Gaze-Based Detection of Thoughts across Naturalistic Tasks Using a PSO-Optimized Random Forest Algorithm
Abstract
:1. Introduction
2. Materials and Methods
2.1. Participants
2.2. Experimental Protocol
2.3. Thought Probes and Self-Assigned Task
2.4. Eye Tracking Data Acquisition and Preprocessing
2.5. Eye Tracking Features
2.6. Machine Learning Model
2.7. Particle Swarm Optimization
3. Results
3.1. Participant Self-Selected Tasks
3.2. Classification Performance Based on All Features
3.3. Classification Performance Based on Optimal Subset of Features
3.4. Comparison of Classification Performance
4. Discussion
4.1. Task-Relatedness and Internal–External Orientation
4.2. Freely Moving, Goal-Directedness, and Sticky Thoughts
4.3. Self- and Others-Oriented Thoughts
4.4. Visual and Auditory Modalities
4.5. Limitations, Future Directions and Implications
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- James, W. The Principles of Psychology; Henry Holt and Co.: New York, NY, US, 1890; Volume 1. [Google Scholar]
- Killingsworth, M.A.; Gilbert, D.T. A Wandering Mind Is an Unhappy Mind. Science 2010, 330, 932. [Google Scholar] [CrossRef] [PubMed]
- Mills, C.; Porter, A.R.; Andrews-Hanna, J.R.; Christoff, K.; Colby, A. How Task-Unrelated and Freely Moving Thought Relate to Affect: Evidence for Dissociable Patterns in Everyday Life. Emotion 2021, 21, 1029–1040. [Google Scholar] [CrossRef] [PubMed]
- Thiemann, R.F.; Mills, C.; Kam, J.W.Y. Differential Relationships between Thought Dimensions and Momentary Affect in Daily Life. Psychol. Res. 2023, 87, 1632–1643. [Google Scholar] [CrossRef] [PubMed]
- Andrews-Hanna, J.R.; Kaiser, R.H.; Turner, A.E.; Reineberg, A.; Godinez, D.; Dimidjian, S.; Banich, M. A Penny for Your Thoughts: Dimensions of Self-Generated Thought Content and Relationships with Individual Differences in Emotional Wellbeing. Front. Psychol. 2013, 4, 900. [Google Scholar] [CrossRef]
- Nyklíček, I.; Tinga, A.M.; Spapens, S. The Relation between Thinking and Mood in Daily Life: The Effects of Content and Context of Thought. Conscious. Cogn. 2021, 95, 103193. [Google Scholar] [CrossRef]
- Mulholland, B.; Goodall-Halliwell, I.; Wallace, R.; Chitiz, L.; Mckeown, B.; Rastan, A.; Poerio, G.L.; Leech, R.; Turnbull, A.; Klein, A.; et al. Patterns of Ongoing Thought in the Real World. Conscious. Cogn. 2023, 114, 103530. [Google Scholar] [CrossRef] [PubMed]
- Kam, J.W.Y.; Wong, A.Y.; Thiemann, R.F.; Hasan, F.; Andrews-Hanna, J.R.; Mills, C. On the Relationship between Unprompted Thought and Affective Well-Being: A Systematic Review and Meta-Analysis. Psychol. Bull. 2024, 150, 621–641. [Google Scholar] [CrossRef] [PubMed]
- Smallwood, J.; Turnbull, A.; Wang, H.; Ho, N.S.P.; Poerio, G.L.; Karapanagiotidis, T.; Konu, D.; Mckeown, B.; Zhang, M.; Murphy, C.; et al. The Neural Correlates of Ongoing Conscious Thought. iScience 2021, 24, 102132. [Google Scholar] [CrossRef] [PubMed]
