Heuristics and Modalities in Determining Truth ... - Semantic Scholar

Proceedings of the 38th Hawaii International Conference on System Sciences - 2005

Heuristics and Modalities in Determining Truth Versus Deception Judee K. Burgoon University of Arizona [email protected]

J. P. Blair The University of Texas at San Antonio [email protected]

Renee E. Strom Michigan State University [email protected]

Portions of this research were supported by funding from the U.S. Air Force Office of Scientific Research under the U.S. Department of Defense University Research Initiative (Grant #F49620-01-1-0394, Judee Burgoon, Principal Investigator). The views, opinions, and/or findings in this report are those of the authors and should not be construed as an official Department of Defense position, policy, or decision.

Abstract In potentially deceptive situations, people rely on heuristic cues to help process information. These heuristic cues can often lead to biases concerning how the receiver views the information provided by the sender. One such bias is a truth bias, which has been documented to occur in many potentially deceptive situations (Levine, Parks & McCornack, 1999). It was hypothesized in this study that receivers would make more truthful than deceptive judgments. This study also sought to explore the impact the modality might have on truth bias. It was hypothesized that the truth bias would be strongest in the visual condition, intermediate in the audio condition, and lowest in the text condition. Finally, whether using computer-mediated forms of communication could improve deception accuracy was addressed. It was hypothesized that deception detection would be most accurate in the audio condition. Results supported the first two hypotheses but not the third.

1. Introduction Deception is a common occurrence in all quarters of human conduct, whether in personal or public affairs, in person or in some mediated communication format. Its very frequency should cause people to become attuned to such behavior and detect it accurately. Yet this is not the case. Accuracy rates in distinguishing truth from deception are notoriously dismal, even among trained professionals [14, 16, 20, 31, 32]. One reason is that recipients of truthful and deceptive communications rely on heuristics—mental shortcuts— that bias their judgments of incoming information [1, 15, 19]. One such bias—the truth bias—has received empirical support in several contexts [2, 27, 32]. Another likely culprit in inaccurate detection is the visual bias [8]. Thus, one objective of the current investigation was to test

for the effects of truth and visual biases in the context of ongoing interaction. Relatedly, if visual biases are at work, then communication modalities that afford access to visual information should be more subject to inaccurate judgments than should modalities that lack such information. Thus, a second objective of the current investigation was to consider whether communication modality influences detection accuracy.

2. Heuristic Processing According to Chaiken [9], heuristic processing is a non-analytic orientation to information processing. When people are operating in a heuristic mode, they focus on that subset of information that enables them to use simple decision rules or heuristics [30] to form a judgment rather than carefully evaluating all of the available information. This is based on premise that people are ‘cognitive misers’ and will expend the least possible amount of cognitive effort that is possible in a given situation unless they are motivated to do otherwise; therefore, when not motivated to process information systematically, message receivers will operate in a heuristic mode. In this heuristic mode, receivers consider only a few informational cues and form a judgment based on these cues rather than on careful consideration of all of the cues available in a given message [29].

2.1. Truth bias The ‘truth bias’ [22] is one of the most commonly cited heuristics in the deception literature. In the context of relational familiarity, truth bias draws upon the belief that messages delivered by friends and acquaintances must be truthful, but it can be applied as well to interactions with strangers. The basic assumption of a truth bias is that when people are required to decide if a person is telling the truth or being deceptive, receivers

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will tend to make more truthful assessments than deceptive ones. People who have been asked to rate truth versus deception have consistently exhibited a substantial truth bias, even when the base rate of deception has been varied [19]. Viewed as a heuristic, the truth bias reflects a cognitive shortcut, a simple decision rule that arises from conventional beliefs and expectations that are used repeatedly in the everyday interaction process. Everyday truth judgments must often rely on stereotypical knowledge that is detached from the assessment of authentic cues [15]. This is especially true when receivers are unmotivated to carefully appraise the sender’s communicative behavior, or are disrupted from appraising the sender’s communicative behavior [17, 18, 28]. Thus, the first hypothesis of this study predicted that: H1: Receivers make more truth assessments than deceptive assessments.

