Behavioral Indicators of Discrimination in Social Interactions
Summary and Keywords
A growing number of studies are utilizing different sorts of behavioral indicators as measures of prejudice and discrimination. Although there are few foolproof behavioral indicators of discrimination (cf. verbal articulations of overt discrimination), patterns of behaviors can often be reliable indicators of discrimination. There are three sets of behavioral indicators. First, there are verbal behaviors such as overt insults or the use of pejorative words to describe stigmatized individuals. Such verbal statements, particularly when overt, make attributions of perceiver prejudice very straightforward. Such exchanges appear to be on the rise and are particularly worthy of study following the apparent 2016 “whitelash” and resistance to acting in ways deemed to be “politically correct.” Other forms of verbal behaviors involve more indirect expressions of prejudice, such as ambiguous comments and subjective references. Second, there are paraverbal behaviors that may index discrimination. For instance, individuals’ tone and pacing of speech may intentionally or unintentionally signal their disapproval or dislike of a stigmatized target. These behaviors are less commonly studied by social scientists but provide indicators about an individual’s intentions toward a stigmatized target. Third and finally, there are both nonverbal microbehaviors (e.g., gestures, eye contact) and macrobehaviors (e.g., avoidance, helping behavior). Behavioral measures—both classic and more state-of-the-art—that might serve as indicators of discrimination have been identified in recent research, and researchers should continue to learn more about them and use them.
How does the Black man discern from a social interaction that he is not liked? What cues does the overweight individual use to assess whether others are being genuine? Is the physically disabled young woman able to accurately detect her interaction partners’ true opinion? The answers to these questions are complex. Other than an honest admission, there is perhaps no single foolproof behavioral detector of dislike, hostility, or discrimination. Yet, across many studies, stigmatized targets are able to accurately infer incivility and discrimination (e.g., Kunstman, Tuscherer, Trawalter, & Lloyd, 2016; see also Richeson & Shelton, 2005). What are they relying upon to make these relatively accurate assessments? People are quick social perceivers who can infer, even more so when threatened or with practice, the negativity and bias that people hold toward them. It has been argued that there is a set or pattern of verbal, paraverbal, and nonverbal behaviors that are often concomitant with overt expressions of discrimination.
How might a social scientist objectively capture discrimination within a social interaction? This topic is woefully understudied in the literature. This chapter identifies and discusses some of the most promising behavioral indicators and offers advice about how to assess disinterest, dislike, and hostility toward stigmatized targets. There is no infallible test. For instance, a smile may signal positivity or it may mask underlying negativity. Yet, emerging research suggests that targets can spot the difference (see Kunstman et al., 2016).
This chapter begins by, first, discussing verbal behaviors, such as overt insults or the use of pejorative words to describe stigmatized individuals. Such verbal statements, particularly when overt, make attributions of perceiver prejudice very straightforward. Such exchanges appear to be on the rise and are particularly worthy of study following the apparent 2016 “whitelash” and resistance to acting in ways deemed to be “politically correct” (Blake, 2016). Other forms of verbal behaviors involve more indirect expressions of prejudice, such as ambiguous comments and subjective references.
Second, this chapter shifts to paraverbal behaviors that may index discrimination. For instance, individuals’ tone and pacing of speech may intentionally or unintentionally signal their disapproval or dislike of a stigmatized target. These behaviors are less commonly studied by social scientists but provide indicators about an individual’s intentions toward a stigmatized target. Third and finally, this chapter discusses both nonverbal microbehaviors (e.g., gestures, eye contact) and macrobehaviors (e.g., avoidance, helping behavior).
By providing very focused attention on behavioral measures that might serve as indicators of discrimination, this chapter attempts to identify them for readers, inspire researchers to learn more about and use them, and offer a concise review of their use in current research. Hence, this chapter is meant to inform others about the displays and patterns of potentially discriminatory responses and also the most strategic—whether classic or more state-of-the-art—ways of measuring these behaviors.
Verbal Indicators of Discrimination
One avenue by which people exhibit discrimination is spontaneous (i.e., unstructured) verbal utterances, which may occur in conversations, interviews, or in more institutionalized contexts such as political speeches and in the media (Augoustinos & Every, 2007). Although verbal discrimination can be subject to the same social desirability concerns that plague many self-report measures of prejudice and discrimination, the free-response nature of verbal expression and its more naturalistic occurrence make assessing more subtle and implicit forms of verbal discrimination, along with its more blatant and overt forms, possible (e.g., Beukeboom, Finkenauer, & Wigboldus, 2010; Maass, Salvi, Arcuri, & Semin, 1989; Miller, Taylor, & Buck, 1991; Wigboldus, Semin, & Spears, 2000).
Overt Verbal Discrimination
Although explicit verbal discrimination is rare given social desirability concerns (Collins & Clément, 2012), individuals are still willing to make statements that derogate or oppress stigmatized groups (although not without many qualifications, as described in sections below). For example, Augoustinos and Every (2007) presented quotations from a number of research papers in which participants were interviewed about race and racial policies. Speakers referred to minorities as “lowlife,” and claimed that minority groups behave abominably and are like animals (p. 137). These sorts of verbal responses often are elicited by interviewing participants about issues such as immigration policies or affirmative action.
Modern/Subtle Verbal Discrimination
Just as researchers using self-report measures of prejudice have created questionnaires to assess the less blatant modern (McConahay, 1986), aversive (Dovidio & Gaertner, 2004), and symbolic (Sears & Henry, 2003) forms of prejudice, researchers who examine spontaneous verbal discrimination have noted that norms against prejudice lead many speakers to qualify their potentially discriminatory remarks so that they appear, on the surface, not to support discrimination (e.g., Augoustinos & Every, 2007; Collins & Clément, 2012; Goodman, 2014).
