted in which the diagnosticity of the neutral behaviors was varied. Study
Three was designed to compare the positivity offset found after evaluatively neutral but possibly diagnostic behaviors with behaviors that were both neutral and completely non-diagnostic.
To this end, a set of behaviors was created and pretested that were thought to be applicable to all humans (e.g. "Sam breathed oxygen."). Twelve of the behaviors that were identified in pretesting as both evaluatively neutral and applicable to 100%
24We thank Jon Krosnick for calling our attention to this alternative explanation. 41 of the human population were selected for inclusion into the non-diagnostic neutral set25 . Participants were led to believe that they were participating in a study concerning artificial intelligence. They were told that a computer was being "taught" to simulate impression formation through generating a set of behavioral descriptors of a person, and then receiving feedback about how "real people" responded to this set of behaviors. This cover story allowed the presentation of the non-diagnostic neutral behaviors in a plausible setting. In the first wave of information, 80% of the participants received the non-diagnostic neutral set, and the other 20% received the neutral set of behaviors that was used in Studies One and Two. The conditions for the second wave of information were identical to those in Study One, with the exception that those participants in the condition to receive six more neutral behaviors received neutral behaviors of the same type (non-diagnostic or regular) as they had received in the first information phase. Thus, five between subject conditions made up the design of this study, the four Valence of Second Phase of Information conditions (Neutral, Positive, Negative, or Mixed) presented after non-diagnostic neutral information and an additional all neutral condition that used neutral behaviors from Studies One and Two.
The predictions concerning the negativity bias were identical to those in
Studies One and Two; negative information should result in the greatest amount of absolute impression change, and evaluations of Sam after the mixed information condition should be more negative than positive. Similarly, the positivity offset was expected to be manifested both in the ratings of Sam after the first set of neutral behaviors, and the increase in ambivalence after the addition of negative behaviors.
25See APPENDIX B. 42 Importantly, if the positive responses to Sam after neutral information that were found in Studies One and Two resulted from the neutral behaviors implying threshold amounts of kindness and/or intelligence, then it should be absent for the 80% of participants in Study Three that received the non-diagnostic neutral behaviors. If, however, the positive reactions to Sam after neutral information was a result of a general positivity offset in the evaluative system, then the diagnosticity of the neutral behaviors should have no effect.
METHOD
Participants
Ninety-two undergraduate participants were recruited from the Ohio State
University research pool and received partial course credit for their participation.
Participants were randomly assigned to the experimental conditions and were tested in groups of 4 to 12.
Procedure
The procedures for Study Three were identical to those for Studies One and
Two, with two exceptions. First, the cover story for Study Three was different from the previous two studies in that participants believed that they were taking part in an experiment concerning artificial intelligence26. Second, 80% of the participants
26 APPENDIX D contains a copy of the script used to convey the artificial intelligence cover story to the participants. 43 received non-diagnostic neutral behaviors, whereas the remaining 20% received the
regular set of neutral behaviors. All other procedures and all dependent measures
were identical to those in Studies One and Two.
RESULTS
The first hypothesis concerning the diagnosticity explanation of the positivity
offset was tested using within subject paired t-tests conducted separately for each
type of neutral behavior. The positivity offset after initially neutral behaviors was
replicated for both the regular set of neutral behaviors (Mp0S = 2.92, Mneg = 1.75),
t(19) = 5.23,g<.01 and for the non-diagnostic set of neutral behaviors
(M pos, = 2.87, M n e g = 1.40), t (73) = 10.74, p < .01.
The four indices were then computed for each participant to assess impression
change between the first and second ratings of Sam, and One-way ANOVAs followed
by planned comparisons were conducted on each index. Both the positivity offset and the negativity bias were replicated in the direction and magnitude of change in ratings
of Sam. As predicted if a positivity offset was present, adding negative information
caused an increase in ambivalence that was significantly greater than adding positive
or neutral information t_(87) = 3.28, g < .01. As predicted by the negativity bias, the changes in impression of Sam were larger in conditions in which participants received negative information (negative or mixed) than for those conditions in which no negative behaviors were presented (positive or either of the neutral conditions). This pattern was seen across positive change, t (88) = -5.32, g <.01, negative change 44 t (87) = 11.97, p < .01, and ambivalence change t (87) = 4.99, p <.01. The finding
that negative information had the greatest absolute impact on total impression change
was also replicated here, t (86) = 7.29, p < ,0127. (see Figure 6).
Evidence for the negativity bias was again apparent in the ratings of Sam after
the mixed information condition. After receiving equal amounts of positive and
negative information, ratings of Sam were more negative(M = 3.32) than positive
(M= 2.52), t (16) = 2.82, p < .05.
DISCUSSION
The results of Study Three replicated the results of the prior two studies.
Both a positivity offset and a negativity bias were evidenced in the hypothesized
fashion. Further, Study Three provided no support for a diagnosticity explanation of
the positivity offset. Indeed, the positive response to Sam after the first set of neutral
behaviors was strongly evident even when the behaviors were applicable to 100% of
the human population. In fact, the difference between initial positivity and negativity
appeared slightly larger for the non-diagnostic neutral information conditions.
Likewise, the addition of negative behaviors to these non-diagnostic neutral behaviors
caused an increase in ambivalence that was of similar magnitude to that found in
27The Omnibus F statistics for each of these indices were: positive change F (4,92) = 19.23, p <.01, negative change F (4,92) = 42.38, p < .01, ambivalence change F (4,92) = 7.40, p <.01, and total change F (4,92) = 20.07, p < .01.
45 Studies One and Two. The combined results of Studies Two and Three, therefore discount a diagnosticity explanation as being sufficient to fully explain both the positivity offset and the negativity bias in impression formation.
Finally, the correlations among the initial ratings of positivity, negativity, and ambivalence in this study replicated the pattern of correlations found in the previous studies. The correlation between initial positivity and negativity was again found to be non-significant (r_= 05, .n.s.) and the correlation between positivity and ambivalence (r = . 18, n.s.l. was smaller than that between negativity and ambivalence
(r = .61, g < .01). Once again, this pattern of data is difficult to reconcile with a bipolar and reciprocal model of evaluative processing.
46 CHAPTER 5
STUDIES FOUR AND FIVE
Despite the supportive evidence for the bivariate model provided by the
results of the prior studies, one plausible explanation of the positivity offset found in
these studies still remains. All three studies used a human being as a target. Even if
behaviors are applicable to the entire human race, if one has generally positive feelings
toward conspecifics the evaluation of the general category "human" may in and of
itself account for positivity offset. It is possible that participants' positive reactions to
Sam after even non-diagnostic neutral information was due simply to the fact that
Sam was still fundamentally similar to our participants, and that this similarity
increased liking (Sears, 1983). Thus, Studies Four and Five were conducted to test
the hypotheses of the bivariate model using non-human targets.
The conditions in Studies Four and Five were similar to those of the prior
studies. All participants received a block of neutral information concerning a fish
(Study Four) or an insect (Study Five) and then reported their positivity, negativity and ambivalence. The second block of information was varied to be neutral, positive, negative, or mixed, and participants' evaluations were recorded a second time. Thus, each of the studies had four experimental conditions.
47 The information participants read about the fish in Study Four or the insect in
Study Five was kept as similar as possible in content and pretested valence and extremity. For example, one positive item in Study Four was "The presence of the
Aguaphore is beneficial to the coral reef in which it lives" (M = 7.51); the corresponding positive item in Study Five was phrased as "The presence of the
Entophore is beneficial to the rainforest in which it lives" (M = 7.62). Thus, any differences that might be found between the studies in evaluative responding would be attributable to the target, rather then to content differences in the information presented.
The cover story used in Study Three was modified slightly and used for
Studies Four and Five. Participants were again told that they were taking part in an experiment concerning artificial intelligence, and that their impressions would assist the computer in learning to perceive and evaluate various objects in a way that mimicked human processing. They were told that the computer was being "trained" to evaluate various people, animals, political issues, and objects, and that they would be receiving information and evaluating one of these target objects. All other instructions were identical to those used in Study Three.
The predictions concerning the negativity bias were identical to those in the prior studies; negative information should result in the greatest amount of absolute impression change, and evaluations of both targets after the mixed information conditions should be more negative than positive. Similarly, the positivity offset was expected to be manifested both in the ratings of the targets after the first set of neutral behaviors, and the increase in ambivalence after the addition of negative behaviors. Importantly, if the positive responses to Sam after neutral information that 48 were found in Studies One through Three resulted from a general positivity that is felt
for all human beings (Sears, 1983), then it should be absent in both Studies Four and
Five, because the fish and insect that served as targets should not evoke liking based
on similarity to the participant. If, however, the positive reactions to Sam after
neutral information was a result of a general positivity offset in the evaluative system, then evidence for the positivity offset should be observed regardless of the nature of the target.
METHOD
Participants
Forty-eight undergraduate participants in Study Four, and forty-eight participants in Study Five were recruited from the Ohio State University research pool and received partial course credit for their participation. Participants were randomly assigned to the experimental conditions and were tested in groups of 4 to
12.