2.2. Visual Bias Just as accuracy in detecting truth and deception can be influenced by a general tendency to tag all incoming information as truthful, so can it be affected by the medium over which messages are transmitted. Extensive research on channel reliance has demonstrated systematic differences in interpretations of communication encounters associated with utilization or exposure to various communication “modalities” such as text-only, audio-only, video-only or audio-visual modalities [3, 4, 13]. Contemporary research on computer-mediated communication (CMC) is further underscoring the importance of considering communication modality in detecting potentially deceptive messages [6]. According to media richness theory [10, 11], communication in different channels may affect deception detection differently [33] because of differences in the availability of social information. Text interactions, for example, only make available verbal information (save for efforts to add in “nonverbal” information through such features as capitalization and emoticons). Auditory channels add vocal information, and audiovisual modalities add kinesic, proxemic, physical appearance, and environmental information. Given strong evidence that humans exhibit a primary visual bias, that is, they are drawn first and foremost to visual information [8], audiovisual modes of communication represent rich resources for deceivers to manipulate and craft a credible presentation. Although such richness conceivably could also advantage receivers, attending to the multiple channels that are available in audiovisual communications increases the cognitive demands on receivers and may make them prone to rely on heuristic cues in audiovisual settings because there are multiple channels of information to manage and to decode.

In addition to cognitive load, audiovisual interaction can create a sense of immediacy and involvement between sender and receiver upon which skillful deceivers can capitalize. Senders may make receivers feel like a friend or confidant. A level of trust between the sender and receiver can be established by incorporating friendly nonverbal cues (e.g., smiling, laughing) that may not be so readily present in less “rich” encounters. Thus, senders have the capability of manipulating the familiarity of the situation in which the communication encounter is occurring and engendering a more powerful truth bias. When communication occurs via audio modalities, senders have only the voice and words to manipulate, and receivers need pay attention to fewer channels when decoding information. With fewer nonverbal channels available, there may also be less chance for senders to foster familiarity and trust through nonverbal actions. The absence of these cues may enable the receiver to focus more heavily on the words the sender is using to convince the receiver of his or her truthfulness. In short, with fewer channels to decode, audio receivers should be less likely to rely on heuristics in order to make a decision about truth versus deception than audio-visual receivers. Finally, in a text-only interaction (e.g., e-mail), a sender can only manipulate the verbal cues in the communicative interaction, and the only cues the receiver has to access are the words on the page or screen. With such lean media, there is a greater sense of detachment between sender and receiver and less potential for deceivers to establish immediacy, rapport, and trust with their targets [6]. Previous research has established that with greater detachment comes a reduction in truth bias and perhaps greater objectivity. Additionally, there is less chance for heuristic processing due to the lessened cognitive processing needed to interpret text (compared to the multiple channel processing of the visual condition). Thus, it seems plausible that the truth bias will be less in text contexts than in either audio-visual or audio contexts. The second hypothesis posits: H2: Truth bias is strongest in the audiovisual condition, intermediate in the audio condition, and lowest in the text condition.

3. Deception Detection Accuracy Closely related yet distinct from the matter of biased information processing is the matter of judgment accuracy. Although much research has combined truth and deception detection within the same estimates, several deception scholars [7, 19, 32] have argued that separate estimates are needed for deception detection accuracy and truth detection accuracy. We follow that same approach here. In one of the few investigations to examine deceit under computer-mediated communication conditions,

