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POTENTIAL NONCONTACT TOOLS FOR RAPID CREDIBILITY ASSESSMENT

FROM PHYSIOLOGICAL AND BEHAVIORAL CUES

Judee K. BurgoonCenter for the Managementof Information

University of ArizonaPO Box 210108Tucson, AZ 85721-0108USA

Douglas C. DerrickCenter for the Managementof Information


Aaron C. ElkinsCenter for the Managementof Information


Sean LaMarc HumphreysCenter for the Managementof Information


Matthew L. JensenMIS DivisionPrice College of BusinessUniversity of Oklahoma307 W. Brooks, Suite 308ENorman, Oklahoma 73019USA

Christopher B.R. DillerCenter for the Managementof InformationUniversity of ArizonaPO Box 210108Tucson, AZ 85721-0108USA

Jay F. Nunamaker, Jr.Center for the Managementof Information


Abstract - Credibility assessment is a perennial and

increasingly urgent problem in light of escalating internationalsecurity threats. New tools such are needed for rapid,noninvasive and possibly unobtrusive detection of deceptionand hostile intent. This paper reports five novel instrumented

approaches to credibility assessment being investigated in amulti-institution research program. These instruments do notrequire physical contact with humans and can reliablymeasure veracity from physiological and behavioral indicators.Data were collected via an experiment, which requiredparticipants to commit a mock crime and then be interviewedby a trained interviewer. During and following the interviews,multiple instruments measured physiological, cognitive andbehavioral responses of interviewees to determine whichautomatable features accurately differentiate truthtellers from

deceivers. Details concerning the instruments and theexperimental method used to test them are shared.

Index Terms Credibility assessment, deception, non-invasive, rapid screening

I. INTRODUCTION

Credibility assessment is a perennial and increasinglyurgent problem in light of escalating international securitythreats. Added to this, there is a growing need for tools toquestion criminal suspects as well as accurately screen

employees. These new tools are necessary because a largeand consistent body of research shows that absent specialtraining or technological aid, deception detection accuracy is

near chance. A meta-analysis summary of over 200experiments shows that people are, on average, correct 54%of the time at distinguishing truths from lies [1,2]. This level ofaccuracy is statistically greater than chance, but is poor in anabsolute sense. Further, specific individual differences such

as age, professional experience, education, cognitive ability,and social skills make little difference [2] and there is littlevariance overall in receivers ability to detect deception [3].Criticisms of existing tools such as the polygraph havemotivated the search for alternative technologies to detect

deception and hostile intent, especially ones that could be

implemented unobtrusively and noninvasively in field settingssuch as airport and checkpoint screening. Technologies andtechniques must be applicable across different cultures andintercultural contexts relevant to screening and educing

information.

II. POTENTIAL INDICATORS OF DECEPTION

Whether behaviorally or physiologically based, at the core

of detecting lies is a fundamental question regarding whatdistinguishes truth from deception and under what conditions.Most current detection methods, both behavioral observationand technological assessments, presume that emotions,arousal, and/or cognitive effort prompt physiological,

psychological and/or behavioral responses that provide abasis for reliable lie detection. This current study evaluatesboth psycho-physiological and behavioral cues for deception.

A. Psycho-Physiological Indicators

Research has shown that those who are deceiving oftenexperience a different state of arousal and stress than truthtellers. This state of arousal may be manifested in severalphysiological responses including: pupil dilation, change inheart rate and blood pressure, increase in body temperatureespecially around the face and eyes, and changes in blink

patterns. It may also be associated with a host of behavioralresponses that have been termed leakage because they areunintentional signs that leak out of the body [4]. Ideally,

screening technologies should take advantage of all of thesepossible measures so as to obtain the most robust andreliable assessment of an individuals state of arousal andstress.

We are scientifically examining these technologies in order

to determine their reliability and feasibility for implementationin a seamless, unified, and automated system of deceptiondetection from multiple sensors. . Table I below lists thepotential physiological or behavioral indicator and themeasurement device under investigation.