- Smallwood, J.; Karapanagiotidis, T.; Ruby, F.; Medea, B.; de Caso, I.; Konishi, M.; Wang, H.-T.; Hallam, G.; Margulies, D.S.; Jefferies, E. Representing Representation: Integration between the Temporal Lobe and the Posterior Cingulate Influences the Content and Form of Spontaneous Thought. PLoS ONE 2016, 11, e0152272. [Google Scholar] [CrossRef]
- Turnbull, A.; Wang, H.T.; Murphy, C.; Ho, N.S.P.; Wang, X.; Sormaz, M.; Karapanagiotidis, T.; Leech, R.M.; Bernhardt, B.; Margulies, D.S.; et al. Left Dorsolateral Prefrontal Cortex Supports Context-Dependent Prioritisation of off-Task Thought. Nat. Commun. 2019, 10, 3816. [Google Scholar] [CrossRef]
- Wang, H.-T.; Poerio, G.; Murphy, C.; Bzdok, D.; Jefferies, E.; Smallwood, J. Dimensions of Experience: Exploring the Heterogeneity of the Wandering Mind. Psychol. Sci. 2018, 29, 56–71. [Google Scholar] [CrossRef]
- Mckeown, B.L.A. From Context to Connectomes: Understanding Differences in Ongoing Thought in the Laboratory and Daily Life. Ph.D. Thesis, University of York, York, UK, 2022. [Google Scholar]
- Ho, N.S.P.; Poerio, G.; Konu, D.; Turnbull, A.; Sormaz, M.; Leech, R.; Bernhardt, B.; Jefferies, E.; Smallwood, J. Facing up to the Wandering Mind: Patterns of off-Task Laboratory Thought Are Associated with Stronger Neural Recruitment of Right Fusiform Cortex While Processing Facial Stimuli. Neuroimage 2020, 214, 116765. [Google Scholar] [CrossRef] [PubMed]
- Konu, D.; Turnbull, A.; Karapanagiotidis, T.; Wang, H.-T.; Brown, L.R.; Jefferies, E.; Smallwood, J. A Role for the Ventromedial Prefrontal Cortex in Self-Generated Episodic Social Cognition. NeuroImage 2020, 218, 116977. [Google Scholar] [CrossRef] [PubMed]
- Simola, J.; Silander, T.; Harju, M.; Lahti, O.; Makkonen, E.; Pätsi, L.-M.; Smallwood, J. Context Independent Reductions in External Processing during Self-Generated Episodic Social Cognition. Cortex 2023, 159, 39–53. [Google Scholar] [CrossRef]
- Eckstein, M.K.; Guerra-Carrillo, B.; Miller Singley, A.T.; Bunge, S.A. Beyond Eye Gaze: What Else Can Eyetracking Reveal about Cognition and Cognitive Development? Dev. Cogn. Neurosci. 2017, 25, 69–91. [Google Scholar] [CrossRef]
- Hannula, D.E.; Althoff, R.R.; Warren, D.E.; Riggs, L.; Cohen, N.J.; Ryan, J.D. Worth a Glance: Using Eye Movements to Investigate the Cognitive Neuroscience of Memory. Front. Hum. Neurosci. 2010, 4, 166. [Google Scholar] [CrossRef]
- Skaramagkas, V.; Giannakakis, G.; Ktistakis, E.; Manousos, D.; Karatzanis, I.; Tachos, N.S.; Tripoliti, E.; Marias, K.; Fotiadis, D.I.; Tsiknakis, M. Review of Eye Tracking Metrics Involved in Emotional and Cognitive Processes. IEEE Rev. Biomed. Eng. 2021, 16, 260–277. [Google Scholar] [CrossRef] [PubMed]
- Spering, M. Eye Movements as a Window into Decision-Making. Annu. Rev. Vision. Sci. 2022, 8, 427–448. [Google Scholar] [CrossRef]
- Uzzaman, S.; Joordens, S. The Eyes Know What You Are Thinking: Eye Movements as an Objective Measure of Mind Wandering. Conscious. Cogn. 2011, 20, 1882–1886. [Google Scholar] [CrossRef]