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Burgoon et al. [6] conducted a study in which pairs of participants completed a get-acquainted task under one of four communication modalities: face to face (FtF), text chat, audio-conferencing, or video-conferencing. Pairs were given a series of topics to discuss (e.g., “Describe the worse job you have ever had”). During the discussion, one randomly selected member of each pair was asked to respond deceptively to the topics being discussed. Following this task, the participants engaged in a second, filler task during which no deception occurred. Participants then rated one another on their communication, trustworthiness, and truthfulness. Results revealed that participants in the FtF condition failed to differentiate truthful responding from deceptive responding. The greatest discrimination occurred in the audio condition; in the text condition, deceivers were actually rated as more trustworthy than truthtellers. Thus, the text condition proved to be the worst for detecting deception. The Burgoon et al. [6] study evidences the fact that people are visually oriented and might base decisions of deception and truthfulness on how the other person is acting. Visual cues (e.g., nonverbal cues) affect how humans respond to other humans; thus, receivers tend to think that deception can be detected more easily by seeing the person. Stereotypic, non-diagnostic indicators often tend to rule how deception is determined, and are often inaccurate [31]. When people interact face-to-face, visual cues like attractiveness, positive nonverbals (e.g., smiling, gestures), tone of voice and dress can have a major impact. These heuristic cues often lead to incorrect processing of information from the sender (e.g., the receiver only notices that the sender is attractive and misses the fact that the story being told is not coherent); therefore, people should be less accurate at detecting deception in this condition than they are at detecting truth. Truth bias in the visual condition may enable a person to be accurate in determining truth but less accuracy will occur in detecting lies in the visual condition. While audio communication lacks some of the cues that are present in audiovisual presentations, audio communication is still a rich source of information. Audio communication also lacks many of the stereotypical (and incorrect) visual cues that people rely upon to make judgments of deception or truth. This lack of stereotypical cues may force receivers to attend to different cues (such as the believability and cohesiveness of a story or the characteristics of the sender’s voice) and these cues may be more diagnostic of truth and/or deception than the stereotypical visual cues. Additionally, if truth bias is lessened in audio condition, this would make receivers more willing to judge deceptive communications as deceptive, and this could increase the overall accuracy of judgments; therefore, we hypothesized that detection of deception accuracy would be highest in the audio condition.

In principle, of course, deception ought to be, and is, detectable from textual features [31]; yet, text is a relatively lean medium and is for many an unfamiliar way to communicate. This makes it easy for people to misread others in this condition (e.g., thinking someone is being rude in a brief e-mail). A major problem with deception detection in this condition is that untrained detectors not only lack familiarity with linguistic and other textual features as clues to deception, they also tend to focus on the wrong cues and overlook the highly diagnostic ones [2, 35]. Another factor that increases the difficulty of detecting deception in this form of communication is that the sender has more time to construct a persuasive message (e.g., can fix mistakes before sending information) than in other methods of communication. This may allow senders to construct messages that are more believable and increase the chances that the receiver can be fooled. This should be most pronounced in asynchronous text messages (such as e-mail), but should also be present in more synchronous text communications (such as chat or IM). This is because, while not allowing the time to edit and craft that completely asynchronous communications offer, slightly asynchronous communications still offer more time to craft and edit than completely synchronous communications (such as face to face conversations). Because the sender has time to manipulate the message and receivers are generally unfamiliar with the textual cues that are diagnostic of deception, deception may be harder to detect in text communications. Thus, people should be less accurate at detecting deception in text than audio; thus, the third hypothesis of this study posited: H3: Deception detection is the most accurate in an audio modality, and less accurate in audiovisual and text modalities. It should also be noted here that, per signal detection theory, bias is generally considered to be independent from accuracy. That is to say that the same accuracy could be reached while at the same time representing very different biases. For example, imagine a sample of materials in which 50% of the materials are truthful and 50% are deceptive. One could obtain 50% accuracy while exhibiting either a complete truth bias (e.g. all materials judged as truthful) or complete deception bias (e.g. all materials judged as deceptive). While all values of bias may not be compatible with all levels of accuracy, a wide variety of bias scores are compatible with a wide variety of accuracy scores in samples that are roughly balanced. Because accuracy is relatively independent from bias, it is possible to show increased bias and increased accuracy or increased bias and decreased accuracy, [36] Concerning accuracy of truth detection by modality, it is unclear which modality would have the highest rate of accuracy, thus formal hypotheses are not posited here.