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TABLE IPHYSIOLOGICAL INDICATORS OF DECEPTION AND

MEASUREMENT DEVICESPotential Indicator Measurement Device under

Investigation

Pupil dilation Near-infrared camera

Increases in facial bodytemperature Thermal camera

Heart rate, bloodpressure

Laser Doppler vibrometer

Micro blinks Ultra-fast camera and startle response

Eye movement Infrared eye tracker

1) Pupillometric Indicators: Pupillometry is the study ofchanges in pupil size and movement. Pupil diameter is knownto enlarge with increased physical effort [5], mental tasks [6]and simple memory tasks [7]. Pupil dilation can result fromsympathetic nervous system stimulation or suppression of theparasympathetic nervous system. This peripheral nervoussystem response, like thermal imaging, is theorized to reflectarousal or stress.

It has been shown that larger pupil dilations are found inguilty participants of Conceal Information Tests [8,9].Cognitive processes as well as emotional arousal result inpupil dilations and the cognitive processes involved indeception are found to impact pupillary response. Dionisio,Granholm, Hillix and Perrine [10] report that deceptiveresponses to a recall task reliably produced larger pupilresponses than truthful answers. The pattern of access intolong-term memory during deception has been associated withdifferences in pupil dilation [11,12] as well.

2) Thermal Indicators: Thermal imaging technologymeasures changes in regional facial blood flow, particularlyaround the eyes. Changes in the orbital area may capturechanges in blood flow related to the fright/flight response

mediated by the sympathetic nervous system.Pavlidis and colleagues [13] created a thermal signature

of deception that is detectable via infrared technology. In the2002 Pavlidis et. al. experiment conducted at DoDPI,randomly-assigned volunteers participated in a mock crime inwhich they were asked to stab a mannequin and rob it [13].The participants were later instructed to assert their innocenceduring a thermal imaging exam. In the test, 83% of theparticipants were correctly categorized as innocent or guilty.(75% of the guilty participants were correctly identified along

with 90% correct categorization of innocent individuals).

3) Cardio-respiratory Indicators: Pulse rate, blood pressureand respiration rate have been shown to be reliable indicatorsof deception [14]. These measures comprise a significant set

of the features evaluated during a conventional polygraphexam. Cardio-respiratory measures are sensitive to emotionalstress, which is predicted to vary between truth tellers and

liars. This is believed to result from an increase in cognitiveeffort and behavioral control needed to appear convincing[15].

Differences in cardiovascular measures such as increasedpulse rate have been identified during deceptivecommunication [16]. Cardiovascular measures areparticularly appealing because they remain involuntary, while

breathing can be regulated. Despite this fact, recent studieshave shown that individuals tend to inhibit breathing whenfaced with stress commonly found during deceptive situations[17]. Continuing advances in technology promise to providemore potential cardiovascular indicators of deception including

systolic time intervals and contractility [18].

4) Blinks and Startle Response Indicators: It has beenshown that eye blinks as accurate indicators of stress and theblink component of the startle pattern are related to emotionalstate and attention. Blink rates during a guilty-knowledge testwere higher when participants falsely denied recognition ofthe stimuli. Additionally, Stern [19] fund that blinks punctuate

moments of taking in and processing information.

5) Eye Movement Indicators: Recent research hassupported the claim that eye behavior and eye movement canbe used for memory assessment. Results indicate thatparticipants eye movements when viewing repeated itemsdiffer from their responses to previously unseen items. Thisincludes fewer fixations in fewer regions and more

randomness in eye movement patterns. For example, Althoff

and Cohen [20] found differences in peoples responses tofamiliar versus novel faces. This technology can be applied todeception detection with concealed knowledge tests [18].

B. Behavioral Indicators

Behavioral indicators stem from the idea that there are

emotional and arousal correlates of deception as well asintentional strategies deceivers employ to evade detection thtare displayed nonverbally. Some behaviors, like thoseassociated with emotional states, are not entirely underconscious control. Compared to the honest message source,deceivers are apt to experience guilt, fear of detection, andperhaps other emotions created by the act of lying [21].Deceivers try to control behavioral displays so as not to give

themselves away, but nonverbal cues to deception leak outanyway, often through nonverbal channels that are moredifficult to control. In other cases, deceivers may intentionallyadopt a pleasant demeanor so as to enhance their credibility.Thus, nonverbal behaviors related to movement (kinesics),voice (vocalics), and distancing (proxemics) are potentiallyreliable deception indicators. The instruments to measurethese automatically are under investigation.

1) Linguistic Indicators: Empirical evidence suggests thatdeceivers may use language differently than truth-tellers [22-24]. Deceivers may have shorter talk time, share fewerdetails, and display elevated uncertainty [1]. Deceivers mayalso demonstrate less language diversity, less complexity,and use more negative words [24].