- Walcher, S.; Körner, C.; Benedek, M. Looking for Ideas: Eye Behavior during Goal-Directed Internally Focused Cognition. Conscious. Cogn. 2017, 53, 165–175. [Google Scholar] [CrossRef]
- Benedek, M.; Stoiser, R.; Walcher, S.; Körner, C. Eye Behavior Associated with Internally versus Externally Directed Cognition. Front. Psychol. 2017, 8, 1092. [Google Scholar] [CrossRef]
- Bixler, R.; D’Mello, S. Automatic Gaze-Based User-Independent Detection of Mind Wandering during Computerized Reading. User Model. User Adap Inter. 2016, 26, 33–68. [Google Scholar] [CrossRef]
- Faber, M.; Bixler, R.; D’Mello, S.K. An Automated Behavioral Measure of Mind Wandering during Computerized Reading. Behav. Res. 2018, 50, 134–150. [Google Scholar] [CrossRef]
- Faber, M.; Krasich, K.; Bixler, R.E.; Brockmole, J.R.; D’Mello, S.K. The Eye-Mind Wandering Link: Identifying Gaze Indices of Mind Wandering across Tasks. J. Exp. Psychol. Hum. Percept. Perform. 2020, 46, 1201–1221. [Google Scholar] [CrossRef] [PubMed]
- Kuvar, V.; Kam, J.W.Y.; Hutt, S.; Mills, C. Detecting When the Mind Wanders Off Task in Real-Time: An Overview and Systematic Review. In Proceedings of the 25th International Conference on Multimodal Interaction, Paris, France, 9–13 October 2023; pp. 163–173. [Google Scholar]
- Seli, P.; Konishi, M.; Risko, E.F.; Smilek, D. The Role of Task Difficulty in Theoretical Accounts of Mind Wandering. Conscious. Cogn. 2018, 65, 255–262. [Google Scholar] [CrossRef]
- D’Mello, S.; Kopp, K.; Bixler, R.E.; Bosch, N. Attending to Attention: Detecting and Combating Mind Wandering during Computerized Reading. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, San Jose, CA, USA, 7–12 May 2016; pp. 1661–1669. [Google Scholar]
- Gwizdka, J. Exploring Eye-Tracking Data for Detection of Mind-Wandering on Web Tasks. In Lecture Notes in Information Systems and Organization; Springer: Cham, Switzerland, 2019; Volume 32, pp. 47–55. [Google Scholar]
- Hutt, S.; Hardey, J.; Bixler, R.; Stewart, A.; Risko, E.; D’Mello, S.K. Gaze-Based Detection of Mind Wandering during Lecture Viewing. In Proceedings of the 10th International Conference on Educational Data Mining, Wuhan, China, 25–28 June 2017; pp. 226–231. [Google Scholar]
- Mills, C.; Bixler, R.; Wang, X.; D’Mello, S.K. Automatic Gaze-Based Detection of Mind Wandering during Narrative Film Comprehension. In Proceedings of the 9th International Conference on Educational Data Mining, Raleigh, NC, USA, 29 June–2 July 2016; pp. 30–37. [Google Scholar]
- Mills, C.; Gregg, J.; Bixler, R.; D’Mello, S.K. Eye-Mind Reader: An Intelligent Reading Interface That Promotes Long-Term Comprehension by Detecting and Responding to Mind Wandering. Hum. Comput. Interact. 2021, 36, 306–332. [Google Scholar] [CrossRef]
- Brishtel, I.; Khan, A.A.; Schmidt, T.; Dingler, T.; Ishimaru, S.; Dengel, A. Mind Wandering in a Multimodal Reading Setting: Behavior Analysis & Automatic Detection Using Eye-Tracking and an EDA Sensor. Sensors 2020, 20, 2546. [Google Scholar] [CrossRef] [PubMed]