Procedure
The procedures for Studies Four and Five were identical to those for Study
Three, with small modifications. First, the cover story for Studies Four and Five was changed slightly to allow the evaluation of non-human targets. Second, the information participants received concerned either a fish (Study Four) or an insect
(Study Five). The information presented was from a new set of items that had been 49 pretested for valence and extremity28. All other procedures and all dependent
measures were identical to those in Study Three.
RESULTS
The first hypothesis concerning the "similarity-attraction" explanation of the
positivity offset was tested using within subject paired t-tests on participants ratings
of the target after the first block of neutral information. The positivity offset after
initially neutral information describing the fish in Study Four was readily apparent,
(Mpps = 3.08, Mneg = 1.64 ), t (47) = 9.08 , p < .01. Similarly, the positivity offset
manifested itself even in participants responses to the insect target in Study Five,
(Mpos,= 2.63, M n eg = 1.92 ), t (47) = 3.19 , p < .01.
One-way ANOVAs followed by planned comparisons were conducted to examine the four indices of impression change. These analyses were conducted separately for each study, but revealed a very similar pattern (see Figures 7 & 8).
Both the positivity offset and the negativity bias were replicated in the direction and magnitude of change in ratings of both the fish and insect targets. First, the addition of negative information caused an increase in ambivalence that was significantly greater than adding positive or neutral information in both Study Four, t (44) 5.27, p
< .01, and Study Five, t (44) 2.23, p < .05. As predicted by the negativity bias, the
28See APPENDIX B.
50 impression of both targets changed to a larger degree in conditions in which participants received negative information (negative or mixed conditions) in the second block as compared to receiving positive or neutral information. This pattern was present in Study Four for : positive change t (44) = -6.65, p <.01, negative change t (44) = 7.99, p < .01, ambivalence change t (44) = 5.91, p <.01. Further, analyses of total impression change revealed that receiving negative information had the greatest impact on absolute impression change t (44) = 5.09, p < .01.29
This pattern of results remained stable in Study Five: positive change t (44) = -6.61, p <.01, negative change t (44) = 7.53, p < .01, ambivalence change t (44) = 5.27, p <.01. The planned contrast of the negative information conditions with all other conditions for total impression change was significant in Study Five as well, t (44) = 6.95, p < ,013°.
Finally, the means for positive and negative responses were examined in the mixed information conditions of both studies. After receiving equal amounts of positive and negative information, ratings of the fish were more negative (M = 2.79) than positive (M= 2.19), t (11) = 2.26, p < .05; as were ratings of the insect (Mneg.=
2.84, Mp0 S 2.35), although in Study Five this difference failed to reach statistical significance, t (11) = 1.41 , p = .18.
29The Omnibus F statistics for these indices in Study Four were: positive change F (3.47) = 20.63, p <.01, negative change F (3,47) = 25.26, p < .01, ambivalence change F (3,47) = 12.39, p <.01, and total change F (3,47) = 10.57, p < .01.
30The Omnibus F statistics for Study Five were as follows: positive change F (3,47) = 24.27, p <.01, negative change F (3,47) = 25.05, p < .01, ambivalence change F (3.47) = 2.85, p <.05, and total change F (3,47) = 15.77, p < .01. 51 DISCUSSION
The results of Studies Four and Five revealed the same patterns of evaluative
response that were found in Studies One through Three. Both the positivity offset
and the negativity bias were demonstrated in the predicted fashion in impressions of
non-human targets. Importantly, these results discount the "similarity-attraction"
explanation of the positivity bias or positivity "bonus" (Sears, 1983). Had the
positivity offset been dependent on perceived similarity to another human being, it
would obviously have been absent in ratings of the fish and insect targets in Studies
Four and Five.
Similarly, the demonstration of the negativity bias in evaluations of non-human
targets implies that this effect is probably not based on expectancies for behavior
caused by social norms (Jones & Davis, 1965; Jones & McGillis, 1976; Fiske, 1980).
Although the social norms of current society do produce more frequent positive than
negative displays of behavior in humans, and knowledge of these norms may lead us to weigh negative behavior more heavily in judgment, it is highly unlikely that we would apply this logic to evaluating the behavior of fish or insects, who are presumably unconstrained by the rules of polite society.
Finally, examinations of the correlations of initial ratings in both Study Four and Study Five replicated the findings of Studies One through Three. Correlations between positivity and negativity were found to be non-significant in both Study Four
(r = -.08, n.s.) and Study Five (r = -. 15, n.s.l. In addition, in both Study Four and
Study Five, the relationship between positivity and ambivalence was found to be 52 smaller (rs = -.01, hjl, and .22 n.s. respectively) then the relationship between negativity and ambivalence (rs = -.58, p < .01 and .45 p < .01, respectively).
The combined results of Studies Four and Five then, support the existence of the positivity offset and negativity bias as general attributes of the evaluative system.
Although processes such as similarity leading to attraction and expectancy violation leading to greater impact may modify the effects of the positivity offset and negativity bias, they are not necessary to produce biases in impression formation. The differential activation functions of the positive and negative evaluative systems, as proposed by the bivariate model of evaluation, appear to be sufficient to produce the positive and negative biases commonly seen in the impression formation literature.
53 CHAPTER 6
GENERAL DISCUSSION
The results of the present research revealed a consistent asymmetry in the
impact of positive and negative information in the evaluative reactions towards a
target. Taken as a whole, Studies One through Five illustrate the predictive validity
of a bivariate model of evaluative processing. Within this general model of
evaluation, positivity and negativity are considered to be separable and non-
interchangeable evaluative substrates that are supported by distinct neural mechanisms
and further distinguished by dissimilar activation functions. As a result of the
differential activation functions of positivity and negativity, evaluative processing is
predicted to be characterized by both a positivity offset and a negativity bias. We
began the current research with six hypotheses designed to test the bivariate model of
evaluation within the domain of impression formation. The combined results of the
studies presented here provided robust support for the bivariate model; the
predictions of the distinct activation functions of positivity and negativity were
supported across all five studies.
Our first hypothesis stated that receiving neutral information about a target should result in impressions that are more positive than negative. This hypothesis received support across all five studies. The positivity offset remained in evidence 54 even when the neutral behaviors were constructed to be completely non-diagnostic,
applicable to 100% of the human population (e.g. "Sam breathes oxygen"; Study
Three). Further, the positivity offset did not appear to be limited to evaluations of humans, but was also observed in evaluations of a fish (Study Four) and an insect
(Study Five). The positivity offset demonstrated in this work, then, could not have been a result of the neutral behaviors implying the absence of negative attributes.
Neither could it have merely reflected the process of similarity leading to attraction, as Sears' (1983) "person positivity bias" would have predicted. Instead, the positivity offset appeared to be a more general feature of the evaluative system.
The second hypothesis concerning the positivity offset was that the receipt of negative information after initially neutral information should initiate a state of conflict or ambivalence. This hypothesis was supported in each of the five studies; reported ambivalence after the second block of information increased to a significantly greater extent for those participants who received negative information as compared to participants who received positive or neutral information in the second block.
The consistent finding of increased ambivalence after the receipt of negative information was informative for two reasons. First, recall that the majority of the explanations of the positivity bias in impression formation characterized this bias as a positive expectancy that resulted either from the frequency of positive acts in a society that punishes negative conduct (Jones & Davis, 1965) or from the evolutionary adaptiveness of holding such positive expectancies in general (Peters,
1971; Peeters & Czipanski, 1990). From this perspective, positive ratings of a neutral target would be an expression of expectancy, like a psychological promissory note for future positive feelings rather than actual positive evaluative output. As such, these 55 positive expectancies, when violated by negative information, could lead to an increase in the impact of that information, but presumably would not produce evaluative conflict. In contrast, the increase in ambivalence detected here implies that participants' positive ratings after the first block of information reflected actual positive evaluative responses rather than mere positive expectancies.
Second, the finding of increased ambivalence after the addition of negative to neutral information is informative because it is difficult to reconcile with the dominant bipolar perspective of attitudes and evaluative process. From the bipolar perspective, a person who receives neutral information should hold an attitude that is psychologically at the center of the evaluative dimension; any subsequent information should move that attitude toward the positive or negative endpoint. If the information received is purely positive or purely negative, there should be no reason to feel conflicted except in unusual circumstances, such as when the evaluator is acting conservatively because of low confidence or high "personal fear of invalidity"
(Thompson & Zanna, 1995; Kruglanski, 1989). In contrast, the bivariate model predicted conflict based upon the existence of a positivity offset; thus an increase in ambivalence was hypothesized and found to occur after the addition of purely negative information but not after the addition of purely positive information.
Importantly, this pattern of results cannot be explained simply from a bipolar perspective. Even if we believed that all 349 of our participants were high in personal fear of invalidity (a shaky assumption, at best), this type of subject population would exhibit increased evaluative conflict across all conditions (Thompson & Zanna, 1995), instead of the predicted asymmetry in ambivalence that occurred here. Thus, the consistent support found for Hypothesis Two suggests that the bivariate 56 conceptualization, which subsumes the bipolar perspective, provides a more general
and extensive model of attitudes and evaluative process.
