Computer methods and programs in biomedicine · 2026
Pre-stimulus oculomotor signatures of deception in the Concealed Information Test.
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Celniak Weronika, Słapczyńska Dominika, Augustyniak Piotr
Аннотация
BACKGROUND AND OBJECTIVE: The detection of concealed knowledge related to a crime in forensic settings is usually performed using a traditional polygraph that records physiological responses to specific stimuli. While these reactive measures are standard, there is growing interest in non-contact behavioral markers that can identify deceptive intent. This study aimed to evaluate the efficacy of anticipatory eye-movement patterns and machine learning classifiers in discriminating between guilty and control participants during the pre-stimulus phase of the examination, identifying cognitive markers that emerge before visual stimuli are even presented.
METHODS: To investigate the effectiveness of eye-tracking measurements combined with machine learning in Concealed Information Test, we examined whether anticipatory eye-movement patterns can reliably distinguish between guilty and control participants. At this phase of examination, participants are not yet presented with visual stimuli. We identified features that could reveal subtle differences in cognitive load, arousal, and gaze control associated with the intent to deceive in a series of statistical tests. In our study, several oculomotor features, particularly scanpath-related measures and pupil diameter, were extracted to capture visual exploration patterns and attentional allocation during the anticipation phase of Concealed Information Test.
RESULTS: The support vector machine classifier achieved a mean participant-level accuracy of 0.729, which was significantly higher than chance.
CONCLUSIONS: These results suggest that anticipatory ocular behavior in CIT may provide valuable insights into cognitive processes associated with anticipation of visual test stimuli even before they are presented.
METHODS: To investigate the effectiveness of eye-tracking measurements combined with machine learning in Concealed Information Test, we examined whether anticipatory eye-movement patterns can reliably distinguish between guilty and control participants. At this phase of examination, participants are not yet presented with visual stimuli. We identified features that could reveal subtle differences in cognitive load, arousal, and gaze control associated with the intent to deceive in a series of statistical tests. In our study, several oculomotor features, particularly scanpath-related measures and pupil diameter, were extracted to capture visual exploration patterns and attentional allocation during the anticipation phase of Concealed Information Test.
RESULTS: The support vector machine classifier achieved a mean participant-level accuracy of 0.729, which was significantly higher than chance.
CONCLUSIONS: These results suggest that anticipatory ocular behavior in CIT may provide valuable insights into cognitive processes associated with anticipation of visual test stimuli even before they are presented.