In Augoustinos and Every’s (2007) discourse analysis of prejudice, they found that participants rarely stated prejudice overtly on its own; instead, statements that derogated minorities were often qualified and highly rationalized in an attempt by the speaker to claim that he/she was not prejudiced. Augoustinos and Every described five patterns of discourse that people tend to use in order to qualify verbal discrimination. First, speakers may engage in denial of prejudice, in which they precede derogatory statements with disclaimers such as “I have nothing against minorities, but . . .” or “I’m not prejudiced, but . . . .” Given that these disclaimers often tend to be immediately followed by discriminatory remarks, it seems possible to place these remarks in the “overt verbal discrimination” category. However, the presence of the disclaimer is consistent with the modern racism point of view that blatant discriminatory remarks are no longer socially acceptable in general society.
Second, speakers may appeal to reason and rationality in order to distance their statements from discrimination. This suggests that many speakers share Allport’s definition of prejudice as “thinking ill of others without sufficient warrant” (Allport, 1966, p. 448), a definition that implies that prejudice is irrational, and that thinking ill of others with sufficient warrant does not qualify as prejudice. When using this tactic, speakers point to external “facts” to justify discriminatory statements, often drawing on first-hand experiences of negative interactions with stigmatized group members. For example, someone may claim to be against immigration because immigrants are criminals, and then back up this statement with statistics or personal experience with criminals who happened to also be immigrants. The criminals who were not immigrants or the immigrants who were not criminal seem to fall by the wayside for such speakers.
Third, speakers may attempt to affirm the in-group and contrast this with derogation of the minority group in order to preserve group esteem before a discriminatory statement (Augoustinos & Every, 2007). For instance, in a political speech a politician may affirm the country’s values of generosity and helping others, but then stress the need to mitigate the influx of refugees for safety reasons. This serves to make discrimination seem reasonable, as it protects the virtuous in-group from the dangerous out-group, and blames the stigmatized target for the discrimination—if the stigmatized targets were not such terrorists/criminals/freeloaders, the in-group would not have to resort to such means.
Fourth, speakers can engage in discursive deracialization, where racial explanations for statements and policies are deemphasized. For example, an individual may state that he/she is against immigration because of unemployment problems or safety concerns, reasons that ostensibly have nothing to do with racial group membership. In these instances, negative opinions and racial marginalization continue to be expressed, but the message is communicated by downplaying race as an explanatory construct (Augoustinos & Every, 2007).
Fifth, speakers may use “liberal arguments for illiberal ends” (Augoustinos & Every, 2007, p. 134). In this case, speakers use liberal values related to justice, fairness, and equality to justify discriminatory statements and policies. For instance, someone might argue against affirmative action because it violates fairness and equality by favoring certain races over others in hiring decisions. Ironically though, these liberal principles of equality, justice, and fairness are leveraged as rationalization of inequality and expressions of opinions and practices that may actually be discriminatory. These qualifications that tend to accompany verbal discrimination can present challenges to researchers when interpreting prejudice-relevant speech, but the qualifying statements themselves might be coded as instances of modern prejudice.
Discrimination, thus, can be reflected in the straightforward content of what people say. However, the same content can be communicated in different ways. That is, subtle discrimination also can be implicitly expressed through the way in which the speaker communicates. For example, modern prejudice can be revealed in the level of abstraction with which people speak about in-group and out-group behaviors. The Linguistic Expectation Bias (Wigboldus et al., 2000) is the tendency to describe stereotype-consistent behavior in more abstract language (“That man is stoic”) than stereotype-inconsistent behavior (“Yesterday morning, that man cried”). The Linguistic Intergroup Bias (Maass et al., 1989) is the tendency to speak more abstractly about positive in-group and negative out-group behaviors. A speaker might describe an out-group member’s positive action in more concrete terms (“He visited her at the hospital”), but describe an in-group member’s positive action in a more abstract sense (“He is compassionate”). The assessment of these linguistic biases is a more implicit measurement of subtle discrimination on the part of the speaker, who would not be intentionally discriminating.
Abstraction level is not the only way speakers implicitly communicate atypicality. Choice of comparison group can communicate normative status. For example, Miller et al. (1991) asked participants to explain gender differences in voting and found that people more often mentioned characteristics of nonprototypical (female) voters to explain differences. Similarly, when describing gender differences in elementary school teachers, participants focused more on explaining the behavior of men, who were seen as atypical in that context. The use of negations (“Robert was not smart” vs. “Robert was stupid”) in speech is another way to communicate atypicality. Beukeboom et al. (2010) found that participants spontaneously used more negations in their descriptions of stereotypically-inconsistent behavior (e.g., a man involved in jazz ballet, a woman involved in rugby).
Benevolent Verbal Discrimination
Subtle discrimination can also be expressed through “benevolent prejudice” (e.g., Glick & Fiske, 1996). According to Glick and Fiske, sexism (and other prejudices) do not always take the form of hostility, but may include patronizing attitudes about protecting and idealizing stigmatized group members. For example, women can be stereotyped as compassionate nurturers (Glick & Fiske, 1996); Aboriginal individuals can be idealized as “in touch with nature” (Werhun & Penner, 2010). Chen and Yang (2015) coded discussion board comments from medical students in universities in Taiwan and found that female students were often the target of benevolent sexism. For example, students discussed how teachers were often protective toward female students, who were seen as weak and fragile, but the teachers often demanded more from male students. Although more difficult to code, expressions of benevolent prejudice in which a speaker idealizes minority groups, or endorses restricting their opportunity in order to protect their idealized status, can be considered a subtle form of discrimination.