- Huang, M.X.; Li, J.; Ngai, G.; Leong, H.V.; Bulling, A. Moment-to-Moment Detection of Internal Thought during Video Viewing from Eye Vergence Behavior. In Proceedings of the 27th ACM International Conference on Multimedia, Nice, France, 21–25 October 2019; pp. 2254–2262. [Google Scholar]
- Chen, K.; Xue, B.; Zhang, M.; Zhou, F. Correlation-Guided Updating Strategy for Feature Selection in Classification with Surrogate-Assisted Particle Swarm Optimization. IEEE Trans. Evol. Comput. 2022, 26, 1015–1029. [Google Scholar] [CrossRef]
- Chen, T.-C.; Alizadeh, S.M.; Albahar, M.A.; Thanoon, M.; Alammari, A.; Guerrero, J.W.G.; Nazemi, E.; Eftekhari-Zadeh, E. Introducing the Effective Features Using the Particle Swarm Optimization Algorithm to Increase Accuracy in Determining the Volume Percentages of Three-Phase Flows. Processes 2023, 11, 236. [Google Scholar] [CrossRef]
- Kamble, A.; Ghare, P.H.; Kumar, V. Optimized Rational Dilation Wavelet Transform for Automatic Imagined Speech Recognition. IEEE Trans. Instrum. Meas. 2023, 72, 4002210. [Google Scholar] [CrossRef]
- Bedolla-Ibarra, M.G.; Cabrera-Hernandez, M.d.C.; Aceves-Fernández, M.A.; Tovar-Arriaga, S. Classification of Attention Levels Using a Random Forest Algorithm Optimized with Particle Swarm Optimization. Evol. Syst. 2022, 13, 687–702. [Google Scholar] [CrossRef]
- The MathWorks Inc. MATLAB, 9.10.0 (R2021a); The MathWorks Inc.: Natick, MA, USA, 2021.
- Kam, J.W.Y.; Rahnuma, T.; Park, Y.E.; Hart, C.M. Electrophysiological Markers of Mind Wandering: A Systematic Review. NeuroImage 2022, 258, 119372. [Google Scholar] [CrossRef]
- O’Connell, R.G.; Dockree, P.M.; Robertson, I.H.; Bellgrove, M.A.; Foxe, J.J.; Kelly, S.P. Uncovering the Neural Signature of Lapsing Attention: Electrophysiological Signals Predict Errors up to 20 s before They Occur. J. Neurosci. 2009, 29, 8604–8611. [Google Scholar] [CrossRef]
- Olsen, A. The Tobii I-VT Fixation Filter. 2012. Available online: http://www.vinis.co.kr/ivt_filter.pdf (accessed on 22 July 2024).
- Van Beers, R.J. The Sources of Variability in Saccadic Eye Movements. J. Neurosci. 2007, 27, 8757–8770. [Google Scholar] [CrossRef] [PubMed]
- Hollander, J.; Huette, S. Extracting Blinks from Continuous Eye-Tracking Data in a Mind Wandering Paradigm. Conscious. Cogn. 2022, 100, 103303. [Google Scholar] [CrossRef] [PubMed]
- Zhang, H.; Miller, K.F.; Sun, X.; Cortina, K.S. Wandering Eyes: Eye Movements during Mind Wandering in Video Lectures. Appl. Cogn. Psychol. 2020, 34, 449–464. [Google Scholar] [CrossRef]
- Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef]
- Zhou, J.; Wang, G.; Liu, J.; Wu, D.; Xu, W.; Wang, Z.; Ye, J.; Xia, M.; Hu, Y.; Tian, Y. Automatic Sleep Stage Classification with Single Channel EEG Signal Based on Two-Layer Stacked Ensemble Model. IEEE Access 2020, 8, 57283–57297. [Google Scholar] [CrossRef]