Numerous linguistic features may be used to gauge thecredibility of messages. For example, both word counts and

verb counts may be used to judge message lengths andaverage sentence lengths, and average word lengths may beused to judge language complexity. Zhou and colleagues[25,26] present a listing of linguistic features that may beuseful in deception detection. This list of features issummarized Table II below.

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TABLE IILINGUISTIC FEATURES USED IN DECEPTION DETECTION

Category Feature Description

Quantity Words, verbs,noun phrases,sentences

Raw counts of words,verbs, noun phrases, andsentences

Clauses,

sentence length

Clauses per sentence;

Words per sentencesWord length Characters per word

Noun phraselength

Words per noun phrases

Complexity

Pausality Punctuation marks per sentence

Modifiers,

modal verbs,otherreferences

Raw counts of modifiers,

modal verbs, and thirdperson pronouns

Uncertainty

Uncertainty Count of words denotinguncertainty

Passive voice Passive verbs divided bytotal verbs

Non-immediacy

You, self, and

other reference

Raw counts of second-

person pronouns, first-

person singular, and first-person plural pronouns

Expressivity Emotiveness (Adjectives + adverbs) /(nouns + verbs)

Lexical diversity Unique words divided by

total words

Content word

diversity

Unique content words

Diversity

Redundancy Function wordspersentence

Temporal-immediate,temporal-

nonimmediate

Temporal-immediatewords our of total words;Temporal-nonimmediate

words out of total words

Specificity

Spatial-close,spatial-far

Close words / total words;Far words / total words

Positiveimagery,

negativeimagery

Mean imagery score;Positive sensorial words /

total words; Negativesensorial words / totalwords

Positivepleasantness,

negativepleasantness

Mean pleasantnessscore; Pleasant words /

total words; Unpleasantwords / total words

Affect

Positiveactivation,negative

activation

Mean activation score;Positive activation words /total words; Negative

activation words / totalwords

These linguistic features are useful for both synchronous andasynchronous conversations and are founded upon research

that has taken place in face-to-face interactions [25]. To date,however, while all published studies report numerousstatistically significant language differences, these differenceshave failed to replicate from study to study [24,27].

2) Kinesic and Proxemic Indicators: In addition to linguisticanalysis, another unobtrusive deception detection method iskinesic analysis. Empirical evidence suggests that deceivers

move their hands and heads differently than truth-tellers. Twometa-analyses conclude that there is a significant decrease inthe amount of illustrating deceivers do in comparison to truth-tellers [28,29]. Illustrating gestures are those gestures whichnormally accompany speech. They can include iconics,metaphorics, beats, and cohesives [30]. Illustrating gesturescan represent semantic content in speech, can emphasize

certain points, or can designate a relationship between ideasin speech. For example, an illustrator may be seen when aperson expresses uncertainty by using a palms-up gesturecalled the hand shrug (often accompanied by a shouldershrug). DePaulo et al. [28] also point out that deceiversdisplay significantly more chin raises than truth-tellers. They

also observed less but non-significant undifferentiated headmovement in deceivers; however, Buller et al. [22] found thatdeceivers show significantly less total head movement thantruth-tellers. Although automated tools cannot yet identifyspecific hand shapes, postures, leans, head movements, orface orientations, Meservy and colleagues [31] havedeveloped a set of features that can be extracted from 2Dimage (frame) sequences that correspond to elements of

these kinesic and proxemic indicators. These features have

been used productively in past research for identifying bodymovements that are correlated with deception. Thesefeatures appear below Table III.

TABLE IIIKINESIC FEATURES IN DECEPTION DETECTION

Body PartMeasured Feature Description

Head, RH,LH

x, y positionPosition of the center point

Head AngleAngle of declination

Head Angledifference

Difference in angles betweenprevious frame and current frame

Head, RH,LH

Difference Distance between x, y positionbetween previous and current

frameHead, RH,LH

Triangle area Area of triangle formed byconnecting right, left hands andhead

Head, RH,LH

Distance Distance between head, and onehand; Euclidean distance

between hands

Head, RH,LH

Tri-centerdistance

Distance between the center ofthe triangle connecting the head

and hands and each of thehands and head

RH, LH Top

Quadrant

Dichotomous flag showing the

presence of the hand above thechin

RH, LH Right

Quadrant


presence of the hand to the right

of the right shoulderRH, LH Center

Quadrant


presence of the hand betweenthe left and right shoulders

RH, LH LeftQuadrant

Dichotomous flag showing thepresence of the hand to the leftof the left shoulder

3) Vocalic Indicators: The link between deception andvocalic features stretches through stress, arousal, andcognitive load. Cognitive load is captured by numerous time-