Measurement of Verbal Discrimination
Measuring verbal discrimination can be more complicated and qualitative than the analysis of self-report questionnaire data. Discursive psychology or discourse analysis provides one theoretical rubric. Discursive psychology is the study of language as a psychological behavior and tends to study verbal responses in naturalistic settings (Lester, 2014). Rather than seeing language as a reflection of an internal construct, such as prejudice, discursive psychology studies how constructs such as prejudice and discrimination are produced through language (Goodman, 2014). In this field, qualitative methods such as conversation analysis are utilized, but can be combined with more quantitative analyses for a mixed methods approach (Stivers, 2015).
One quantitative way that verbal accounts can be analyzed for discrimination is through differences in abstraction. This can be achieved through the Linguistic Categories Model (LCM; Semin & Fiedler, 1989), which can be useful to analyze speech for both Linguistic Expectation Bias (Wigboldus et al., 2000) and Linguistic Intergroup Bias (Maass et al., 1989). Additionally, researchers can code for linguistic nuances that communicate atypicality, such as the centering of explanations around a group considered atypical (e.g., female voters, male elementary school teachers; Miller et al., 1991), or the use of negations when describing stereotype-inconsistent behavior (e.g., male ballet dancers; Beukeboom et al., 2010).
Researchers can also utilize computerized text analysis tools such as Linguistic Inquiry and Word Count to extract quantitative information about themes relevant to prejudice (LIWC; Tausczik & Pennebaker, 2010). LIWC could enable researchers to judge if people express more positive or negative emotions when talking about stigmatized targets. Additionally, pronoun use could be analyzed when individuals are talking about stigmatized targets to see if there are differences between the use of first- and second-person pronouns when talking about nonstigmatized versus stigmatized targets, with higher first-person pronoun frequency correlating with closer relationships (Tausczik & Pennebaker, 2010). More directly, conversations might be analyzed to see if speakers utilize words associated with particular stereotypes when speaking about stigmatized targets. The frequency of these stereotype-relevant words could be correlated with self-report or behavioral measures of prejudice.
To summarize, conversations, interviews, and other naturalistic verbal reports can be rich sources of data for research on discrimination. Although verbal discrimination is likely subject to some of the same social desirability concerns as self-report prejudice questionnaires, speakers in naturalistic settings have the flexibility to communicate a more nuanced message and to distance themselves from discrimination. Thus, participants may be more willing to refer to stigmatized targets as “lowlife,” “like animals,” and in other stereotype-consistent terms in spontaneous speech, because they have the ability to verbalize why they think these beliefs are not discriminatory. Nuanced, complicated beliefs such as benevolent prejudice may also be easier to detect in spontaneous conversations compared to self-report questionnaires. Benevolent prejudice might be uncovered in spontaneous speech by asking participants relevant questions about whether stigmatized target groups are special or inspiring (Grue, 2016). Individuals may be particularly frank about their benevolent prejudices in response to such questions, as benevolent prejudice may arouse fewer self-presentation concerns compared to more blatant discrimination. Verbal statements in speech can also be analyzed as implicit measures of discrimination by coding for aspects such as word abstraction and negation. This provides another method of circumventing social desirability concerns related to discrimination.
Future directions might include assessing whether overt verbal prejudice changes in frequency over time. Because norms can be different between cultures and over time, it is possible that blatant, unqualified verbal prejudice might be expressed more frequently with certain audiences or during specific time periods. Additionally, the expression of verbal discrimination might serve different functions for different people. For peripheral group members, blatant or subtle verbal discrimination might serve as expressions of in-group loyalty and conformity to group norms (Noel, Wann, & Branscombe, 1995). For other group members, verbal discrimination could be conscious or unconscious expressions of power (e.g., Bergh, Akrami, Sidanius, & Sibley, 2016). Further research is also needed to explore implicit indicators of subtle verbal prejudice beyond word abstraction, negation, and the use of first- versus second-person pronouns. Are there other stylistic differences in speech that might indicate that a speaker is referring to something atypical, or counter to stereotypes? Computerized text analysis tools such as LIWC can help researchers investigate differences between speech about or directed at stigmatized group members, compared to members of nonstigmatized groups. Finally, research is needed to explore how verbal indicators of discrimination interact with other behavioral indicators, some of which are detailed in the following sections.
Paraverbal Indicators of Discrimination
When encountering unfamiliar, threatening, or complex situations, individuals tend to experience a greater degree of arousal (Kahneman, 1973). This arousal is accompanied by physiological responses that are difficult to control, thereby signaling an individual’s internal state, including their attitude, mood, and emotions (Zuckerman, DePaulo, & Rosenthal, 1981). It is logical, therefore, that these autonomic physiological responses, such as voice pitch, speech disturbances, and shorter interaction durations, may increase in frequency when individuals find themselves in unfamiliar, threatening, and complex contexts in which they might face discrimination (Zuckerman & Driver, 1985). These involuntary expressions (e.g., pitch, pauses, speech errors) are known as paraverbal behaviors. While some of the paraverbal behaviors discussed in the following section have not been explored directly in the context of interpersonal discrimination, it follows from other research (e.g., related to deception) that there may be links between many of these paraverbal expressions and discrimination. Thus, this is an understudied area that presents an exciting opportunity for future research.
No singular paraverbal expression or even set of paraverbal expressions definitively indicates that discrimination is occurring in an interaction. Rather, combinations of specific paraverbal expressions may be more likely to occur when an individual is discriminating compared to when they are not, due to the stress, negativity, and anxiety that characterize discriminatory interactions. By attending to these behavioral indicators, one may ascertain, with caution, that discrimination may be the internal state driving an individual’s paraverbal behaviors in an interaction.