- Chen, Y.-T.; Lee, H.-H.; Shih, C.-Y.; Chen, Z.-L.; Beh, W.-K.; Yeh, S.-L.; Wu, A.-Y. An Effective Entropy-Assisted Mind-Wandering Detection System Using EEG Signals of MM-SART Database. IEEE J. Biomed. Health Inform. 2022, 26, 3649–3660. [Google Scholar] [CrossRef]
- Chicco, D.; Tötsch, N.; Jurman, G. The Matthews Correlation Coefficient (MCC) Is More Reliable than Balanced Accuracy, Bookmaker Informedness, and Markedness in Two-Class Confusion Matrix Evaluation. BioData Min. 2021, 14, 13. [Google Scholar] [CrossRef]
- Dong, H.W.; Mills, C.; Knight, R.T.; Kam, J.W.Y. Detection of Mind Wandering Using EEG: Within and across Individuals. PLoS ONE 2021, 16, e0251490. [Google Scholar] [CrossRef]
- Kennedy, J.; Eberhart, R. Particle Swarm Optimization. In Proceedings of the ICNN’95—International Conference on Neural Networks, Perth, WA, Australia, 27 November–1 December 1995; Volume 4, pp. 1942–1948. [Google Scholar]
- George, S.T.; Subathra, M.S.P.; Sairamya, N.J.; Susmitha, L.; Joel Premkumar, M. Classification of Epileptic EEG Signals Using PSO Based Artificial Neural Network and Tunable-Q Wavelet Transform. Biocybern. Biomed. Eng. 2020, 40, 709–728. [Google Scholar] [CrossRef]
- The MathWorks Inc. MATLAB, 9.11.0 (R2021b); The MathWorks Inc.: Natick, MA, USA, 2021.
- Kazerani, R. Improving Breast Cancer Diagnosis Accuracy by Particle Swarm Optimization Feature Selection. Int. J. Comput. Intell. Syst. 2024, 17, 44. [Google Scholar] [CrossRef]
- Jang, D.; Yang, I.; Kim, S. Detecting Mind-Wandering from Eye Movement and Oculomotor Data during Learning Video Lecture. Educ. Sci. 2020, 10, 51. [Google Scholar] [CrossRef]
- Walcher, S.; Korda, Ž.; Körner, C.; Benedek, M. The Effects of Type and Workload of Internal Tasks on Voluntary Saccades in a Target-Distractor Saccade Task. PLoS ONE 2023, 18, e0290322. [Google Scholar] [CrossRef] [PubMed]
- Ceh, S.M.; Annerer-Walcher, S.; Körner, C.; Rominger, C.; Kober, S.E.; Fink, A.; Benedek, M. Neurophysiological Indicators of Internal Attention: An Electroencephalography–Eye-tracking Coregistration Study. Brain Behav. 2020, 10, e01790. [Google Scholar] [CrossRef]
- Stawarczyk, D.; Majerus, S.; Maquet, P.; D’Argembeau, A. Neural Correlates of Ongoing Conscious Experience: Both Task-Unrelatedness and Stimulus-Independence Are Related to Default Network Activity. PLoS ONE 2011, 6, e16997. [Google Scholar] [CrossRef]
- Christoff, K.; Irving, Z.C.; Fox, K.C.R.; Spreng, R.N.; Andrews-Hanna, J.R. Mind-Wandering as Spontaneous Thought: A Dynamic Framework. Nat. Rev. Neurosci. 2016, 17, 718–731. [Google Scholar] [CrossRef]
- Fourtassi, M.; Rode, G.; Pisella, L. Using Eye Movements to Explore Mental Representations of Space. Ann. Phys. Rehabil. Med. 2017, 60, 160–163. [Google Scholar] [CrossRef]
- Sulfaro, A.A.; Robinson, A.K.; Carlson, T.A. Properties of Imagined Experience across Visual, Auditory, and Other Sensory Modalities. Conscious. Cogn. 2024, 117, 103598. [Google Scholar] [CrossRef]
- Hung, S.-M.; Hsieh, P.-J. Mind Wandering in Sensory Cortices. Neuroimage Rep. 2022, 2, 100073. [Google Scholar] [CrossRef]