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However, it is important to address how truth detection may play out within each modality. Due to the visual bias present in video modalities, receivers may judge the majority of senders as truthful and thus achieve high truth accuracy more as an artifact of truth bias than as a result of accurate information processing. Comparatively, audio modalities include reliable (diagnostic) information and may reduce the truth bias; therefore, audio modalities may yield a high degree of accuracy when judging both truth and deceit. Finally, text modalities, by virtue of creating a greater sense of detachment, may lead to more moderated or tempered assessments of truth and hence result in less accuracy when judging truthful messages. However, the fact that text-based cues can be highly reliable indicators of truth or deceit leaves open the possibility of high accuracy in judging truthful messages in text conditions. Thus, it may be that different communication modalities do not vary on truth detection accuracy, only on deception detection accuracy.

4. Method 4.1 Participants The sample consisted of 51 undergraduate students enrolled in communication courses at a large mid-western university. Each subject was randomly assigned to one of three conditions: (1) audiovisual, (2) audio, or (3) text, and each participant was randomly assigned to judge one material in the assigned condition. Seventeen students judged one material a piece in the audiovisual condition, 17 judged one material in the audio condition, and 17 judged one material in the text condition. Students who participated in the study received extra credit for their participation.

4.2 Procedure Upon arriving at the study site, participants were placed at a computer where they completed a consent form and received experimental instructions. The instructions explained that they would be observing an interviewee being questioned about the theft of a wallet and that the interviewee would be making a plea of innocence, regardless of whether they were innocent or guilty. The instructions then stated that the participant’s task was “to determine whether the person that you will be observing is telling the truth about his/her innocence or whether the statement they are giving is deceptive.” After observing the interview, the participants completed a brief questionnaire about what they saw, heard, or read, were debriefed about the purpose of the study, given credit, and thanked for their time.

4.3 Judgment Materials The video, audio, and text files for this study were recorded from a mock theft study conducted by the authors. In the mock theft study, participants were randomly assigned to a deceptive or innocent condition. Those in the deceptive condition were asked to take a wallet from a classroom on an assigned day. Those in the innocent condition were simply told that a theft would occur from the classroom on a given day. Both in the innocent and the guilty were motivated to succeed by offering them a small monetary prize if they could convince the interviewer that they were innocent. All of the mock theft subjects in this study were video recorded. The interviewer on the video asked each interviewee a structured set of questions that included items such as, “Did you take the wallet,” and “Do you know where the wallet is now?” A total of 17 videos (9 innocent and 8 deceptive subjects) from the mock theft study were randomly selected for this study. These videos were then turned into two types of windows media files. The first contained both the video and audio from the videotape. The second contained only the audio from the videotape. A transcript of each of the 17 interviews was also produced. In the audiovisual conditions, the participants of this study observed the windows media file that contained both video and audio. In the audio condition, the participant listened to the windows media file that contained only audio, and in the text condition, the participants read the transcript of the interaction from a computer screen.

4.4 Instrumentation Participants were given a questionnaire1 to complete after observing the interviewer and interviewee interaction. Participants rated, on a 0 to 10 scale, how truthful they thought the interviewee was in answering the questions in the interview. In addition, subjects were asked to rate how much confidence they had in their assessments. Finally, subjects were asked to dichotomously indicate whether they thought that the interviewee was guilty or innocent of taking the wallet.

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The first half of the questionnaire had participants rate the person on 32 attributes (7-point Likert-type scale) (e.g., credible, disinterested, sincere, tense). Subjects also completed 24 items about the interviewee’s information management (e.g., The interviewee gave very brief answers). Subjects were also asked if they would choose the interviewee as a roommate, as a job candidate, as a house sitter for pets, and as a date for a friend (7-point Likert-type scale). These items were not included in the data analyses.

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Graph 1 – Mean Truth Estimates by Modality

5. Results

8.2

5.1 Hypothesis 1 8.0 8.0 7.8

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Mean Truth Estimate

Hypothesis 1 predicted that participants would make more truthful than deceptive judgments. On the dichotomous judgments, 67% of the participants indicated that they thought that the interviewee was truthful and 33% judged the interviewee as deceptive. This finding was significantly different from chance (t (50) = -2.05, p < .05). On the 10-point truthfulness scales, the mean judgment was 7.58 (SD = 1.58). This mean was significantly higher (more truthful) than the midpoint of the scale (t (50) = 11.70, p < .001). These results support Hypothesis 1. The participants made more truthful judgments than deceptive ones.