Pitch is operationalized as the mean fundamental frequency throughout a complete speech sample, providing a measure of how high or low a voice is (DeGroot & Motowidlo, 1999). The typical mean pitch for men is about 128 Hz, while for women, it is about 225 Hz (Boone, 1977).
While the relation between pitch and interpersonal discrimination has not yet been directly tested, there is reason to believe that voice pitch may serve as a paraverbal expression of discrimination based on research in other areas. The literature on pitch in interviews and deception has shown that pitch is difficult to control and that stress is likely to increase fundamental frequency, resulting in individuals using a higher pitch when lying (Zuckerman et al., 1981; see also Ekman, 1992; DePaulo et al., 2003). Because interpersonal interactions involving discrimination likely induce stress in one or both parties, it follows that these interactions would be characterized by increased fundamental frequencies, or higher pitch. Again, pitch has not been studied as a paraverbal expression in interpersonal interactions involving discrimination specifically, so this is an understudied area of research that holds significant promise for future research in diversity and discrimination. Pitch can be measured using computer-based methods of voice and speech analysis. One such tool that utilizes video recordings to analyze, synthesize, and manipulate speech is the PRAAT program, which is one of the more well-known speech analysis programs (with more than 5,000 users from 100 countries; Boersma & van Heuven, 2001; Burris, Vorperian, Fourakis, Kent, & Bolt, 2014).
Speech disturbances are characteristics of “flustered” speech and are largely beyond linguistic and social control (Kasl & Mahl, 1965). Kasl and Mahl (1965) developed eight empirically derived categories of speech disturbances and found that when individuals are anxious, the frequency of all but one of the speech disturbances (“Ah,” which behaves slightly differently than the other speech disturbances; see Mahl, 1955) increases. Other than “Ah,” these eight categories of speech disturbances include sentence change, sentence incompletion, repetition, stutter, tongue-slip, omission, and intruding incoherent sound.
“Ah” is exactly what it sounds like—the insertion of the word “ah” or one of its less common variants, “eh,” “er,” and “um.” An example is “Well . . . ah . . . when I first got to work . . .” Sentence change is a correction an individual makes in the structure or content of the message as it progresses. In order to be considered a sentence change, these corrections must be perceived by the listener as disruptions in the flow of the expression. An example of sentence change is “Well, he’s . . . at times he can be stubborn.” A sentence incompletion is an instance in which the speaker interrupts their own sentence and leaves it unfinished, proceeding with the expression without correction. An example of a sentence incompletion is “My apologies that I missed our meeting about the . . . ah . . . I was stuck in traffic.” Repetition is the sequential unnecessary reiteration of one or more words—typically of one or two words. An example of repetition is “She was . . . She was telling me about her vacation.” A stutter is the unintentional repetition of sounds, particularly initial consonants. An example of a stutter is “I couldn’t quite l . . . l . . . let it go.” Tongue-slips include neologisms, the rearrangement of words from their correct sequential position in an expression, and the replacement of an unintended word for an intended word. An example of a tongue-slip is “He decided to move the party out(doors) . . . indoors from outdoors since it rained.” Omission is the exclusion or dropping of parts of, and sometimes entire, words. The most common omission is that of the final syllables of words. An example of omission is “It’s not imposs . . . nothing is impossible.” An intruding incoherent sound is one that is completely unintelligible as a word to the listener. It simply intrudes without actually changing the form of the expression. It also cannot be considered a stutter, omission, or tongue-slip (though there may be some crossover in reality). An example of an intruding incoherent sound is “I was born . . . dh . . . in southern Tennessee” (Kasl & Mahl, 1965).
Speech disturbances are some of the more salient paraverbal indicators of discrimination. The basis for this is that an individual who is interacting with a stigmatized target may experience anxiety throughout the duration of the interaction for a variety of reasons—they may feel threatened by the stigmatized target, they may feel uncomfortable engaging with the stigmatized target, or they may possess negative feelings toward the stigmatized target; or their anxiety could be a result of all of these and other reactions. In the literature, speech disturbances are typically measured through simple frequency counts (Boomer, 1963; Kasl & Mahl, 1965). Usually, interpersonal interactions are audio-recorded and then transcribed, and these recordings and transcripts are analyzed, with each speech disturbance noted. Oftentimes, researchers calculate ratios, such as the speech disturbance ratio (SDR), which is the number of speech disturbances divided by the number of words spoken (Boomer & Goodrich, 1961; Mahl, 1956).
Response latency refers to the time between the end of one communicator’s message and the beginning of the other communicator’s reply (e.g. DePaulo et al., 2003; Greene, O’Hair, Cody, & Yen, 1985; Sporer & Schwandt, 2006). There is a paucity of research on response latency and interpersonal discrimination; however, other streams of research can inform the development of a broad understanding of and expectation for how response latency may be related to prejudice. Some deception research has found that response latency is positively related to deception (Sporer & Schwandt, 2006), with communicators pausing for more time before replying when their message was deceptive. These researchers put forth a working memory model explanation for this finding. Due to greater demands on one’s processing capacity, deceptive communication necessitates more cognitive effort (Miller & Stiff, 1993; Vrij, 2000; Zuckerman et al., 1981). Other research has failed to find support for a positive relationship between response latency and deception, which may be due to the fact that response latencies may be shorter for those deceivers who have time to plan their responses, as they are not using the time immediately following a question to formulate their story (DePaulo et al., 2003). An alternative explanation for shorter, rather than longer, response latencies related to deception is that the anxiety and physiological arousal involved with deception may increase the intensity, thereby decreasing the latency, of the deceiver’s reply (Zuckerman et al., 1981).