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based measures such as utterance or turn duration, numberof turns, duration of silences, turn switch pauses. Stress andarousal are estimated by fundamental frequency (F0)measures such as mean, minimum, maximum, and variancethat capture pitch and pitch change; and energy-based

measures such as RMS (root-mean-square) mean, maximumand minimum that measure sound amplitude over time.

III. POTENTIAL DETECTION TECHNOLOGIES

Numerous technologies have been developed toautomatically and unobtrusively or noninvasivelyevaluate a persons credibility during an interaction.These technologies fall into two categories previouslymentioned: psycho-physiological and behavioral. Psycho-

physiological methods generally focus on detecting elevatedinternal arousal, stress, emotional and cognitive states thataccompany deception. Elevated arousal, for example, can bemonitored via numerous psycho-physiological cues (e.g.,elevated blood pressure). Behavioral methods generallyfocus on detecting changes in overt behavior. These changes

in behavior can be non-strategic or unintentional (e.g.,associated with cognitive effort) or strategic or intentional

(e.g., limiting the detail in responses). Tables IV-IX in thefollowing sections summarize the pros and cons of thesetechnologies for detecting deception.

A. Near-infrared Camera and Imaging

The near-infrared camera measures the changes in pupildilation, which have been associated with measures ofarousal, emotional state, and cognitive load. Limitations forpupillometry for rapid screening assessments are largelybounded by environmental variables and the complexity offactors associated with this physiological response. Physicalexertion, drugs, or agitation may cause dilation and a rapid

screening environment, like an airport, may have displays ofarousal among on travelers. Finally, the changes in the pupilare so minute that excessive movement or motion by theperson being evaluated makes it difficult to measure

accurately. Pupillometry has the benefit of being non-contactand non-invasive. The influence of cognitive processes onpupil dilation also may produce confounds should deceptiondetection assessment arise in a non-native languageexchange. The cognitive effort of communicating in a foreignor non-native language could result in an increase in pupilsize regardless of veracity, which may necessitate accountingfor culture and language.

TABLE IVNEAR-INFRARED IMAGING

Evaluation Criteria:Pupillometry

Result

Validity Theoretically sound; Validity still being

determined

Shortcomings Highly sensitive to subject movement;Requires unobstructed view of eyes

Consistent acrosscultures

High probability

Cost of implementation Moderate

Usefulness in rapidscreening

Yes

B. FLIR Thermal Imaging Camera

Thermal imaging technology measures changes inregional facial blood flow, particularly around the eyes.Changes in the orbital area may capture changes in blood

flow related to the fright/flight response mediated by thesympathetic nervous system. Initial studies of thermal

imaging by Levine and Pavlidis [32] observed a detectableresponse to an auditory startle. Pavlidis and colleagues [13]expand these findings to include a thermal signature ofdeception that is detectable via these imaging techniques.

Tracking the face and capturing the temperature signalfrom the preorbital area during the course of an interaction

determine a deceptive or non-deceptive classification. Theaverage pereorbital temperature is then used to establish abaseline for the individual. This baseline is compared againstinstances of high psycho-physiological value identified with apattern recognition algorithm [33]. Finally, a deceptive or non-deceptive classification is made based on the temperaturesignal patterns identified during the interaction.

Preliminary findings suggest that thermal imaging is a

promising avenue for deception detection. Given that this

technology is still in its infancy, further research is needed todetermine its reliability and consistency, its mapping of whatexactly this technology is capturing, as well as assessing itspotential for use within a screening domain.

