Quantifying the Predictability of Visual Scanpaths Using Active Information Storage
Entropy-based measures are an important tool for studying human gaze behavior under various conditions. In particular, gaze transition entropy (GTE) is a popular method to quantify the predictability of a visual scanpath as the entropy of transitions between fixations and has been shown to correlate...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/2/167 |
| _version_ | 1797405893831163904 |
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| author | Patricia Wollstadt Martina Hasenjäger Christiane B. Wiebel-Herboth |
| author_facet | Patricia Wollstadt Martina Hasenjäger Christiane B. Wiebel-Herboth |
| author_sort | Patricia Wollstadt |
| collection | DOAJ |
| description | Entropy-based measures are an important tool for studying human gaze behavior under various conditions. In particular, gaze transition entropy (GTE) is a popular method to quantify the predictability of a visual scanpath as the entropy of transitions between fixations and has been shown to correlate with changes in task demand or changes in observer state. Measuring scanpath predictability is thus a promising approach to identifying viewers’ cognitive states in behavioral experiments or gaze-based applications. However, GTE does not account for temporal dependencies beyond two consecutive fixations and may thus underestimate the actual predictability of the current fixation given past gaze behavior. Instead, we propose to quantify scanpath predictability by estimating the active information storage (AIS), which can account for dependencies spanning multiple fixations. AIS is calculated as the mutual information between a processes’ multivariate past state and its next value. It is thus able to measure how much information a sequence of past fixations provides about the next fixation, hence covering a longer temporal horizon. Applying the proposed approach, we were able to distinguish between induced observer states based on estimated AIS, providing first evidence that AIS may be used in the inference of user states to improve human–machine interaction. |
| first_indexed | 2024-03-09T03:17:48Z |
| format | Article |
| id | doaj.art-475ef366911247559f0fcc1f448279e4 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T03:17:48Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-475ef366911247559f0fcc1f448279e42023-12-03T15:16:33ZengMDPI AGEntropy1099-43002021-01-0123216710.3390/e23020167Quantifying the Predictability of Visual Scanpaths Using Active Information StoragePatricia Wollstadt0Martina Hasenjäger1Christiane B. Wiebel-Herboth2Honda Research Insitute Europe GmbH, Carl-Legien-Str. 30, 63073 Offenbach/Main, GermanyHonda Research Insitute Europe GmbH, Carl-Legien-Str. 30, 63073 Offenbach/Main, GermanyHonda Research Insitute Europe GmbH, Carl-Legien-Str. 30, 63073 Offenbach/Main, GermanyEntropy-based measures are an important tool for studying human gaze behavior under various conditions. In particular, gaze transition entropy (GTE) is a popular method to quantify the predictability of a visual scanpath as the entropy of transitions between fixations and has been shown to correlate with changes in task demand or changes in observer state. Measuring scanpath predictability is thus a promising approach to identifying viewers’ cognitive states in behavioral experiments or gaze-based applications. However, GTE does not account for temporal dependencies beyond two consecutive fixations and may thus underestimate the actual predictability of the current fixation given past gaze behavior. Instead, we propose to quantify scanpath predictability by estimating the active information storage (AIS), which can account for dependencies spanning multiple fixations. AIS is calculated as the mutual information between a processes’ multivariate past state and its next value. It is thus able to measure how much information a sequence of past fixations provides about the next fixation, hence covering a longer temporal horizon. Applying the proposed approach, we were able to distinguish between induced observer states based on estimated AIS, providing first evidence that AIS may be used in the inference of user states to improve human–machine interaction.https://www.mdpi.com/1099-4300/23/2/167eye trackinginformation theoryactive information storagescanpath |
| spellingShingle | Patricia Wollstadt Martina Hasenjäger Christiane B. Wiebel-Herboth Quantifying the Predictability of Visual Scanpaths Using Active Information Storage Entropy eye tracking information theory active information storage scanpath |
| title | Quantifying the Predictability of Visual Scanpaths Using Active Information Storage |
| title_full | Quantifying the Predictability of Visual Scanpaths Using Active Information Storage |
| title_fullStr | Quantifying the Predictability of Visual Scanpaths Using Active Information Storage |
| title_full_unstemmed | Quantifying the Predictability of Visual Scanpaths Using Active Information Storage |
| title_short | Quantifying the Predictability of Visual Scanpaths Using Active Information Storage |
| title_sort | quantifying the predictability of visual scanpaths using active information storage |
| topic | eye tracking information theory active information storage scanpath |
| url | https://www.mdpi.com/1099-4300/23/2/167 |
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