7.4

7.2 7.1 7.0 Text

Audio

Video

MODALITY

5.2 Hypothesis 2

5.3 Hypothesis 3

Hypothesis 2 predicted that the truth bias observed in Hypothesis 1 would be largest in the video condition, intermediate in the audio condition and smallest in the text condition. In the visual condition, 14 (82%) of the participants judged the material as truthful; in the audio condition, 12 (71%) of the stimulus materials were judged as truthful, and 8 (47%) of the subjects were judged as truthful in the text condition. These differences approached significance (X2(2) = 4.91, p = .086). A planned linear contrast that treated the proportion of truthful judgments as a mean revealed an ordinal increase in the proportion of truthful judgments across modalities (F (1, 48) = 4.97, p < .05). As can be seen in Graph 1, the mean truth estimate for the video group was 7.98 (SD = 1.92), the mean for the audio group was 7.65 (SD = 1.36), and the mean for the text group was 7.12 (SD = 1.35) on the ten-point scales. These mean differences approached statistical significance using a planned linear contrast (t (48) = 1.61, p = .055, one-tailed). Overall, these findings are supportive of Hypothesis 2. The most truth bias appeared to occur in the audiovisual condition. The next most bias was in the audio condition, and the least in the text condition.

Hypothesis 3 predicted that detection of deception would be the most accurate in the audio condition and lower in the text and video conditions. Because the hypothesis did not make any prediction for the truthful materials, only the deceptive materials were considered for this hypothesis. Observers were correct in judging 3 (38%) of the deceptive stimulus materials as deceptive in the audio condition, 3 (38%) of the deceptive materials in the text condition, and 1 (13%) of the deceptive materials in the video condition (See Graph 2). These differences were not significant using a X2 test (X2 (2) = 1.61, p = .45). Regarding the 10-point truth assessment scales, the mean rating for deceptive subjects in the text condition was 7.11 (SD = 1.50), the mean truth assessment in the audio condition was 7.07 (SD = 1.25), and the mean truth assessment in the video condition was 8.41 (SD = 1.72). A planned contrast (-1, 2, -1) was non-significant (t (21) = 1.06, p = .30). Hypothesis 3 was not supported. Examination of the mean truth assessments suggested that the deceptive subjects in the video condition may have been evaluated as being more truthful than the deceptive subjects in either the audio or text conditions. A post hoc test (Tukey’s B) was conducted to examine this possibility. This test did not reveal a significant difference in the assessments of truthfulness of the deceptive subjects by condition. Hypothesis 3 was not supported. No differences in detection of deception accuracy were observed by modality.

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Graph 2 – Correct Judgments by Modality 8 7

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Text 1 1 0

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negative rate. For example, a single error in intelligence analysis could have profound implications for national security. This means that the reduced truth bias found in text or audio conditions may be preferable for intelligence assessment tasks. To conclude, deception displays are dynamic and complex and it is important to continue conducting research that incorporates the multifaceted nature of a deceptive encounter. The results of this study are preliminary in nature, but they suggest that modality could have a profound impact on truth bias and this could in turn have serious implications for the detection of deception. These possibilities warrant further research into the impact of modality on truth bias and the deception of detection.

Deceptive

7. References

Judgment

6. Discussion In this study, experimental subjects exhibited a general truth bias, and support was found for our belief that richer media may generate more truth bias. We did not find support for our hypothesis that subjects would be more accurate in detecting deception in the audio condition. We instead found that detection of deception accuracy was below 50% in all of the conditions and that there were no significant differences in detection by condition. This general inability of receivers to detect deception is consistent with previous research [19, 31]. Our failure to find accuracy differences by modality may be the result of a lack of power. We plan to collect additional data to address this possibility. It may also be that the low stakes nature of the “mock theft” scenario used to develop the stimulus materials for this study did not produce many differences in behavior between truthful and deceptive subjects for the receivers to detect. We are currently examining this possibility by coding the judgment materials for nonverbal and verbal cues associated with deception. Of course, it is also possible that receivers are simply poor at detecting deception regardless of communication modality. While our detection of deception accuracy did not vary significantly by modality, our finding that truth bias varied by modality could have profound implications for the detection of deception. This finding suggests that false positive and false negative rates can vary by modality without having a large impact on accuracy. It may then be that the biases inherent in different modalities would make certain modalities preferable for different detection of deception tasks. For example, our criminal justice system values protection of the innocent; therefore, this system would want as few false positives as possible. This suggests that the truth bias inherent in the video condition may desirable in courtroom detection of deception tasks. Other systems may desire a low false