Hypothesis Three concerned the negativity bias. We predicted that negative
information should have the greatest impact on impressions in general, because of the
steeper slope of the activation function of negativity. In each of the five studies this
effect was significant; total impression change was larger for those participants who
received negative information in the second block than for any other experimental condition. Further, the same pattern of results was found even when initial levels of positivity, negativity, and ambivalence were statistically controlled for. It was possible that negative information produced greater absolute change as a result of the initial values that reflected the positivity offset. These values could have limited the extremity of positive change from first to second ratings, whereas the extremity of negative change would be less restricted. Results of ANCOVAs that entered as covariates the ratings after the first block of information, however, also revealed the greater impact of negative information on impression change. Thus, the plausible but uninteresting possibility that these results were due to methodological artifact was found to be nontenable .
The fourth and fifth hypotheses of the bivariate model in impression formation concerned the negativity bias and the effect of mixed information. Given equally positive and negative information, the resulting impression was predicted to be more negative then positive as a result of the negativity bias. This pattern of results was found across the five studies. Importantly, this negativity bias was thought to be a general feature of the evaluative system rather than merely a result of attributional processing. Because Study One used both morality and ability relevant behaviors, the
57 finding of a negativity bias in the mixed condition of Study One was promising.
However, morality relevant attributes may simply have been weighted more heavily in
the overall evaluation (Martijn et. al., 1992).
The results of Study Two were thus particularly important because the
diagnosticity of the positive and negative information was varied in separate
conditions. The main effect of Domain in Study Two showed that greater negativity
was reported overall for the morality relevant behaviors, a finding consistent with the
past literature exploring diagnosticity and biases in impression formation (Skowronski
& Carlston, 1987, 1989). Importantly, this effect was not qualified by a Domain X
Valence Condition interaction; the negativity bias as revealed by the stronger impact
of negative than positive behaviors in the mixed condition was supported across the
evaluations of both morality relevant and ability relevant behaviors. Thus, negative
impressions may be more likely when assessing morality relevant information because
of the addition of processes concerned with the assessment of diagnosticity, but the
negativity bias was not found to be limited to morality relevant information,
suggesting that it is unlikely that the negativity bias results from diagnosticity alone.
The final prediction of the bivariate model concerned its generality. Many of
the prior explanations of positivity and negativity biases in impression formation
assumed that these biases were exclusive to person perception. Sears' (1983)
explanation of the positivity bias as a product of liking due to basic similarity with
another human target obviously limits this bias to the impressions of other people.
Similarly, explanations of the positivity bias that rest upon expectations of normal
human conduct (Jones & Davis, 1965; Matlin & Stang, 1978, Fiske, 1980) predict this positive expectancy only in the evaluation of human behavior. Likewise, 58 explanations of the negativity bias that rest upon the costs of norm violations within
polite society (Kanouse & Hanson, 1972; Jones & McGillis, 1976; Fiske, 1980) also
assume a human target. The results of Studies Four and Five, in which non-human
targets (a fish and an insect) were evaluated, suggest that none of these human-
centered explanations is sufficient to account for either the positivity offset or the
negativity bias, but instead may be a result of the asymmetries predicted by the
bivariate model of evaluation. Because the bivariate model is a general model of
evaluation, rather than a model of impression formation, the asymmetries in activation
function are not assumed to be limited to evaluations of humans, but rather are
thought to be fundamental features of the evaluative system, and as such, are broadly
applicable to any target.
The support found for the bivariate model in the domain of impression formation is persuasive precisely because the bivariate model is not a model of
impression formation, but rather a general model of evaluation. Unlike the prior theories that were often explicit attempts to explain the positive and negative biases
observed in the impression formation literature, the bivariate model was instead conceived on the basis of neurophysiological data implying separable evaluative subsystems for positivity and negativity (see Cacioppo & Berntson, 1994 or
Cacioppo, Gardner, & Berntson, in press, for review). Despite the fact that the model was not designed specifically for impression formation, the assumption of distinct activation functions accommodates both the existing literature on impression asymmetries and the current dataset rather well. In addition, exploratory analyses of the five studies that examined the correlations among positivity, negativity, and ambivalence ratings provided evidence that further supported the bivariate model. 59 Specifically, the bivariate model of evaluation proposes that positivity and
negativity represent distinct evaluative substrates that can be reciprocally activated,
independently activated, coactivated, or coinhibited. A strict bipolar perspective, on
the other hand, allows only reciprocal activation of positivity and negativity, as they
represent the endpoints of a single evaluative dimension. The correlations between
positivity and negativity are therefore of interest in exploring the two models. A large
negative correlation between positivity and negativity would indicate the reciprocal
activation that the bipolar model demands. Conversely, a small or non-significant
correlation would challenge the bipolar model's assumption of reciprocal activation,
and would instead imply that positivity and negativity may sometimes be activated
independently, as the bivariate model allows. Recall that examination of the correlations after the first phase of information revealed that positivity and negativity were non-significantly correlated in each of the five studies. Further, in each of the
studies, the correlation between positivity and ambivalence was smaller than the correlation between negativity and ambivalence. This latter finding was interpreted as another manifestation of the positivity offset; any negativity felt after the first block of neutral information would be in conflict with the positivity that this information was expected to arouse, and thus would engender ambivalence.
The correlations among positivity, negativity, and ambivalence after the second block of information could not be tested within a single study, because of the small number of participants in each condition. Therefore, to examine whether receiving positive, negative, more neutral, or mixed information in the second block affected the relationships among positivity, negativity, and ambivalence, the data from all five studies were combined into a single data set. This data set was large enough 60 to provide reliable correlation coefficients within each information condition31.
Before examining the correlations within condition, however, the correlations among the three subscales were examined after the first block of information, with a combined sample size of 349. Even with this large sample, the correlation between positivity and negativity was found to be small (r = -.06, n.s.). The correlation between positivity and ambivalence (r = .15, p < .01) was small but significant, and the correlation between negativity and ambivalence (r = .48, p < .001) was moderate in size and significant.
The correlations among the second ratings within each of the different information conditions are depicted in Table 1. Interestingly, even the second ratings of positivity and negativity were only moderately correlated (rs ranged from -.11, ry^ to -.52 p < .001 )32. The correlations between positivity and ambivalence also remained small in size (rs ranged from -.22, as^ to . 16 n.s.). and the pattern of these correlations was intuitively sensible. For example, positivity was negatively related to ambivalence in the positive information condition, but positively related to ambivalence in the negative information condition. Finally, in all cases the correlation between negativity and ambivalence was moderately positive and significant (rs ranged from .29, p <.01 to .58, p< .001.), the pattern that would be predicted by the positivity offset. Thus, the pattern of correlations in the present research was more compatible with the bivariate than the bipolar model of evaluative
3'The cell sizes for each of the conditions in the collapsed set ranged from 84 to 92 (see Table One).
32These correlations do imply a move toward reciprocal activation after recieving the second block of information. 61 processes. The low correlations between positivity and negativity provided support for the existence of nonreciprocal modes of activation, implying that positivity and negativity may indeed be distinguishable evaluative substrates, and the consistent positive relationship between negativity and ambivalence provided additional evidence of the positivity offset.
Support for the Bivariate Model: Fact or Artifact?
A host of potential alternative but uninteresting explanations of the results of
Studies One through Five rest upon the possibility of artifactual responding as a result of various aspects of the methods and measures used. For example, it could be argued that the small correlations between positivity and negativity in the present research are merely an artifact of the BEAMs scales themselves, or a result of systematic response artifacts resulting from the use of unipolar scales in general. We are confident that the low correlation between positivity and negativity is not merely a result of wording on the BEAMs because, unlike other scales of positivity and negativity (such as the Positive and Negative Affect Scale, Watson, Clark, &
Tellegen, 1988) that use different words to describe the different affective states, the
BEAMs were constructed using antonyms to describe positivity and negativity
(Cacioppo, Crites, Snydersmith & Gardner, in preparation). In addition, we have observed it in endorsements of positive and negative beliefs towards blood and organ donation (Cacioppo & Gardner, 1993; Gardner & Cacioppo, 1995; Gardner and
Cacioppo, under review), affect towards college roomates using the PANAS scale
(Cacioppo, Crites, Snydersmith, & Gardner, in preparation), and in numbers of open ended likes and dislikes toward presidential candidates (Ankerbrand, Visser, 62 Krosnick, Cacioppo, & Gardner, in preparation). Thus, this finding of non-reciprocal
activation of positivity and negativity is not unique to the BEAMs, even in our own
research33.
The second possibility, that low correlations between positivity and negativity
simply are the result of using unipolar scales in general, is also refutable. Although
Green and his colleagues (Green, 1988; Green, Goldman, & Salovey, 1992) have
persuasively demonstrated that demand characteristics, particularly a "yea-saying" or
acquiescence bias, can artificially reduce the correlation between two factors, it is
unlikely that such measurement artifacts explain the current findings. The demand
characteristic present in our studies is much more likely to be a pull towards
evaluative consistency, and thus reciprocal ratings, than an acquiescence bias. After
all, the logical counterpart to a "very positive" rating is "not at all negative" and in
fact, in studies in which unipolar attitude scales were presented with and without filler
material between them, positivity and negativity were found to be more highly related
in the former than the latter condition (Thompson, Zanna, & Griffin, 1992).
