AnswerTruthDetector: a combined cognitive load approach for separating truthful from deceptive answers in computer-administered questionnaires
In human-computer interaction, much empirical research exists. Online questionnaires increasingly play an important role. Here the quality of the results depend strongly on the quality of the given answers, and it is essential to distinguish truthful from deceptive answers. There exist elegant singl...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2023-11-01
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| Series: | i-com |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/icom-2023-0023 |
| _version_ | 1797387330687860736 |
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| author | Maleck Moritz Gross Tom |
| author_facet | Maleck Moritz Gross Tom |
| author_sort | Maleck Moritz |
| collection | DOAJ |
| description | In human-computer interaction, much empirical research exists. Online questionnaires increasingly play an important role. Here the quality of the results depend strongly on the quality of the given answers, and it is essential to distinguish truthful from deceptive answers. There exist elegant single modalities for deception detection in the literature, such as mouse tracking and eye tracking (in this paper, respectively, measuring the pupil diameter). Yet, no combination of these two modalities is available. This paper presents a combined approach of two cognitive-load-based lie detection approaches. We address study administrators who conduct questionnaires in the HCI, wanting to improve the validity of questionnaires. |
| first_indexed | 2024-03-08T22:22:39Z |
| format | Article |
| id | doaj.art-7ba2b3e1803e4835ba09a10dcce00700 |
| institution | Directory Open Access Journal |
| issn | 2196-6826 |
| language | English |
| last_indexed | 2024-03-08T22:22:39Z |
| publishDate | 2023-11-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | i-com |
| spelling | doaj.art-7ba2b3e1803e4835ba09a10dcce007002023-12-18T12:43:22ZengDe Gruyteri-com2196-68262023-11-0122324125110.1515/icom-2023-0023AnswerTruthDetector: a combined cognitive load approach for separating truthful from deceptive answers in computer-administered questionnairesMaleck Moritz0Gross Tom1Human-Computer Interaction Group, University of Bamberg, Bamberg, GermanyHuman-Computer Interaction Group, University of Bamberg, Bamberg, GermanyIn human-computer interaction, much empirical research exists. Online questionnaires increasingly play an important role. Here the quality of the results depend strongly on the quality of the given answers, and it is essential to distinguish truthful from deceptive answers. There exist elegant single modalities for deception detection in the literature, such as mouse tracking and eye tracking (in this paper, respectively, measuring the pupil diameter). Yet, no combination of these two modalities is available. This paper presents a combined approach of two cognitive-load-based lie detection approaches. We address study administrators who conduct questionnaires in the HCI, wanting to improve the validity of questionnaires.https://doi.org/10.1515/icom-2023-0023truth detectionlie detectionquestionnaire validationeye trackingmouse movementscognitive-load-based deception detection |
| spellingShingle | Maleck Moritz Gross Tom AnswerTruthDetector: a combined cognitive load approach for separating truthful from deceptive answers in computer-administered questionnaires i-com truth detection lie detection questionnaire validation eye tracking mouse movements cognitive-load-based deception detection |
| title | AnswerTruthDetector: a combined cognitive load approach for separating truthful from deceptive answers in computer-administered questionnaires |
| title_full | AnswerTruthDetector: a combined cognitive load approach for separating truthful from deceptive answers in computer-administered questionnaires |
| title_fullStr | AnswerTruthDetector: a combined cognitive load approach for separating truthful from deceptive answers in computer-administered questionnaires |
| title_full_unstemmed | AnswerTruthDetector: a combined cognitive load approach for separating truthful from deceptive answers in computer-administered questionnaires |
| title_short | AnswerTruthDetector: a combined cognitive load approach for separating truthful from deceptive answers in computer-administered questionnaires |
| title_sort | answertruthdetector a combined cognitive load approach for separating truthful from deceptive answers in computer administered questionnaires |
| topic | truth detection lie detection questionnaire validation eye tracking mouse movements cognitive-load-based deception detection |
| url | https://doi.org/10.1515/icom-2023-0023 |
| work_keys_str_mv | AT maleckmoritz answertruthdetectoracombinedcognitiveloadapproachforseparatingtruthfulfromdeceptiveanswersincomputeradministeredquestionnaires AT grosstom answertruthdetectoracombinedcognitiveloadapproachforseparatingtruthfulfromdeceptiveanswersincomputeradministeredquestionnaires |