Of the two potential changes in response latency—increased or decreased—it is theoretically logical that response latency would decrease in interactions involving discrimination. In an interaction between a stigmatized target and a nonstigmatized individual, there is a clear reason for physiological arousal and anxiety. Because an individual who is interacting with a stigmatized target may be experiencing feelings of threat, discomfort, or general negativity, the individual may become anxious and, in turn, physiologically aroused, leading to shorter response latencies resulting from the intensity of the response and a desire to terminate the interaction more quickly. Response latency can be measured by simply counting the number of seconds or minutes that pass between the time one communicator completes their message and the time the other communicator initiates their reply (Baskett & Freedle, 1974).
Message Duration/Number of Words/Speech Rate
Message duration is the amount of time one spends talking, number of words is the frequency count of words spoken, and speech rate is simply the combination of these two measures. That is, speech rate is the number of words spoken divided by the amount of time one spends talking (Sporer & Schwandt, 2006). Because these paraverbal aspects of communication are so intricately related, they will be considered one concept for the purpose of this discussion.
Again, due to a lack of research on speech rate and interpersonal discrimination, other streams of research must be relied upon for clues as to how speech rate and prejudice may be associated. First, research on evaluations of others based on speech rate has demonstrated that communicators with faster speech rates are viewed as more competent (e.g., intelligent, confident, ambitious) and less benevolent (e.g., kind, polite, sincere; Brown, 1980). For example, DeGroot and Motowidlo (1999) found that vocal cues, including speech rate, that lead interviewers to develop favorable impressions of job applicants (e.g., trust, liking, and credibility attributed to interviewee) are also related to effective on-the-job performance for individuals in management careers. Additional research in this area has indicated that faster speech rates result in higher evaluations of competence (Smith, Brown, Strong, & Rencher, 1975), more persuasion and higher evaluations of knowledge, intelligence, and objectivity (Miller, Maruyama, Beaber, & Valone, 1976), and more preferential evaluations, better recall, and more attention (MacLachlan, 1979).
The deception literature can also provide cues as to how speech rate may operate in discriminatory interactions, as speech rates have been studied extensively in this sphere. Sporer and Schwandt (2006) propose a number of hypotheses based on various theoretical perspectives that are somewhat contradictory, but that nevertheless may provide insight into the role of speech rate in discriminatory interactions. For example, taking an approach based on the physiological arousal that accompanies lying, one who is deceiving would be expected to speak more quickly, whereas affective theory would predict that the guilt and fear of being caught that an individual may feel when they are deceiving would lead to slower speech (so as to avoid contradictions, for example). According to a third theoretical approach, attempted control, individuals who are lying will attempt to avoid behaviors they associate with lying, so to the extent that an individual believes that slow speech will be interpreted by others as lying, they will avoid this behavior and speak quickly (and vice versa). This decision is based on a number of factors, including the extent to which planning, preparation, and rehearsal precede the deception. Finally, the cognitive load/working memory theory predicts that, due to the greater processing demands that the crafting of a deceptive message requires, individuals who are lying will speak more slowly than those who are telling the truth (Vrij, 2000). The lack of consensus regarding speech rate has been echoed empirically by other researchers. For example, Zuckerman and Driver (1985) found that when deceivers had only limited opportunity to plan what they would say, they spoke more slowly than individuals telling the truth; however, when deceivers had more opportunity to plan, they spoke more quickly than individuals telling the truth.
From the literature discussed previously, the finding that is most pertinent to prejudice and discrimination is that individuals who speak slowly are perceived to be more benevolent than those who speak more quickly, and, relatedly, the theory that is most compelling in explaining discriminatory interactions is that regarding physiological arousal leading to faster speech rates. Based on these ideas, it is expected that an individual interacting with a stigmatized target would exhibit a faster speech rate than usual, a conclusion that is bolstered by the rather intuitive experimental finding that individuals who are interacting with stigmatized targets tend to terminate the interaction as quickly as possible, presumably to avoid the discomfort and negativity they associate with the target (e.g., King, Shapiro, Hebl, Singletary, & Turner, 2006). As is the case with many other paraverbal expressions, measurement of speech rate (as well as message duration and number of words) is best facilitated through audio recordings of interactions and transcriptions of those recordings. Most studies measure speech rate by audio-recording an interaction and then calculating the number of syllables per second or minute (DePaulo et al., 2003).
As noted previously, many paraverbal behaviors have not been empirically explored in the context of interpersonal discrimination. Research on interpersonal discrimination would benefit substantially from experimental research on the occurrence of paraverbal behaviors in interactions between stigmatized and nonstigmatized individuals. Given the foundation that research on subtle discrimination (e.g., Barron, Hebl, & King, 2011; Morgan, Walker, Hebl, & King, 2013) and deception (e.g., DePaulo et al., 2003; Sporer & Schwandt, 2006) has laid in this arena in terms of conceptualization, operationalization, and measurement of paraverbal and nonverbal behaviors, there is a clear path forward in this area that is simply ripe for research.
Nonverbal Indicators of Discrimination
Again, when considering verbal, paraverbal, and nonverbal behaviors, it is important that the reader and the scientist who is engaging in measurement understand that a single behavior is rarely, if ever, an effective indicator of discrimination; rather, a pattern of nonverbal behaviors (ideally coupled with the expression of consistent verbal and paraverbal behaviors) is more likely to be a more convincing indication of discrimination. However, so as to best inform readers and researchers, a discussion of many of these nonverbal behaviors individually is warranted.