Therefore, the demand characteristic that is most likely to be present in the current
research would spuriously inflate, rather than diminish, the negative correlations
between positivity and negativity.34
33FinalIy, any artifact that was a result of the wording of the response options would be expected to affect all three subscales equally, because the response options are identical across the three scales. Therefore, response option artifacts cannot explain the asymmetric relationship between positivity and ambivalence and negativity and ambivalence.
34 Indeed, in a LISREL analysis of unipolar attitude scales performed by Thompson et. al. (1992), the correlated error terms were negative rather than positive in sign. 63 Therefore, the correlational data showing evidence for the distinct nature of
positivity and negativity could not be explained by an artifact caused by the BEAMs.
However, it could be argued that the evidence for the differential activation functions
of positivity and negativity in impression change was a spurious result of assessing
positive and negative responses separately. Although the evidence presented for the
positivity offset and the negativity bias in all five studies was from data collected with
the BEAMs, we are confident that this alternative explanation can be refuted on
logical, historical, and empirical grounds.
First, participants are free to use the BEAMs in any fashion they wish. Given the aforementioned demand towards evaluative consistency, the use of them in a
spuriously reciprocal fashion is far more likely than responding in ways that would artifactually produce both a positivity offset and negativity bias. As evidence for the fact that participants are free to use the BEAMs in any way they wish, notice that the correlational data revealed an increase in reciprocal responding on the positivity and negativity subscales in the positive information and negative information conditions, but no such increase in reciprocity between positivity and negativity in the mixed conditions. Such a change in mode of activation, although opaque from a bipolar perspective (Edwards & Ostrom, 1972), is consistent with the bivariate model of evaluation.
Their finding lends support to the presence of a consistency bias rather than an acquiescence bias in attitude measurements using unipolar positive and negative scales.
64 Second, the existing literature concerning biases in impression formation has
consistently shown these biases using both unipolar and bipolar measures of
evaluation. The positivity bias has been predominantly investigated in studies using
bipolar measures (Kaplan, 1973; Sears, 1983) as has the negativity bias (Anderson,
1965; Fiske, 1980; Martijn et. al., 1992). Thus, reported biases in the archival
impression formation literature discount the notion that the current results are based
upon the use of unipolar attitude scales. Nonetheless, we decided to conduct our
own analyses to rule out this alternative interpretation of the current findings. To this
end, the data from the five studies were stacked, and a regression analysis was
performed on this combined dataset to investigate the impact of the amount of
positive and negative information participants received upon the bipolar ratings of the
target that were given at the end of each study.
The bipolar ratings consisted of an average of the five bipolar scales (reverse
scored when necessary and centered at zero) and could range from "-3", not at all
positive, to "3", extremely positive. The independent variables of amount of positive
and negative information were defined by the second block of information.
Participants who were in the all positive conditions were coded as receiving six pieces
of positive information and no negative information, participants in the all negative
conditions were coded as receiving six pieces of negative information and no positive information, participants in the mixed conditions were coded as receiving three pieces
of each type of information, and participants in the all neutral condition were coded as receiving neither positive nor negative information.
The predictions of the bivariate model for this regression equation are straightforward. First, the negative information should have a larger impact on bipolar 65 ratings than positive information because of the negativity bias. Second, the intercept
should be positive, because of the positivity offset. Results of this analysis revealed
the predicted pattern35. The negativity bias was supported by the larger impact of
negative information (B = -. 18; t(347) = -11.68, p < .001) than positive information
(B = .02; t(347) = 1.09, n.s.). In addition, the positivity offset was clearly evidenced
by the positive intercept (a = .893; t(347) = 10.29, p < .001), most likely reflecting
the residual positivity found within the neutral conditions. The appearance of both a
positivity offset and a negativity bias in the bipolar ratings bolsters the conclusions
drawn from Studies One through Five. The evidence in the current research
supporting the distinct activation functions of positivity and negativity in evaluations
does not appear to be limited to the use of unipolar attitude scales.
Of course, any test of positive-negative asymmetries in impressions or
evaluations assumes that the positive and negative inputs are equal in extremity.
This is particularly crucial for the negativity bias, for if the negative information was more extreme than the positive information then the evidence shown here for the greater impact of negative information could simply be an extremity effect rather than the negativity bias. Peeters & Czipanski (1990) discussed two ways in which the extremity of positive and negative inputs could be controlled. The first method is through the careful pretesting and selection of stimuli to ensure comparability in the subjective ratings given to each item of information; this is the method used most frequently in the impression formation literature examining negativity biases
35The F statistic for the regression equation was also significant; F (2,340) = 92.76, p < .001, R2.= .35, as were the correlations between amount of positive information and the bipolar rating (r = .31 .01) and amount of negative information and bipolar rating (r = -.59, p < .01).
6 6 (Skowronski & Carlston, 1987; Fiske, 1980). The second method is through the use
of "objective" indices of extremity, for example defining one unit of positivity as the
gain of a dollar and one unit of negativity as the loss of a dollar. This method is
prevalent in the judgment and decision making literature concerning risk aversion
(Kahneman & Tversky, 1984; Levy, 1992).
The current research obviously relied on the former method, that of selecting
stimuli on the basis of comparable scale values in pretesting, because in impression
formation "objectively" equivalent behaviors are difficult to identify at best.
Recently, however, we have conducted a study to ensure that the predictions of the
bivariate model are supported when objectively equivalent inputs are used.
In brief, this study involved participants in a "rock-paper-scissors" game on a
computer36 and the wins and losses of lottery tickets were used as the positive and
negative inputs. All participants began the game with four lottery tickets, subsequent
wins resulted in the gain of one lottery ticket per win, losses resulted in the forfeiture
of one lottery ticket per loss, and ties resulted in neither a gain nor a loss of a ticket.
The first two rounds of the game were ties, so participants neither gained nor lost any
tickets,37 after these initial games, participants rated their reactions to the outcome of the game thus far by answering the BEAMs on the computer as well as items taken
from the PANAS mood inventory (Watson, Clark & Tellegen, 1988). Next,
36Participants were run in groups of four, and believed that they were playing against another participant in the study.
37As rock-paper-scissors is a game of chance with equal opportunities for wins, losses and ties, the majority of participants were not suspicious of their pattern of wins, losses and ties. Data from those few participants who expressed suspicion that the game was somehow "rigged" were excluded from analyses. 67 participants were randomly assigned to one of four conditions: four wins, four losses, four ties, or two wins and two losses randomly ordered. Participants again evaluated their reactions to the outcome of the game using the BEAMs and indicated their mood using the PANAS items.
The game study was intended to replicate and extend38 the existing tests of the bivariate model. Preliminary analyses of the BEAMs in this study revealed that participants generally rated the game as more positive than negative after ties, and the loss condition had a greater impact on ratings than the win condition. Positivity in response to a neutral outcome (no gain/no loss) may be interpreted as evidence for the positivity offset, representing residual positivity at zero input. Likewise, the larger effect of losses than wins upon evaluations of the game's outcome is consistent with the predictions of the negativity bias. The preliminary findings of the game study, if treated as a conceptual replication of the impression formation research presented here, is thus encouraging in its support for the differential activation functions of positivity and negativity. Although we were careful to equate the subjective positive and negative inputs in the impression formation studies39,
38Response times to answer the items on the BEAMs and the mood inventory were recorded to investigate the effects of positive and negative inputs on time to respond to positive and negative questions. Neither the response time data nor the mood data have been fully analyzed at this time.
39To the largest extent possible, we selected behaviors that had already been normed for valence and diagnosticity by Skowronski & Carlston (1987), with the exceptions of the non-diagnostic neutral behaviors in Study Three and the behaviors attributed to the fish and insect in Studies Four and Five. All behaviors, self-generated and otherwise, were pretested on our own subject population to control for extremity differences.
6 8 converging evidence from the gaming study which used more objectively equal inputs
was nonetheless reassuring. The predictions of the bivariate model do not appear to
be limited to situations in which the informational inputs are equated through
subjective ratings. Instead, the positivity offset and negativity bias appear to be
general features of evaluative processing, observable in reactions to objectively equal
gains and losses.
All of the above data, then, argue against an interpretation of the results of the
current studies as a reflection of methodological artifacts brought on by scale
wording, the use of unipolar measures, or unequal extremity of the informational
inputs. Instead, the consistency of the present findings argue for the efficacy of the
bivariate model in accommodating the positivity and negativity biases in impression
formation.