[1] D. B. Buller, J. K. Burgoon, W. Afifi, C. H. White, and A. L. S. Buslig. “Judging message credibility: The impact of heuristics, cognitive busyness, and strategic behavioral adaptation”, Paper presented to the annual meeting of the International Communication Association, Montreal, May, 1997. [2] D. B. Buller, J. K. Burgoon, C. H. White, and A. S. Ebesu, “Interpersonal deception VII. Behavioral profiles of falsification, equivocation, and concealment”, Journal of Language and Social Psychology, 1994, 366-395. [3] J. K. Burgoon, “The relationship of verbal and nonverbal codes”, In B. Dervin & M. J. Voight (Eds.), Progress in Communication Sciences. Norwood, NJ: Ablex Publishing, 1985, pp. 263-298. [4] J. K. Burgoon, “Nonverbal signs”, In M. L. Knapp & G. R. Miller (Eds.), Handbook of Interpersonal Communication, Sage, Beverly Hills, CA, 1994, pp. 344-390. [5] J. K. Burgoon, J. A. Bonito, A. Ramirez, K. Kam, N. Dunbar, and J. Fischer, “Testing the interactivity principle: Effects of mediation, propinquity, and verbal and nonverbal modalities in interpersonal deception”, Journal of Communication, 2002, pp. 657-677. [6] J. K. Burgoon, J. A. Bonito, G. M. Stoner, and N. E. Dunbar, “Trust and deception in mediated communication”, Proceedings of the 36th Hawaii International Conference on System Sciences, IEEE, Los Alamitos, 2003. [7] J. K. Burgoon, D. B. Buller, A. S. Ebesu, and P. Rockwell, “Interpersonal deception: V. Accuracy in deception detection”, Communication Monographs, 1994, pp. 303-325. [8] J. K. Burgoon, D. B. Buller, and G. W. Woodall, Nonverbal Communication: The Unspoken Dialogue, McGraw-Hill, New York, 1996. [9] S. Chaiken, “Heuristic versus systematic information processing and the use of source versus message cues in

0-7695-2268-8/05/$20.00 (C) 2005 IEEE

6


persuasion”, Journal of Personality and Social Psychology, 1980, pp. 752-766. [10] R. L. Daft and R. H. Lengel, “Information richness: A new approach to managerial behavior and organization design”, In B. M. Staw & L. L. Cummings (Eds.), Research in Organizational Behavior, JAI Press, Greenwich, CT, 1984, pp. 191-233. [11] R. L. Daft and R. H. Lengel, “Organizational information requirements, media richness, and structural design”, Management Science, 1986, pp. 554-571. [12] B. M. DePaulo, J. J. Lindsay, B. E. Malone, L. Muhlenbruck, K. Charlton, and H. Cooper, “Cues to deception”, Psychological Bulletin, 2003, pp. 74-118. [13] B. M. DePaulo, M. Zuckerman, and R. Rosenthal, “Detecting deception: Modality effects”, In L. Wheeler (Ed.), Review of Personality and Social Psychology. Sage, Beverly Hills, CA, 1980, pp.125-162. [14] P. Ekman, M. O'Sullivan, and M. G. Frank, “A few can catch a liar”, Psychological Science, 1999, pp. 263-266. [15] K. Fiedler and I. Walka, “Training lie detectors to use nonverbal cues instead of global heuristics”, Human Communication Research, 1993, pp. 199-223. [16] M. G. Frank and P. Ekman, “The ability to detect deceit generalizes across different types of high-stake lies”, Journal of Personality & Social Psychology, 1997, pp. 1429-1439.