There are advantages to examining nonverbal behaviors rather than (or in addition to) verbal behaviors. To begin, nonverbal behaviors may be a more accurate reflection of an individual’s propensity to discriminate. Past researchers have suggested that verbal behaviors are much easier for people to control; hence, the extant discrimination may remain hidden if alternatives to verbal behaviors are not measured. Research by Dovidio, Kawakami, and Gaertner (2002) has shown that in interactions between Black and White interaction partners, White partners may focus more on their verbal behaviors in judging the interaction, whereas Black partners, who might know that such partners will say the right things, might spend more time focusing on the nonverbal behaviors. Indeed, it is possible that discrimination is more likely to emerge from nonverbal (compared to verbal) behaviors because such behaviors are harder to volitionally control and/or because perceivers are so focused on suppressing discrimination in their verbal behaviors that the discrimination leaks out of nonverbal behaviors.
Additionally, the examination of nonverbal behaviors allows for a multifaceted assessment that can include, in the case of facial expressions, (a) facial expressions that are consciously recognized as emotions, (b) micro-level measurements of facial expressions (e.g., smiling, frowning), and (c) macro-level measurements (e.g., patterns of avoidance and helping behaviors). What follows is a brief review of all of these potential nonverbal measures that might serve as discrimination indices.
The most widely regarded means of nonverbal communication may be the use of facial expressions. A number of muscles innervate the facial region (particularly the eyes, lips, nose, and forehead) and work together to communicate scores of different expressions, many of which are universally recognized. The fact that people make these expressions often and quickly, and sometimes without full awareness or control, provides researchers with a solid set of tools to assess states associated with discrimination such as emotions (e.g., happiness, surprise, sadness, anger, fear, disgust) and valences (e.g., positive, negative, liking, disliking, friendliness, hostility). Overt facial expressions are some of the most common ways in which perceived discriminatory behavior is examined (Crosby, Bromley, & Saxe, 1980), and a wide number of discrimination researchers have relied on this type of measurement (e.g., Dasgupta & Rivera, 2006; Dovidio, Kawakami, & Gaertner, 2002).
When using facial expressions as indices of discrimination, methodologists typically use a reliable set of raters to simply rate overall emotions or other gestalt states (e.g., “interest,” “friendliness,” “rudeness”) by observing an interaction or, more commonly, coding videotapes of these interactions. This macro-approach to assessing facial expressions has been an effective way for researchers to establish behavioral indices of discrimination using Likert-type rating scales that reflect some degree of the presence or absence of a particular set of expressions.
There are some emotional expressions that have not been linked or studied enough in the context of discrimination. One such emotion, for example, is that of disgust. Research by Rozin, Markwith, and McCauley (1994) shows that people feel disgust when they are asked to try on sweaters that people with AIDS have previously worn. Yet, very little research has looked at the specific behavioral composite of disgust that would signal discrimination (cf. Olatunji & Sawchuk, 2005). Thus, more research is needed to break this down.
In very recent research, Kunstman, Tuscherer, Trawalter, and Lloyd (2016) describe smiles as “overt signals of positive affect” (p. 1196). The lack of smiling, then, may indicate underlying negativity. Previous studies on interactions between nonstigmatized individuals and stigmatized targets have relied upon (a) stigmatized targets’ ratings of the extent to which others smiled at them (e.g., Hebl, King, Glick, Singletary, & Kazama, 2007), (b) third-party observers’ ratings of the extent to which nonstigmatized individuals smiled at the stigmatized targets (e.g., King, et al., 2006), and/or (c) independent coders’ ratings of the extent to which nonstigmatized individuals smiled in videotaped interactions (e.g., Dovidio et al., 2002; Gaither & Sommers, 2013). All of these codings, often in addition to other nonverbal indicators, have been helpful in establishing positivity, or the lack thereof, directed toward stigmatized targets.
Although smiling is largely interpreted as indicating positivity, it is important to note that sometimes people exhibit fake smiles, known as a non-Duchenne smiles. These smiles involve raising the corners of one’s mouth as if to smile (contracting the zygomatic major facial muscle) and raising one’s cheeks, but without the crow’s feet around the eyes (contracting the orbicularis oculi facial muscle, which, when paired with the contracted zygomatic major muscle, indicates a genuine or Duchenne smile). Kunstman et al. (2016) explain that the fake smile may be a particularly good marker of discrimination and that many minorities are able to accurately distinguish between White people who smile with a real versus a fake smile. As such, minorities may be suspicious and feel threatened by smiles; hence, coders might measure genuine as well as non-Duchenne smiles to assess levels of discrimination.
If smiles are described as signs of positive affect, then frowns might best be described as signs of negative affect. Whereas such global ratings of affect can be coded, actual measurement of nonvisible movements in electromyographic (EMG) activity from the brow (the corrugator facial muscle) region also can indicate preparation of a frown. Other researchers have distinguished between different types and amounts of frowns, differentiating them into constant frowns, fleeting frowns, and grimaces, and by recording the amount of time spent with eyebrows constantly raised, fleetingly raised, constantly knitted, fleetingly knitted, or relaxed (see Hess, 2013). It is clear, however, that frowns are a good indicator of dislike, uncertainty, or some sort of other negative behavior felt by the emoter.
In addition to smiling and frowning behaviors associated with nonverbal expressions of emotions, one of the most common measures associated with discrimination has focused on patterns of eye behavior. A discussion of two specific categories of eye behavior—the startle response and eye contact—follow.
Researchers have suggested that attitudes toward certain groups can be determined by an individual eye movement. One set of such movements, known as the startle response, is an automatic, reflexive, and very sensitive response to a brief, intense stimulus. The stimulus that evokes the startle response is typically one that evokes very negative or fearful (versus positive or appealing) imagery. Some have argued that the startle response protects individuals from potential harm by propelling them to action (see Blumenthal & Franklin, 2009). Recent research suggests that the startle eye response can indicate racial and homophobic bias (Amodio, Harmon-Jones, & Devine, 2003; Mahaffey, Bryan, & Hutchison, 2005). The startle response is generally easy to measure, but because it is very sensitive to a broad range of stimuli, it is also sensitive to stimuli other than just objects or persons of dislike.