It is important to note that evidence for the validity of the bivariate model of
evaluation does not imply invalidity of extant interpretations of positivity and negativity biases in impression formation. Robust support exists for the role of diagnosticity in person perception (Skowronski & Carlston, 1989). Similarly, the novelty of negative behavior in current society is irrefutable (Jones & Davis, 1965;
Fiske, 1980), and the ability of perceived similarity to produce attraction has a long empirical history (Byrne, 1971; Sears, 1983). We are not arguing that these processes do not contribute to positivity and negativity biases in impression formation, obviously they do. However, we would assert that these explanations are insufficient to account for the overarching pattern of positive-negative asymmetries in evaluations. It is thus not our intention to supplant the explanations of impression biases that are present in the literature, but to complement them. 69 A three-stage model o f evaluative processing
One difference between the existing explanations of positivity and negativity
biases in impression formation and the bivariate model of evaluation is that the former
require learned and somewhat complex cognitive processing, whereas the latter
proposes that the evaluative subsystem is fundamentally constructed to produce the
positivity offset and negativity bias. We believe that both positions are tenable. The
results of the more complex cognitive calculations (e.g. the larger impact of diagnostic information) may be overlaid upon or integrated with the asymmetries inherently produced by basic evaluative processing. Further, those basic evaluative asymmetries may motivate attention and cognition, producing a synergy between higher and lower order systems.
Our findings in a separate program of research, one that focuses upon the use of event-related brain potentials as indices of attitudes, have led us to propose a three- stage model of evaluative processing that may be helpful in reconciling the different perspectives of impression biases. This model posits an initial evaluative categorization stage that is sensitive to both valence and extremity (Cacioppo, Crites,
Gardner & Berntson, 1993, Gardner, 1994), impervious to conscious attempts at control (Crites, Cacioppo, Gardner, & Berntson, 1995), reflects a combination of rudimentary cognitive and affective mechanisms (Cacioppo, Crites & Gardner, in press), and is very possibly spontaneous in nature (Bargh, 1994; Gardner &
Cacioppo, in press). The initial evaluative categorization of a stimulus is then succeeded by a bivalent response predisposition which prepares the organism for the 70 evaluative response itself (see Figure 7). Each of the perspectives of the asymmetries
in impression formation, from the bivariate model's account, to attributional
explanations, and even including the original conjectures of demand characteristics,
can be accommodated within the three-stage model of evaluative processing.
The latter two stages, response predisposition and evaluative responding, are constrained to bipolarity because the purpose of these stages is the preparation and execution of an approach or avoidance response. In contrast, the initial stage of evaluative categorization is not constrained to bipolarity, and it is plausible that instead independent assessments of both the positivity and the negativity of a stimulus take place. The distinct motivational substrates and differential activation functions hypothesized by the bivariate model should most purely manifest at this early stage, for it is at this stage that the translation from input to initial positive and negative evaluations takes place, and this stage is also the least likely to be affected by controlled processing. These evaluative categorizations are then integrated into a predisposition to respond to the stimulus in a positive or negative fashion. At this stage, more complex cognitive processing, such as those that assess and accommodate the diagnosticity of a particular behavior, may moderate the initial evaluative categorization of the stimulus. Finally, the response predisposition is translated into an evaluative response. Processes that allow the modification of overt behavior to conform to the perceived demands of a particular situation or audience are presumed to occur at the juncture between response disposition and the evaluative response itself.
The latter two stages depend upon the output from the initial evaluative categorization stage. Therefore, because evaluative categorizations are presumed to 71 be affected by the differential activation functions of positivity and negativity,
evidence of a positivity offset and a negativity bias may be seen across all stages of
evaluative processing. However, the addition of unrelated processes (such as the
attributional biases previously demonstrated in the person perception literature) will
also affect the ultimate impression of a target. In this sense, the evidence for the
bivariate model in the present studies is particularly compelling, for the self-report
measures which demonstrated these findings represent the final stage of evaluative
processing40.
Conclusion
The findings of the current research add to a growing body of evidence that attests to the insufficiency of a bipolar perspective of evaluative processing. The results reported here consistently supported a general bivariate model of evaluation in which positivity and negativity act as distinct motivational substrates and are characterized by different activation functions. Impressions after the receipt of neutral information demonstrated a positivity offset, even in the face of non diagnostic neutral information or the evaluation of non-human targets. The data also supported the existence of a negativity bias that weights negative information more heavily in impressions. Existing explanations of the mechanisms underlying biases in impression formation seem insufficient to account for the pattern of findings in the
40Ongoing research in our laboratory that utilizes a late positive brain potential as an index of evaluative categorization is designed to examine the positivity offset and negativity bias in a more clear-cut fashion. Preliminary evidence from Crites et. al. (1995) supports the manifestation of a positivity offset and negativity bias in brain potential measures of evaluative categorizations 72 present research, but may play an influential role in moderating impressions at a later stage in evaluative processing.
Although the bivariate model accommodates positivity and negativity biases in impression formation, it is not limited to person perception, but rather is relevant for any area of inquiry in which evaluative processing plays a substantial role. Ongoing and future research will explore the implications of the bivariate model in such diverse domains as blood donation, AIDS prevention, and voting behavior. The results of the current research alone, however, are sufficient to question the continued adherence to the bipolar perspective of attitudes and evaluation so dominant in the psychological literature. Instead, the implications of these findings for impression formation, and for social psychology more generally, suggest that a bivariate model may more comprehensively describe evaluative processes. Thus, in contrast with the traditional assertion that positivity and negativity represent opposite values of a single algebraic variable, the current work implies, instead, that such an equating of positivity with negativity may indeed be a comparison as flawed and as fraught with differences as that of apples with cabbages.
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83 APPENDIX A:
ILLUSTRATIONS
84 \ 0 '
Figure 1: Representation of the bivariate model of evaluation. Notice the positive and negative motivational substrates can be independently activated, coactivated and coninhibited as well as reciprocally activated.
85 9
8
7
6
Q. 5
4
3
2
1
0 0 2 4 6 8 10 12 14 Increasing Input Positivity Negativity
Figure 2: The activation functions of positivity and negativity. Notice at zero activation there is a positivity offset, represented by the intercept value being larger than zero. The negativity bias is represented by the steeper slope of the negative activation function.
86 Figure 3: Changes in impressions of Sam after the second block of information for for information of block second the after Sam of inimpressions Changes 3: Figure participants in Study One. Study in participants
Change 25 1 -2.5 15 - -1.5 -0.5 . - 0.5 2.5 2.5 1.5 t NEU POS NEG MIX MIX NEG POS NEU Condition 87 31 posch □ negch ■ ambch Figure 4: Changes in impressions of Sam after second block of behaviors for for behaviors of block second after Sam of inimpressions Changes 4: Figure participants in Study Two who received Morality Relevant information. Relevant Morality received who Two Study in participants
Change 25 1 -2.5 -1.5 -0.5 . - 0.5 2.5 1.5 NEU POS NEG NEG POS NEU Condition 77 88 MIX 11 □ ■ ambch negch posch Figure 5: Changes in impressions of Sam after second block of behaviors for for behaviors of block second after Sam of inimpressions Changes 5: Figure participants in Study Two who received Ability Relevant information. Ability Relevant received who Two Study in participants
Change 25 - -2.5 15 - -1.5 -0.5 0.5 . r 2.5 1.5 NEU POS NEG MIX MIX NEG POS NEU Condition 89 linn H □ ambch negch posch 2.5 T
□ posch
H negch
ill ambch
-1.5 -
-2.5 1 NEU1 NEU2 POS NEG MIX Condition
Figure 6: Changes in impressions of Sam after the second block of information for participants in Study Three. NEU1 represents the evaluatively neutral set used in Studies 1 and 2, and NEU2 represents the evaluatively neutral and non-diagnostic set.
90 Figure 7: Changes in impressions of the Aguaphore after the second block of of block second the after Aguaphore the of impressions in Changes 7: Figure information for participants in Study Four. Study in participants for information
Change -2.5 15 - -1.5 -0.5 . - 0.5 2.5 2.5 1.5 t NEU POS NEG MIX MIX NEG POS NEU Condition 91 J posch CJ ambch Figure 8: Changes in impressions of the Entophore after the second block of of block second the after Entophore the of inimpressions Changes 8: Figure information for participants in Study Five. Study in participants for information
Change -2.5 -1.5 -0.5 0.5 2.5 2.5 . - 1.5 t NEU POS NEG MIX MIX NEG POS NEU Condition 92 11 negch ■ □ ambch posch Figure 9: The three-stage model of evaluation.
93 Study 1 Positivity Negativity Ambivalence Neutral 1.95 Positive Negative Mixed
Study 2 Positivity Negativity Ambivalence Neutral 1.95 Positive Negative Mixed
Study 3 Positivity Negativity Ambivalence Neutral 1.95 Positive Negative Mixed
Study 4 Positivity Negativity Ambivalence Neutral 1.95 Positive Negative Mixed
Study 5 Positivity Negativity Ambivalence Neutral 1.95 Positive Negative Mixed
Table 1: Means after first block of information by condition. No significant effects were found for condition after the first block of information in any of the studies.
94 All Conditions Positive 1 Negative 1 N = 349 Negative 1 -.06 Ambivalence 1 .15* .48**
Positive Positive 2 Negative 2 N = 86 Negative 2 -.52** Ambivalence 2 -.22 .29*
Negative Positive 2 Negative 2 N = 84 Negative 2 -.36** Ambivalence 2 .16 .30*
Mixed Positive 2 Negative 2 N = 85 Negative 2 -.11 Ambivalence 2 -.02 .51*
Neutral Positive 2 Negative 2 N = 92 Negative 2 -.27* Ambivalence 2 -.05 .58*
Table 2: Correlations among positivity, negativity, and ambivalence ratings across the combined dataset of Studies One through Five. The type of information participants received in the second block, and the sample sizes, are indicated to the left of each table. "Positive 1" denotes positive ratings after the first block of neutral information, "Positive 2" denotes positive ratings after the second block of information, etc.