[24] B. R. Schlenker, “Self-presentation”, In M. R. Leary & J. P. Tangney (Eds.), Handbook of Self and Identity, Guilford Press, New York, 2002, pp. 492-518 [25] B. R. Schlenker and M. R. Leary, “Social anxiety and selfpresentation: A conceptualization and model”, Psychological Bulletin, 1982, pp. 641-669. [26] B. R. Schlenker and B. A. Pontari, “The strategic control of information: Impression management and self-presentation in daily life”, In A.Tesser, R. Felson, & J. Suls (Eds.), Perspectives on Self and Identity, American Psychological Association, Washington, D.C., 2000, pp. 199-232. [27] J. B. Stiff, H. J. Kim, and C. N. Ramesh, “Truth biases and aroused suspicion in relational deception”, Communication Research, 1992, pp. 326-345. [28] J. B. Stiff, G. R. Miller, C. Sleight, P. Mongeau, R. Garlick, and R. Rogan, “Explanations for visual cue primacy in judgments honesty and deceit”, Journal of Personality and Social Psychology, 1989, pp. 555-564. [29] A. Todorov, S. Chaiken, and M. D. Henderson, “The heuristic-systematic model of social information processing”, In J. P. Dillard & M. Pfau (Eds.), The Persuasion Handbook: Developments in Theory and Practice, Sage Publications, London, 2002, pp. 195-211. [30] A. Tversky, and D. Kahneman, “Judgment under uncertainty: Heuristics and biases”, Science, 1974, pp. 11241131.

[17] D. T. Gilbert, D. S. Krull, and P. S. Malone, “Unbelieving the unbelievable: Some problems in the rejection of false information”, Journal of Personality and Social Psychology, 1990, pp. 601-613.

[31] A. Vrij, Detecting Lies and Deceit: The Psychology of Lying and the Implications for Professional Practice. Wiley and Sons, Chichester, England, 2000.

[18] T. R. Levine, and S. A. McCornack, “A critical analysis of the behavioral adaptation explanation of the probing effect”. Human Communication Research, 1996, pp. 575-588.

[32] A. Vrij and S. Mann, “Who killed my relative? Police officers' ability to detect real-life high-stake lies”, Psychology, Crime, & Law, 2001, pp. 119-132.

[19] T. R. Levine, H. S. Park, and S. A. McCornack, “Accuracy in detecting truths and lies: Documenting the ‘veracity effect’” Communication Monographs, 1999, pp. 125-144.

[33] J. B. Walther, and M. P. Parks, “Cues filtered out, cues filtered in: Computer mediated communication and relationships”, In M .L. Knapp, J. A. Daly, & G. R. Miller (Eds.), The Handbook of Interpersonal Communication, Sage, Thousand Oaks, CA, 2002.

[20] S. Mann, A. Vrij, and R. Bull, “Detecting true lies: Police officers' ability to detect suspects' lies”, Journal of Applied Psychology, in press. [21] S. A. McCornack, “Information manipulation theory”, Communication Monographs, 1992, pp. 1-16.

[34] M. Zuckerman, B. M. DePaulo and R. Rosenthal, “ Verbal and nonverbal communication of deception”, In L. Berkowitz (Ed.), Advances in Experimental Social Psychology, Academic Press, New York, 1981, pp. 1-59.

[22] S. A. McCornack, and M. R. Parks, “Deception detection and relationship development: The other side of trust”, In M. McLaughlin (Ed.), Communication Yearbook. Sage, Beverly Hills, CA, 1986, pp. 377-389.

[35] M. Zuckerman, and R. E. Driver, “Telling lies: Verbal and nonverbal correlates of deception”, In F. S. (Ed.), Multichannel Integrations of Nonverbal Behaviors Lawrence Erlbaum, Hillsdale, NJ, 1985, pp. 129-147.

[23] B. A. Pontari and B. R. Schlenker, “The influence of cognitive load on self-presentation: Can cognitive busyness help as well as harm social performance?” Journal of Personality and Social Psychology, pp. 1092-1108.

[36] D. M. Green and J. A. Swets, Signal detection theory and psychophysics Wiley, New York, 1966.

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