Another pattern of behavior that is commonly examined by researchers as a potential indicator of discrimination is eye contact, or the amount of time an interactant makes visual contact with a target (Dovidio, Brown, Heltman, Ellyson, & Keating, 1988). A number of studies have used eye contact to study discrimination toward various groups. One group of researchers has measured and found that perceivers make less eye contact with gay and lesbian individuals, obese individuals, and pregnant individuals (Hebl, Foster, Mannix, & Dovidio, 2002; Hebl et al., 2007; King et al., 2006). Although these researchers used general perceptions of eye contact, other researchers also have assessed more minute and/or objective forms of eye contact including time spent directly looking at interactant, time spent with eyes averted from interactant, number of eye blinks, and time spent with eyes closed (e.g., Dovidio et al., 1988; Hess, 2013).
Touching or Haptics
Touching one another as a source of communication is known as haptics (Andersen, Gannon, & Kalchik, 2013). Touch, like many other nonverbal expressions, is a powerful and complex concept, given the many messages it may convey. Touching another person or oneself can communicate positive affect, negative affect, dominance, interpersonal reactions, interaction management, and task requirements, among other messages (Knapp, Hall, & Horgan, 2013). Beyond the many potential messages touch may convey, another aspect of touching that is multifaceted is the context. That is, the gender, age, culture, personality, relationship, and environment of the communicators all determine, to some extent, the meaning that people attach to that instance of touch. There are also specific characteristics of the touch, including the method, strength, duration, and frequency of the touch, as well as which body part is touched (Knapp et al., 2013). Research on mixed interactions could benefit from examining how communication is influenced through the firmness or length of a handshake or embrace, the initiation of and reciprocation of touching behavior, and the extent to which individuals violate norms of haptics (see Bonaccio, O’Reilly, O’Sullivan, & Chiocchio, 2016).
The way in which people posture themselves can be another indicator of discrimination. In one of the most classic and informative studies on nonverbal behaviors, Word, Zanna, and Cooper (1974) conducted a study examining the treatment that White interviewers directed toward White and Black targets. Results revealed that the interviewers oriented their body less directly toward Black than White applicants. Indeed, such a direct orientation toward another person demonstrates attention and respect, whereas orientation away can signify the opposite. These differences in nonverbal posturing also have been referred to as “open” and “closed” body positions, and earlier studies (Henderson-King & Nisbett, 1996; Kawakami, Phills, Steele, & Dovidio, 2007) indicate how easily such ratings can be made. Kawakami et al. (2007) had confederates themselves rate participants’ body orientation by scoring −4 to 4 with a 0 indicating that a participant is sitting directly in front of them and with a −4 or 4 indicating 40% to the left or right. Clearly, these ratings could be done by independent raters as well so as not to have actual interactants bias the process themselves by also doing the ratings. Studies examining posture not only could look at body orientation, but might also code the relaxed/open nature of arms and hands, the extent to which one clasps one’s hands, the forward versus straight-up posturing of one’s body in interactions, and the nodding and leaning in behaviors that interactants display (see Hess, 2013).
Interpersonal Space and Distance
Another nonverbal measure that can indicate discrimination is the distance that an individual stands apart from, places a chair away from, or sits next to a stigmatized target. Such measures comprise an entire study of nonverbal behaviors called proxemics, which focuses on personal space that people use to communicate. For the current chapter, there are two aspects associated with interpersonal distance that are particularly worthy of mentioning. First, researchers can examine the extent to which interpersonal space, or personal sphere, of targets is invaded. Most people feel discomfort, anger, or anxiety when their close intimate personal space (e.g., ~2 feet in the U.S.; Hall, 1963) is violated, unless it is by a very close friend or family member. Having space violated is often one of the behaviors cited in sexual harassment and bullying complaints (Yousaf, 2014), and recent evidence suggests that such strong emotional reactions to personal space invasions are automatically controlled by the amygdala (Kennedy, Gläscher, Tyszka, & Adolphs, 2009).
Second, one of the most common nonverbal behaviors used as a discrimination proxy is not the extreme closeness but rather, the avoidance of or significant distance apart that an interactant moves or remains from a stigmatized target. In the classic study by Word et al. (1974), White interviewers sat farther away from Black than White job applicants. This common action toward stigmatized targets may be the behavioral concomitant to disgust, fear, or dislike (see Izard, 1993). A number of other studies also have used physical seating distance (see Crosby et al., 1980). For instance, Madera and Hebl (2013) examined how far apart White job interviewers would sit apart from an ostensible Black job applicant. Similarly, Kawakami et al. (2007) had trained confederates to estimate the distance between the participant’s chair and his/her own using a scale from 1 (closest distance) to 9 (farthest distance).
Additional Avoidance Behavior
Interpersonal distance or space is a micro-level behavior often captured in more macro-level indicators of discrimination such as general avoidance behavior. Many studies assess avoidance behaviors by examining the extent to which perceivers not only place themselves physically apart from an individual but also the extent to which they choose to socially interact, work, and establish contacts and meaningful relationships with stigmatized targets (see Talaska, Fiske, & Chaiken, 2008). In a classic study by Snyder, Kleck, Strenta, and Mentzer (1979), participants had a choice between watching the same movie alone or with a disabled individual. Most participants chose to watch it with the disabled individual. However, in a follow-up study, when participants were given a choice to watch a movie with a disabled individual or a different movie alone, they chose to avoid the disabled individual and watch the alternative movie by themselves. However, the movies were counterbalanced across conditions so it was the disabled individual they were avoiding, and not any particular film. This study not only importantly shows that participants often avoid stigmatized individuals but that they are more likely to do so when they can mask their reasons for avoidance. That is, behavioral indices of discrimination may be particularly likely to emerge when attributions for discrimination are ambiguous.