TABLE VTHERMAL IMAGING

Evaluation Criteria Result

Validity Validity still being investigated; initialresults are promising

Shor tcomings Sensi tive to environmental changes;

Susceptible to subject movement andphysiology

Consistent across

cultures

High probability

Cost ofimplementation High


Yes

C. Laser Doppler Vibrometer

Similar to thermal imaging or pupillometry, LDV incredibility assessment would be used to measurephysiological activity as an index of emotion, stress or fatigue[18]. This promising technology yields measures of cardio-respiratory and tremor activity, and it may be used to assessfacial movements. The LDV technology is based on thetheoretical concept that internal physiology has mechanicalcomponents that can be detected in the form of skin surface

vibrations. The system senses and measures vibrations frommultiple forms of activity, which are used to differentiateamong stress and emotional states. Because LDV signalscan be obtained during unconstrained interviews orinterrogations, the system is compatible with use in credibilityassessment. The LDV records video in addition to the lasermeasures. Data storage requirements are approximately 60-75 gigabytes per hour. Even though the LDV is a medically-

safe Class-2 laser, prudence suggests not exposing it to theparticipants eyes. Automatic face detection software keepsthe laser on the participants neck and follows his or her

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gentle movements. If the participant moves outside the field ofvision, the laser is automatically shut off for extra safety.

Currently, five limitations have been identified regardingLDVs use in a rapid screening environment, which futuredevelopment can resolve. First, the laser is observable by the

participant, who may change or suppress behavior knowingthat they are being observed. Developmental plans include a

LDV in the non-visible spectrum, which can resolve thispotential limitation. Secondly, the LDV currently requires anoperator to set up, point the laser at the subject, monitor thetracking, and initiate/terminate recording. While the LDVsoftware is easily learnable within minutes and does notrequire technical expertise, this requirement does add to

fielding constraints. Further development of the automaticface detection tracking will reduce the human involvement.Third, the data output requires post-processing and algorithminterpretation by a trained operator for measurements otherthan pulse. Voice vibration through the larynx is recordedalong with the cardio-respiratory activity. This data must bealgorithmically separated from the cardio-respiratory data, aprocess that is not yet real-time. Fourth, the post-processing

methods used to calculate these measurements are nascent

and are not standardized. Finally, the LDV requires a clearline of sight with the carotid artery. Consequently, if anindividual wears a turtleneck sweater or moves erratically, thereliability of the LDVs measurements are compromised.Tracking technologies are continuously improving to cope withmovement. A benefit of the LDV is that it is not negativelyaffected by changes in environmental conditions such as

temperature, lighting conditions, humidity, electromagneticfields, or ambient noise.

TABLE VILASER DOPPLER VIBROMETER


Validity Validity still being investigated; initialresults are promising

Shor tcomings Only pulse measurements are availablein real-time; Additional measures

require post-processing with no currentstandards; Sensitive to rapid movementbut not environmental changes.


High probability

Cost of

implementation

High


Yes

D. Fast Infrared Camera for Eye Blink

An additional area for investigation along with pupillometry

is the role of eye blink. Fukuda [34] reports that blink ratesduring a guilty-knowledge test are higher when participantsfalsely denied recognition of the stimuli. Additionally, Stern[19] finds that blinks punctuate moments of taking in andprocessing information. Eye blinks in conjunction withinterviews and guilty knowledge tests may complementpupillometry. Moriyama et. al. [35] demonstrate successfulautomated recognition of eye blinking behavior inspontaneous productions with a number of occlusionsincluding glasses and non-frontal poses. This suggests thatautomated detection of blinks in screening assessments is

also a viable technology. Implementation of this technologyrequires further investigation into confounding factors such asage of target and the correlation of blink rate with internalstate. Harrigan and OConnell [36] report that nervousnessresults in an increase in eye blinking while Bagley andManeilis [37], Bauer, Strock, Goldstein, Stern, and Walrath[38], and Wallbott and Schrer [39] all suggest that increased

cognitive load results in a decrease in eye blinking.Additionally, eye blink rate has also been found to decreasewith age [40]. Research in this area is ongoing. With furthervalidation, eye blink in conjunction with pupillometry may beappropriate for screenings in large-scale evaluations.