95 APPENDIX B:
BEHAVIORS USED IN EACH STUDY
96 STUDY ONE:
NEUTRAL BEHAVIORS: BLOCK ONE
Weighed the vegetables before buying them
Got a haircut
Replaced a burnt out light bulb
Took a walk in the local park
Mailed the telephone bill
Ate a cheeseburger for lunch
NEUTRAL BEHAVIORS: BLOCK TWO
Regularly watched 'Cosmos' on television
Rode the bus to work
Shopped for groceries
Went to the dentist for an annual checkup
Bought a new shirt
Checked public phone slots for dimes
POSITIVE BEHAVIORS: BLOCK TWO
Had several essays published in the New Yorker
Planned a special birthday party for an elderly neighbor
Gave vegetables from his garden to all of his neighbors
Returned a lost wallet intact
Was on the chess team in college
Spoke four languages fluently
97 NEGATIVE BEHAVIORS: BLOCK TWO
Joked about the death of a coworker's pet within earshot of the coworker
Had trouble figuring out English grammar in school
Cut in front of some people who were waiting in line to buy movie tickets
Cheated at poker
Graduated at the bottom20% of his high school class
Fell because he piled boxes on a chair and stood on them to reach a high shelf
MIXED BEHAVIORS: BLOCK TWO41
Gave vegetables from his garden to all of his neighbors
Had trouble figuring out English grammar in school
Cut in front of some people who were waiting in line to buy movie tickets
Returned a lost wallet intact
Was on the chess team in college
Cheated at poker
41This is just one example of a mixed behavior sequence. Four mixed behavior sequences were constructed after counterbalancing morality and ability dominance with equal numbers of positive and negative behaviors. 98 STUDY TWO:
NEUTRAL BEHAVIORS: BLOCK ONE
Weighed the vegetables before buying them
Got a haircut
Replaced a burnt out light bulb
Took a walk in the local park
Mailed the telephone bill
Ate a cheeseburger for lunch
NEUTRAL BEHAVIORS: BLOCK TWO
Regularly watched 'Cosmos' on television
Rode the bus to work
Shopped for groceries
Went to the dentist for an annual checkup
Bought a new shirt
Checked public phone slots for dimes
POSITIVE BEHAVIORS: BLOCK TWO - MORALITY RELEVANT
Planned a special birthday party for an elderly neighbor
Gave vegetables from his garden to all of his neighbors
Returned a lost wallet intact
Volunteered as a Big Brother every weekend
Gave back extra change at the supermarket
Refunded money for a car that happened to break down 10 days after he sold it
99 POSITIVE BEHAVIORS. BLOCK TWO - ABILITY RELEVANT
Had several essays published in the New Yorker
Figured out how to fix a broken toaster
Was on the chess team in college
Spoke four languages fluently
Organized a successful committee
Developed a device to help control industrial pollution
NEGATIVE BEHAVIORS: BLOCK TWO - MORALITY RELEVANT
Joked about the death of a coworker's pet within earshot of the coworker
Cut in front of some people who were waiting in line to buy movie tickets
Cheated at poker
Wouldn't donate money to help buy flowers for a hospitalized coworker
Wrote a bad check
Resold his defective stereo
NEGATIVE BEHAVIORS: BLOCK TWO - ABILITY RELEVANT
Had trouble figuring out English grammar in school
Graduated at the bottom 20% of his high school class
Fell because he piled boxes on a chair and stood on them to reach a high shelf
Bought himself clothes that were the wrong size
Needed a calculator to add two numbers of any kind
Had trouble figuring out the tip in a restaurant
100 MIXED BEHAVIORS: BLOCK TWO - MORALITY RELEVANT
Cheated at poker
Gave vegetables from his garden to all of his neighbors
Cut in front of some people who were waiting in line to buy movie tickets
Returned a lost wallet intact
Wouldn't donate money to help buy flowers for a hospitalized coworker
Volunteered as a Big Brother every weekend
MIXED BEHAVIORS: BLOCK TWO - ABILITY RELEVANT
Had trouble figuring out English grammar in school
Was on the chess team in college
Graduated at the bottom 20% of his high school class
Organized a successful committee
Had trouble figuring out the tip in a restaurant
Figured out how to fix a broken toaster
101 STUDY THREE:
NON-DIAGNOSTIC NEUTRAL BEHAVIORS: BLOCK ONE
Lived on planet Earth
Consumed water on a regular basis
Usually maintained a body temperature of 98.6 degrees
Breathed oxygen
Communicated through both verbal and non-verbal means
Was usually awake during the daylight hours
REGULAR NEUTRAL BEHAVIORS: BLOCK ONE
Weighed the vegetables before buying them
Got a haircut
Replaced a burnt out light bulb
Took a walk in the local park
Mailed the telephone bill
Ate a cheeseburger for lunch
NON-DIAGNOSTIC NEUTRAL BEHAVIORS: BLOCK TWO
Was exposed to sunlight
Digested food
Walked upright
Typically wore clothing
Was the offspring of a male and a female
Dreamt at night
102 REGULAR NEUTRAL BEHAVIORS: BLOCK TWO
Regularly watched 'Cosmos' on television
Rode the bus to work
Shopped for groceries
Went to the dentist for an annual checkup
Bought a new shirt
Checked public phone slots for dimes
POSITIVE BEHAVIORS: BLOCK TWO
Had several essays published in the New Yorker
Planned a special birthday party for an elderly neighbor
Gave vegetables from his garden to all of his neighbors
Returned a lost wallet intact
Was on the chess team in college
Spoke four languages fluently
NEGATIVE BEHAVIORS: BLOCK TWO
Joked about the death of a coworker's pet within earshot of the co worker
Had trouble figuring out English grammar in school
Cut in front of some people who were waiting in line to buy movie tickets
Cheated at poker
Graduated at the bottom 20% of his high school class
Fell because he piled boxes on a chair and stood on them to reach a high shelf
103 MIXED BEHAVIORS: BLOCK TWO
Gave vegetables from his garden to all of his neighbors
Had trouble figuring out English grammar in school
Cut in front of some people who were waiting in line to buy movie tickets
Returned a lost wallet intact
Was on the chess team in college
Cheated at poker
104 STUDY FOUR:
NEUTRAL BEHAVIORS: BLOCK ONE
The aguaphore lives on earth
The aguaphore inhabits salt-water
The aguaphore has several gills
The aguaphore swims in a forward fashion
The aguaphore has a tail
The aguaphore maintains a body temperature similar to that of the surrounding water
NEUTRAL BEHAVIORS: BLOCK TWO
The aguaphore swims in shallow and deep waters
The aguaphore is usually awake during the daylight hours
The aguaphore rests at regular intervals
The aguaphore lives near reefs
The aguaphore digests food
The aguaphore is an offspring of both a male and a female of the species
POSITIVE BEHAVIORS: BLOCK TWO
The aguaphore will often protect its mate and offspring
The aguaphore is one of the most attractive species of fish
The aguaphore is helpful to the coral reef in which it lives
The natives have a name for the aguaphore which means "graceful sea creature"
The aguaphore is useful commercially
The presence of the aguaphore can improve the populations of other fish and sea mammals 105 NEGATIVE BEHAVIORS: BLOCK TWO
The aguaphore will often attack its mate and offspring
The aguaphore is one of the least attractive species of fish
The aguaphore is harmful to the coral reef in which it lives
The natives have a name for the aguaphore which means "vicious sea creature"
The aguaphore is useless commercially
The presence of the aguaphore can damage the populations of other fish and sea
mammals
MIXED BEHAVIORS: BLOCK TWO
The aguaphore is one of the least attractive species of fish
The aguaphore will often protect its mate and offspring
The presence of the aguaphore can damage the populations of other fish and sea
mammals
The aguaphore is useless commercially
The aguaphore is helpful to the coral reef in which it lives
The natives have a name for the aguaphore which means "graceful sea creature"
106 STUDY FIVE:
NEUTRAL BEHAVIORS: BLOCK ONE
The entophore lives on earth
The entophore inhabits the rain forest
The entophore has several legs
The entophore walks in a forward fashion
The entophore has a mouth
The entophore maintains a body temperature similar to that of the surrounding environment
NEUTRAL BEHAVIORS: BLOCK TWO
The entophore inhabits both warmer and cooler areas
The entophore is usually awake during the daylight hours
The entophore rests at regular intervals
The entophore lives near trees
The entophore digests food
The entophore is an offspring of both a male and a female of the species
POSITIVE BEHAVIORS: BLOCK TWO
The entophore will often protect its mate and offspring
The entophore is one of the most attractive species of insect
The native people have a name for the entophore which means "graceful creature"
The entophore is useful commercially
The entophore is helpful to the rain forest plants in which it lives
107 The presence of the entophore can improve the populations of other rain forest animals
NEGATIVE BEHAVIORS: BLOCK TWO
The entophore will often attack its mate and offspring
The entophore is one of the least attractive species of insect
The natives have a name for the entophore which means "horrible creature"
The entophore is useless commercially
The entophore is harmful to the rain forest plants in which it lives
The presence of the entophore can damage the populations of other rain forest
animals
MIXED BEHAVIORS: BLOCK TWO
The entophore is one of the least attractive species of insect
The entophore will often protect its mate and offspring
The presence of the entophore can damage the populations of other rain forest
animals
The entophore is useless commercially
The entophore is helpful to the rain forest in which it lives
The natives have a name for the entophore which means "graceful creature"
108 APPENDIX C:
INSTRUCTIONS AND THE BEAMs SCALES
The Bivariate Evaluation and Ambivalence Scales (Cacioppo et. al., in preparation) are presented on the next few pages, along with the final bipolar rating sheet. All of these measures were used in Studies One through Five. As presented here, the scales have Sam as the target. In Study Four, the target was The Aguaphore, and in Study Five the target was the Entophore. During the experiments, filler scales were presented between each of the subscales to minimize carryover.