Other research on avoidance behaviors has looked at social network analyses and friendship patterns (e.g., Stark, 2015). For instance, Hebl, Williams, Kell, Sundermann, and Davies (2012) found that people were more likely to “friend” and connect with Black people who were less (rather than more) stereotypical in appearance. Avoidance behaviors are such a hallmark of reactions to stigmatized individuals that recent additional research examines not only avoidance of particular groups of stigmatized people but also avoidance of people who are somehow connected with those people. Dubbed terms such as “stigma-by-association,” “mere-proximity effect,” or “courtesy stigma,” behavioral reactions to stigmatized individuals can be examined by measuring avoidance of those who interact with people who are gay, overweight, or have psychological disorders (e.g., Hebl & Mannix, 2003; Swim, Ferguson, & Hyers, 1999; van der Sanden, Bos, Stutterheim, Pryor, & Kok, 2013).
Although proxemics (interpersonal space and distance) were discussed earlier, it is important to acknowledge that chronemics, or nonverbal measures of time (i.e., walking speed, promptness, punctuality; see Bonaccio et al., 2016) might also provide researchers with potentially new or underutilized dependent measures of gauging avoidance and other nonverbal indices of discrimination. Bonaccio et al. (2016) also point out that the construction of environments such as floor plans, decorations, or environmental spacing constitute additional forms of nonverbal behavior with interpersonal ramifications. Surely, these nonverbal decisions have the potential to create and/or further foster patterns of avoidance toward stigmatized individuals.
Another set of nonverbal behaviors that has been used to examine discriminatory behaviors is that of helping behaviors. Some of these studies rely on face-to-face interactions in which stigmatized individuals make direct requests for help. In other studies, the targets themselves are not necessarily present, but their need for help is implied. These early paradigms focused predominantly on comparing helping behaviors directed toward Black versus White individuals. However, the dependent behaviors differed across studies and included a range of measurable nonverbal behaviors such as (a) responses to requests for money (Dutton & Lake, 1973), (b) picking up materials accidentally dropped (e.g., Wegner & Crano, 1975), (c) providing emergency assistance (e.g., Graf & Ridell, 1972), (d) making a helpful phone call (Gaertner & Bickman, 1971), and (e) being willing to mail an envelope (Benson, Karabenick, & Lerner, 1976). Other research has distinguished whether individuals provide differential amounts of help to people based on stigmatized group membership or specific value violation (Batson, Denten, & Vollmecke, 2008; Batson, Eidelman, Higley, & Russell, 2001; Batson, Floyd, Meyer, & Winner, 1999; Goldfried & Miner, 2002; Mak & Tsang, 2008). More recent studies have examined whether people are likely to volunteer to help, provide assistance when specifically requested, and financially support organizations related to the target’s stigma (e.g., Preston & Ritter, 2013; see Talaska et al., 2008). Across these studies, results reveal that stigmatized individuals are helped less often than their nonstigmatized counterparts.
To summarize, nonverbal indicators of discrimination contain a very rich set of expressions that provide behavioral indices of discrimination. Some of these behaviors have been well utilized whereas others have not. In a recent review and agenda for future research published in Journal of Management, Bonaccio et al. (2016) provide one of the most comprehensive reviews to date of nonverbal behaviors that might be used by organizational scholars. Although Bonaccio et al. (2016) provide a few examples of topics in which a greater focus on nonverbal behaviors might be beneficial, one that is not included in their review but that is important is nonverbal behavioral concomitants of discrimination. Although people’s expression of any particular nonverbal behavior cannot be interpreted with absolute certainty, patterns of nonverbal behaviors that emerge may point to emotional displays that are readily and universally interpretable. Clearly, more research that examines the nonverbal behaviors that are consistently displayed by people who are prejudiced and/or who are discriminating against others is needed.
As was clear in the preceding section, nonverbal behaviors may sometimes be expressed sincerely or duplicitously. As discrimination research utilizing nonverbal behaviors continues, it will be important to collect more insights about (a) whether there are strategic ways to accurately assess nonverbal behavioral responses, (b) moderators that lead to enhanced accuracy in interpretation, (c) which nonverbal behaviors are the most promising, and (d) more accurate and precise coding systems.
This chapter described verbal, paraverbal, and nonverbal behaviors that, when examined together, can provide a compelling set of behavioral indicators of discrimination. The study of nonverbal behaviors is an old practice, dating back to Charles Darwin’s (1872) book entitled The Expression of the Emotions in Man and Animals. Clearly, the study of emotion has been a mainstay topic in psychology. Yet, the emotional expressions and accompanying verbal, paraverbal, and nonverbal behaviors associated with discrimination are less well understood and provide a very promising line of future research inquiry.
There are several things to conclude about using verbal, paraverbal, and nonverbal behaviors as indicators of discrimination in social interactions. First, there are specific behaviors that might reflect patterns of discrimination; of course, there is no infallible method of determining discriminatory intent. However, researchers can look for certain behaviors that are consistent and concomitant with other behaviors, that, together, may indicate patterns of discrimination. Second, the current list of behaviors that may indicate discrimination is not exhaustive. Instead, it captures a host of readily and easily identifiable and measurable behaviors. Third, while there exists research associated with the different types of behavioral measures, more future research is needed for each behavioral assessment. That said, this chapter brought together insights, measurement descriptions, details, and studies that have utilized a variety of verbal, paraverbal, and nonverbal behaviors. Future researchers should expand on this.
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