TABLE VIIFAST INFRARED CAMERA

Evaluation Criteria:

Eye blink

Result

Validity Validity still being investigated; Initialresults are promising

Shor tcomings Highly sensit ive to subject movement;Requires unobstructed view of eyes; Data

intensive


High probability

Cost ofimplementation

Moderate

Usefulness in rapid

screening

Yes

E. Eye Movement Tracker

The benefit of this technology not only includes an

assessment of veracity, but also includes the potential fornarrowing areas for further investigation. For example, if atraveler entering a checkpoint is selected for secondaryscreening and this test is administered, their habituationresponse not only may indicate deception (i.e., if their eyemovement indicates familiarity with a weapon amongst anarray of objects) but the weapon under question is alsoidentified. While this technology would be difficult to

implement in large-scale rapid screening, its value may lie inrapid secondary assessments. Cultural concerns for eyemovement-based memory assessment arise from knownbiases for own-race recognition of faces. People may be lessable to discriminate known versus novel stimuli when theseobjects originate from non-native culture. Additionally, it isunknown how well matched test items must be to target itemsfor the appropriate response to register.

TABLE VIIIEYE MOVEMENT TRACKER


Validity Validity still being determined; Initialresults are promising

Shortcomings Requires concealed knowledge;Requires subject cooperation


High probability

Cost of implementation Moderate


Yes

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F. Automated Linguistic Analysis

There are two main approaches to automating theevaluation of credibility in text-based documents: data/textmining and natural language processing (NLP). Data and text

mining is the technique of extracting lexical features from textbased on theoretical models of deception. A second

approach is to use NLP techniques to classify documents.This approach uses the assumption that deceivers will usedifferent words in different combinations and ratios than non-deceivers. Machine learning algorithms are then employed onthe extracted set of lexical features to classify a document asdeceptive or non-deceptive.

If the communication is already in a text-based format, theassessment of credibility is a fast process requiring onlymoments to evaluate. Current research is investigatingtechniques of combining speech recognition tools and text-based credibility assessment to extend the applications toreal-time use for verbal-based screening scenarios.

The accuracy rate of linguistic analysis varies acrosscontexts and environments. However, the accuracy rate

seems to be consistently higher than the rate exhibited by

most people. It is important to note that linguistic analysismust be tuned for the specific context in which it is used.Using numerous classification methods, the featuresdescribed by Zhou et al. achieved accuracy rates between60-80% in both experimental and field studies [41]. TheAgent99 Analyzer belongs to a suite of automated deceptiondetection tools known as Agent99 [24] and has been used to

with real-world data to achieve accuracies of 72% [42]. Bondand Lee [27] used Linguistic Inquiry and Word Count (LIWC)to code the statements of prisoners achieving accuracy of69.1 percent using logistic regression. Zhou et al., [41]achieved a range of credibility accuracies between 72- 92% in384 computer-mediated messages depending on theclassification method used. Fuller et al. [43] report 72%accuracy in statements of interest in criminal Air Force

investigations. Typical rapid screening involves only a fewshort-answer questions. However, linguistic analysis reliesupon interactive exchanges and is most diagnostic whenexamining longer utterances. This limits the usefulness oflinguistic analysis where interactions are only a few seconds.However, where interactions are longer (visa permitapplications, border, secondary screening, etc.), linguisticanalysis can be an effective screening tool. Among significantconstraints posed by culture of the target person are that

linguistic analysis is vulnerable to artifacts in speech ofsubjects who speak English poorly or who must communicatethrough a translator.

TABLE IXAUTOMATED LINGUISTIC ANALYSIS


Validity Validity still being determined;

Initial results are promising

Shortcomings Requires detailed narratives for higher accuracy; Potential

artifacts from ESL subjects;Data must be in text

Consistent across cultures Low probability

Cost of implementation Low

Usefulness in rapid

screening

Yes

IV. EXPERIMENTAL METHOD

Data were collected via an experiment requiringparticipants to commit a mock crime and then be interviewedby a professional interviewer during which, or following which,the noninvasive, psycho-physiological measures detected andtracked physiology and behavior.

A. Experimental Design

The purpose of the study was to identify technologies thataccurately detect different cues exhibited during deceptivecommunication. Internal validity was of primary concern inthis experiment as the main intent was to show thatmanipulation of independent variables resulted in measurablephysiological or behavioral changes that accurately

distinguish truthtellers from deceivers. (see Figure 1).

Figure. 1: Relationships among Variables

This experiment incorporated a randomized two-groupdesign. Participants were randomly assigned to levels of theindependent variable. Internal validity was further supportedthrough verbal manipulation checks included to verify thatinstructions were understood and followed. Great care wastaken to limit and eliminate laboratory artifacts, by restrictinginteraction with laboratory technicians, creating motivation forsuccessful deception by offering substantial monetarybonuses to participants that were viewed as credible, and by

having the crime committed in a realistic fashion.The implemented experimental design facilitated the

comparison of potential technologies at accurately measuringthe dependent variables between and within levels of theindependent variable.