109 EXAMPLE FORM A:
The purpose of this questionnaire is to assess the make up of your POSITIVE reactions to Sam. You should think carefully about ONLY your POSITIVE reactions to this person. Then rate the extent to which each of the adjectives that are listed on the next page describes your positive reactions. When using the adjectives to rate your positive reactions, you should think carefully about ONLY your positive reactions to Sam. That is, try to separate your positive feelings about Sam from any negative feelings you might have. Even then, not every positive adjective will be a good descriptor of your positive feelings, therefore, circle the scale description that most closely matches the relationship between your positive reactions and the adjective. For example, imagine that instead of rating Sam, you were rating a social issue like nuclear power, and "wonderful" is an adjective on which you will rate your reactions to nuclear power. You would make your ratings on a scale like the one below:
WONDERFUL
very slightly/not at all a little moderately quite a bit extremely
If "wonderful" does not describe your positive reactions to nuclear power, (or if you have no positive reactions), you should circle "very slightly/not at all". Alternatively, if "wonderful" describes your positive reactions very well but not perfectly, you might circle "quite a bit." Remember, although the purpose of this questionnaire is to assess the positivity of your reactions, the nature of positive reactions is variable. Thus, some adjectives may describe your reactions very well, whereas others will not describe them at all. You should, therefore, try to think about each adjective separately, because the extent to which each adjective is a good descriptor of the way you feel may vary. Now, please think about Sam, and turn the page.
110 My attitude toward Sam can be described by.
DESIRABLE very slightly/not at all a little moderately quite a bit extremely
POSITIVE very slightly/not at all a little moderately quite a bit extremely
LIKABLE very slightly/not at all a little moderately quite a bit extremely
HAPPY very slightly/not at all a little moderately quite a bit extremely
SUPPORTING very slightly/not at all a little moderately quite a bit extremely
GOOD very slightly/not at all a little moderately quite a bit extremely
ATTRACTIVE very slightly/not at all a little moderately quite a bit extremely
SATISFYING very slightly/not at all a little moderately quite a bit extremely 111 The purpose of this questionnaire is to assess the make up of your NEGATIVE reactions to Sam. You should think carefully about ONLY your NEGATIVE reactions to this person. Then rate the extent to which each of the adjectives that are listed on the next page describes your negative reactions. When using the adjectives to rate your negative reactions, you should think carefully about ONLY your negative reactions to Sam. That is, try to separate your negative feelings about Sam from any positive feelings you might have. Even then, not every negative adjective will be a good descriptor of your negative feelings, therefore, circle the scale description that most closely matches the relationship between your negative reactions and the adjective. For example, imagine that instead of rating Sam, you were rating a social issue like nuclear power, and "inappropriate" is an adjective on which you will rate your reactions to nuclear power. You would make your ratings on a scale like the one below:
INAPPROPRIATE
very slightly/not at all a little moderately quite a bit extremely
If "inappropriate" does not describe your negative reactions to nuclear power, (or if you have no negative reactions), you should circle "very slightly/not at all". Alternatively, if "inappropriate" describes your negative reactions very well but not perfectly, you might circle "quite a bit." Remember, although the purpose of this questionnaire is to assess the negativity of your reactions, the nature of negative reactions is variable. Thus, some adjectives may describe your reactions very well, whereas others will not describe them at all. You should, therefore, try to think about each adjective separately, because the extent to which each adjective is a good descriptor of the way you feel may vary. Now, please think about Sam, and turn the page.
112 My attitude toward Sam can be described by.
UNFAVORABLE
very slightly/not at all a little moderately quite a bit extremely
UNAPPEALING
very slightly/not at all a little moderately quite a bit extremely
UNPLEASANT
very slightly/not at all a little moderately quite a bit extremely
DISAGREEABLE
very slightly/not at all a little moderately quite a bit extremely
DISAPPROVING
very slightly/not at all a little moderately quite a bit extremely
PUNISHING
very slightly/not at all a little moderately quite a bit extremely
DISTRESSED
very slightly/not at all a little moderately quite a bit extremely
UNCOMFORTABLE very slightly/not at all a little moderately quite a bit extremely 113 The purpose of this questionnaire is to assess the CONFIGURATION of your reactions to Sam. You should think carefully about the configuration of your positive and negative reactions to this person. Then rate the extent to which each of the adjectives that are listed on the next page describes this configuration. When using the adjectives to rate the configuration of your reactions, you should think carefully about the relationship between your positive and negative reactions. Then, try to decide how closely each of the adjectives describes this configuration. Not every adjective will be a good descriptor of the configuration between your positive and negative feelings, therefore, circle the scale description that most closely matches the relationship between the configuration of your reactions and the adjective. For example, imagine that instead of rating Sam, you were rating a social issue like nuclear power, and "unstable" is an adjective on which you will rate the configuration of your reactions to nuclear power. You would make your ratings on a scale like the one below:
UNSTABLE
very slightly/not at all a little moderately quite a bit extremely
If "unstable" does not describe the configuration of your positive and negative reactions to nuclear power, you should circle "very slightly/not at all". Alternatively, if "unstable describes the configuration of your reactions very well but not perfectly, you might circle "quite a bit." Remember, although the purpose of this questionnaire is to assess the configuration of your reactions, the nature of this configuration is variable. Thus, some adjectives may describe the configuration of your reactions very well, whereas others will not describe the configuration at all. You should, therefore, try to think about each adjective separately, because the extent to which each adjective is a good descriptor of the way you feel may vary. Now, please think about Sam, and turn the page.
114 My attitude toward Sam can be described by.
MUDDLED very slightly/not at all a little moderately quite a bit extremely
CONTRADICTORY very slightly/not at all a little moderately quite a bit extremely
UNIFORM very slightly/not at all a little moderately quite a bit extremely
DIVIDED very slightly/not at all a little moderately quite a bit extremely
CONSISTENT very slightly/not at all a little moderately quite a bit extremely
CONFLICTING very slightly/not at all a little moderately quite a bit extremely
TENSE very slightly/not at all a little moderately quite a bit extremely
JUMBLED very slightly/not at all a little moderately quite a bit extremely 115 EXAMPLE FORM B:
The purpose of this questionnaire is to assess the make up of your NEGATIVE reactions to Sam. You should think carefully about ONLY your NEGATIVE reactions to this person. Then rate the extent to which each of the adjectives that are listed on the next page describes your negative reactions. When using the adjectives to rate your negative reactions, you should think carefully about ONLY your negative reactions to Sam. That is, try to separate your negative feelings about Sam from any positive feelings you might have. Even then, not every negative adjective will be a good descriptor of your negative feelings, therefore, circle the scale description that most closely matches the relationship between your negative reactions and the adjective. For example, imagine that instead of rating Sam, you were rating a social issue like nuclear power, and "inappropriate" is an adjective on which you will rate your reactions to nuclear power. You would make your ratings on a scale like the one below:
INAPPROPRIATE
very slightly/not at all a little moderately quite a bit extremely
If "inappropriate" does not describe your negative reactions to nuclear power, (or if you have no negative reactions), you should circle "very slightly/not at all". Alternatively, if "inappropriate" describes your negative reactions very well but not perfectly, you might circle "quite a bit." Remember, although the purpose of this questionnaire is to assess the negativity of your reactions, the nature of negative reactions is variable. Thus, some adjectives may describe your reactions very well, whereas others will not describe them at all. You should, therefore, try to think about each adjective separately, because the extent to which each adjective is a good descriptor of the way you feel may vary. Now, please think about Sam, and turn the page.