B. Sample

Participants were solicited from the community through

newspaper ads, flyers, and online publications. Participantsreceived monetary compensation for their time, and were ableto earn a bonus for successfully deceiving the interviewer.

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C. Procedures

Participants arrived at a suspicious-appearing location,where they received audio-recorded instructions to report toanother location. Participants who were randomly assigned to

the deception condition also received instructions to commit atheft at the next location. Both innocent and guilty parties were

then directed to a third location where they were interviewedby a professional examiner. To receive a substantial bonus,they had to convince the interviewer of their truthfulness andtheir innocence in conducting the crime. The procedures wereintended to heighten anxiety and simulate the circumstancessurrounding actual criminal conduct. The interview was video

recorded and consisted of a series of scripted questionsregarding the participants involvement in the theft. During theinterview, the instrumentation (discussed in Section III)measured the participants pupil dilation, preorbitaltemperature, cardio-respiratory activity, blink activity andstartle response. Following the interview, participants wereadministered an eye tracking exam used to measure their eyemovements while looking at familiar and unfamiliar images,

some of which consisted of items present at the crime scene,

and would have only been seen by those in the guiltycondition. Participants then completed a post-interviewsurvey and were debriefed.

V. CONCLUSION

Among technologies for rapid detection, the mostpromising are LDV, pupillometry, kinesic analysis, linguisticanalysis, and vocalic analysis. Although eye-tracking has onlymoderate application in rapid screening, it is alsorecommended for investigation as guilty knowledge screening

of suspects may be required with large numbers of peoplewho were present at a crime and in secondary screening.

Automated nonverbal and linguistic systems hold morepromise. Research suggests that there are linguistic andbehavioral differences that distinguish truths for lies, but thesecues are faint with large noise to signal ratios. Thus, humanunaided perception simply may not be sensitive enough. Ifthis is the case, automated systems may be better suited to

detection of faint signals.Research should focus on the active elicitation of

diagnostic information. Whereas research indicates thatdeception cannot be detected solely from behavioralobservation when deceivers are allowed to lie on their ownterms, relatively little research has focused on what can beactively done to thwart liars and facilitate leakage. Theresearch reviewed here that does exist on active interviewing

strategies is promising. To the extent that lies can bedetected without technological aid, effective interviewing

strategies hold the most promise. Therefore it isrecommended that programmatic behavioral research bedirected at investigating the utility of interviewing strategies.

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VII. APPENDIX

This work was supported by the Defense Academy for

Credibility Assessment under directions of theCounterintelligence Field Activity. The views, opinions, and/orfindings in this report are those of the authors and should notbe construed as an official U.S. Government position, policy,or decision.

VIII. VITA

Judee K. Burgoon is a professor of communication, aprofessor of family studies and human development, Directorof human communication research for the Center for theManagement of Information, and Site Director, Center for

Identification Technology Research at the University ofArizona. Her research interests are in deception, trust,interpersonal interaction, and new technologies. She receivedher doctorate in communication and educational psychologyfrom West Virginia University.

Douglas C. Derrick is a PhD student at the University ofArizona. His research interests include trust, leadership, andtext mining. He has masters degrees from Texas A&M

University and San Jose State University.Aaron C. Elkins is a PhD student at the University ofArizona. His primary research interests include data fusion,deception detection, and computer-mediated communication.

Sean LaMarc Humpherys is a third-year PhD student atthe University of Arizona, and conducts research into linguisticcredibility assessments. He has a masters degree fromBrigham Young University.

Matthew L. Jensen is an Assistant Professor in MIS at theUniversity Oklahoma. His primary interests are computer-aided decision making, human-computer interactions, anddecision-making under uncertainty. He received his PhD fromthe University of Arizona.

Christopher B.R. Diller is a doctoral candidate at theUniversity of Arizona. His primary research interests are

collaborative multimedia networks, economics of informatics,decision support systems, and operations management. He

has two masters degrees from the University of Florida.Jay F. Nunamaker Jr. is the Regents and Soldwedel

Professor of MIS, computer science, and communication andthe director of the Center for the Management of Informationat the University of Arizona, Tucson. He received his PhD insystems engineering and operations research from CaseInstitute of Technology.

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