116 My attitude toward Sam can be described by.
BAD
very slightly/not at all a little moderately quite a bit extremely
UNLIKABLE
very slightly/not at all a little moderately quite a bit extremely
UNSATISFYING
very slightly/not at all a little moderately quite a bit extremely
UNATTRACTIVE
very slightly/not at all a little moderately quite a bit extremely
OPPOSING very slightly/not at all a little moderately quite a bit extremely
NEGATIVE very slightly/not at all a little moderately quite a bit extremely
UNHAPPY very slightly/not at all a little moderately quite a bit extremely
UNDESIRABLE very slightly/not at all a little moderately quite a bit extremely 117 The purpose of this questionnaire is to assess the CONFIGURATION of your reactions to Sam. You should think carefully about the configuration of your positive and negative reactions to this person. Then rate the extent to which each of the adjectives that are listed on the next page describes this configuration. When using the adjectives to rate the configuration of your reactions, you should think carefully about the relationship between your positive and negative reactions. Then, try to decide how closely each of the adjectives describes this configuration. Not every adjective will be a good descriptor of the configuration between your positive and negative feelings, therefore, circle the scale description that most closely matches the relationship between the configuration of your reactions and the adjective. For example, imagine that instead of rating Sam, you were rating a social issue like nuclear power, and "unstable" is an adjective on which you will rate the configuration of your reactions to nuclear power. You would make your ratings on a scale like the one below:
UNSTABLE
very slightly/not at all a little moderately quite a bit extremely
If "unstable" does not describe the configuration of your positive and negative reactions to nuclear power, you should circle "very slightly/not at all". Alternatively, if "unstable describes the configuration of your reactions very well but not perfectly, you might circle "quite a bit." Remember, although the purpose of this questionnaire is to assess the configuration of your reactions, the nature of this configuration is variable. Thus, some adjectives may describe the configuration of your reactions very well, whereas others will not describe the configuration at all. You should, therefore, try to think about each adjective separately, because the extent to which each adjective is a good descriptor of the way you feel may vary. Now, please think about Sam, and turn the page.
118 My attitude toward Sam can be described by.
MUDDLED
very slightly/not at all a little moderately quite a bit extremely
CONTRADICTORY
very slightly/not at all a little moderately quite a bit extremely
UNIFORM
very slightly/not at all a little moderately quite a bit extremely
DIVIDED
very slightly/not at all a little moderately quite a bit extremely
CONSISTENT
very slightly/not at all a little moderately quite a bit extremely
CONFLICTING
very slightly/not at all a little moderately quite a bit extremely
TENSE very slightly/not at all a little moderately quite a bit extremely
JUMBLED very slightly/not at all a little moderately quite a bit extremely 119 The purpose of this questionnaire is to assess the make up of your POSITIVE reactions to Sam. You should think carefully about ONLY your POSITIVE reactions to this person. Then rate the extent to which each of the adjectives that are listed on the next page describes your positive reactions. When using the adjectives to rate your positive reactions, you should think carefully about ONLY your positive reactions to Sam. That is, try to separate your positive feelings about Sam from any negative feelings you might have. Even then, not every positive adjective will be a good descriptor of your positive feelings, therefore, circle the scale description that most closely matches the relationship between your positive reactions and the adjective. For example, imagine that instead of rating Sam, you were rating a social issue like nuclear power, and "wonderful" is an adjective on which you will rate your reactions to nuclear power. You would make your ratings on a scale like the one below:
WONDERFUL very slightly/not at all a little moderately quite a bit extremely
If "wonderful" does not describe your positive reactions to nuclear power, (or if you have no positive reactions), you should circle "very slightly/not at all". Alternatively, if "wonderful" describes your positive reactions very well but not perfectly, you might circle "quite a bit." Remember, although the purpose of this questionnaire is to assess the positivity of your reactions, the nature of positive reactions is variable. Thus, some adjectives may describe your reactions very well, whereas others will not describe them at all. You should, therefore, try to think about each adjective separately, because the extent to which each adjective is a good descriptor of the way you feel may vary. Now, please think about Sam, and turn the page.
120 My attitude toward Sam can be described by.
FAVORABLE
very slightly/not at all a little moderately quite a bit extremely
AGREEABLE
very slightly/not at all a little moderately quite a bit extremely
DELIGHTED
very slightly/not at all a little moderately quite a bit extremely
APPEALING
very slightly/not at all a little moderately quite a bit extremely
APPROVING
very slightly/not at all a little moderately quite a bit extremely
COMFORTABLE very slightly/not at all a little moderately quite a bit extremely
PLEASANT very slightly/not at all a little moderately quite a bit extremely
REWARDING very slightly/not at all a little moderately quite a bit extremely 121 Please rate your overall impression of Sam on the following scales.
My impression is that Sam is...
BAD GOOD 1 2 3 4 5 6 7
WISE FOOLISH 1 2 3 7
PLEASANT UNPLEASANT 1 2 3 7
UNKIND KIND 1 2 3 7
LIKABLE UNLIKABLE 1 2 3 7
Thank you for participating in this study!!
122 APPENDIX D:
COVER STORY FOR STUDIES THREE THROUGH FIVE
123 Impression Formation Script:
Hi and welcome to SC-4. My name is Wendi and I’ll be your experimenter today.
I want to tell you a little bit about the experiment, and why your participation is important, before we begin. That way, you can leave as soon as you are finished rather than waiting around for everyone to get done.
This is a project in which we’re investigating whether we can simulate “social intelligence” using an artificial intelligence computer program.
How many of you have heard the phrase "Artificial Intelligence"? (wait for show of hands)
Anyone want to explain it to the others? (pause)
In brief, artificial intelligence programs are called “intelligent” because they “learn” how to do a task, rather then being programmed with strict answers for the task. As a simplified example, your calculator gives you the correct answer to an addition problem because it has the answer programmed right into it, all it has to do is retrieve it from its database. An Artificial Intelligence computer could also give you the correct answer to an addition problem, but not because it has the answer stored in memory somewhere but rather because it knows how to add. . .it “learned” how to add through having someone enter addition problems and then the correct answer, eventually, after hundreds and hundreds of these feedback loops, the AI computer 124 “learned” what it had to do with the input in order to get to the correct output.
Now, AI programs have been ver. 'ccessful at learning mathematical tasks, spatial strategy tasks (like chess), and signal processing tasks (for example the military uses
AI systems to discriminate between radar signals that indicate a tank vs. a schoolbus).
But, as of yet, no on has examined whether an AI system could learn more social aspects of intelligence.
What do I mean by “social intelligence?” Well, one of the things people do that's amazingly “intelligent” from a data processing perspective is that we navigate through a complex social world — meeting new people, being exposed to new events, ideas, and products, and we can form impressions and opinions about these people, events, and products very quickly and easily, even given limited information. As an example, lets pretend your roommate comes home from a party and tells you about a person
Joe that they met. They’ll probably only tell you a few things about Joe — a bit about his appearance, a few things he said or did, and yet even though you have never met
Joe, you can easily form an impression of whether you would like or dislike him, and whether you would want to interact with him — all based on the few bits of information your roommate gave you. That's pretty smart! And its that type of social intelligence that we’re interested in simulating.
So, to that end, our computer has a number of databases: it has databases about 2 people: a male and a female, 2 animals: an insect and a fish; and 2 products: a car and a laundry detergent. Within each of these databases are literally hundreds of descriptors of the person, product, or animal. The computer makes observations 125 about the person, product, or animal based on these descriptors, and prints out these
observations one to a page. We then combine these observation into packets and give
them to you, real people, and ask you to give *your* impression of the person,
product, or animal based upon the information you’ve been given. Then, your
impressions are fed back into the computer as the “correct” answer for that particular
combination of observations. We will do this 100s of times, and literally 1000s of
college students will be helping us out by giving their impressions to help the
computer “learn”
The exciting thing about this project is that we as psychologists don’t really know
how it is people form impressions, we are only slowly learning the psychological
"rules of the game" so to speak. But, if we can get the computer to “behave” like a
person, then what the computer does while its forming impressions may give us
insight into what humans do.
Are there any questions so far? If not, I’ll tell you what we’re going to be doing.
Each of you will receive 4 booklets.
2 booklets will be marked Ql, and Q2 — these are the impression questionnaires.
The other 2 booklets will be marked SI and S2 if you’re forming an impression of a
person, AI & A2 if you’re forming an impression of a fish, El & E2 if you’re forming
an impression of an insect, and PI & P2 if you’re forming an impression of a product.
These booklets hold the computer observations, printed one to a page, of the person, animal, or product. 126 You'll first be reading through the first computer observation booklet, marked SI,
AI, El or P I. I will be timing you through the booklet, telling you when to open the booklet and when to turn each page. You'll have 7 seconds to read each item of information. Once you’ve finished reading it, you cannot go back to it. After the first booklet of information, you will answer Ql, which will ask for your first impressions of the person, animal, or product. Then we'll read the second booklet of computer observations, marked S2, A2, E2, or P2, once again I'll tell you when to start and when to turn each page. Finally, you will answer the Q2 booklet. In this second booklet, give us your impression of the person, product, or animal based upon *all* of the information you’ve been given. When you’re finished, bring all four booklets to me and I’ll sign your experiment cards.
Does anyone need a pen or pencil?
Ok, remember, there are NO right or wrong answers in this study. . .just be as honest as possible in reporting your reactions to the informaiton you'll be given. Finally, be assured that your answers are completely anonymous, no one will be able to connect your answers with you personally in any way.
Ready to start?
127