- Hutt, S.; Krasich, K.; Mills, C.; Bosch, N.; White, S.; Brockmole, J.R.; D’Mello, S.K. Automated Gaze-Based Mind Wandering Detection during Computerized Learning in Classrooms. User Model User Adap. Inter. 2019, 29, 821–867. [Google Scholar] [CrossRef]
- Kucyi, A.; Kam, J.W.Y.; Andrews-Hanna, J.R.; Christoff, K.; Whitfield-Gabrieli, S. Recent Advances in the Neuroscience of Spontaneous and Off-Task Thought: Implications for Mental Health. Nat. Ment. Health 2023, 1, 827–840. [Google Scholar] [CrossRef] [PubMed]
Thought Dimensions | Number of Participants Considered for Classification |
---|---|
On-Task vs. Off-Task | 5 |
Internal vs. External | 5 |
Freely Moving vs. Not Freely Moving | 5 |
Goal-Directed vs. Not Goal-Directed | 6 |
Sticky vs. Not Sticky | 4 |
Self-Oriented vs. Not Self-Oriented | 4 |
Others-Oriented vs. Not Others-Oriented | 6 |
Visual vs. Not Visual | 6 |
Auditory vs. Not Auditory | 6 |
Eye Tracking and Statistical Features | |
---|---|
fixation duration | count, mean, median, minimum, maximum, range, standard deviation, root mean squared deviation (RMSD) |
saccade duration | count, mean, median, minimum, maximum, range, standard deviation |
saccade amplitude | mean, median, minimum, maximum, range, standard deviation |
saccade velocity | mean, median, minimum, maximum, range, standard deviation |
fixation–saccade ratio | count |
horizontal saccade | count |
Thought Dimensions | MCC | AUC | BA | kappa | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Mean | Max | Min | Mean | Max | Min | Mean | Max | Min | Mean | Max | Min | |
On-Task vs. Off-Task | 0.17 | 0.25 | 0.13 | 0.65 | 0.68 | 0.63 | 0.58 | 0.61 | 0.57 | 0.17 | 0.24 | 0.13 |
Internal vs. External Orientation | 0.34 | 0.39 | 0.29 | 0.73 | 0.75 | 0.70 | 0.67 | 0.70 | 0.64 | 0.33 | 0.39 | 0.28 |
Freely vs. Not Freely Moving | 0.13 | 0.18 | 0.06 | 0.60 | 0.63 | 0.56 | 0.56 | 0.59 | 0.53 | 0.12 | 0.18 | 0.06 |
Goal- vs. Not Goal-Directed | 0.27 | 0.33 | 0.22 | 0.70 | 0.73 | 0.67 | 0.63 | 0.65 | 0.60 | 0.26 | 0.32 | 0.20 |
Sticky vs. Not Sticky | 0.22 | 0.27 | 0.16 | 0.65 | 0.68 | 0.61 | 0.60 | 0.62 | 0.58 | 0.21 | 0.26 | 0.16 |
Self- vs. Not Self-Oriented | 0.33 | 0.37 | 0.28 | 0.73 | 0.76 | 0.69 | 0.66 | 0.68 | 0.63 | 0.32 | 0.36 | 0.27 |
Others- vs. Not Others-Oriented | 0.42 | 0.45 | 0.35 | 0.79 | 0.81 | 0.76 | 0.70 | 0.72 | 0.67 | 0.41 | 0.44 | 0.34 |
Visual vs. Not Visual | 0.29 | 0.34 | 0.24 | 0.71 | 0.74 | 0.68 | 0.64 | 0.67 | 0.61 | 0.28 | 0.34 | 0.23 |
Auditory vs. Not Auditory | 0.21 | 0.27 | 0.14 | 0.70 | 0.74 | 0.63 | 0.60 | 0.63 | 0.58 | 0.21 | 0.27 | 0.14 |
Thought Dimensions | Optimal Features |
---|---|
On-Task vs. Off-Task | fixation (count, mean, median, min, RMSD); saccade (min, std), velocity (max, range); fixation–saccade ratio |
Internal vs. External Orientation | fixation (median, max, range, std); saccade (median, min); amplitude (std); velocity (median, max); horizontal saccades |
Freely vs. Not Freely Moving | fixation (count, mean, min, max, std); saccade (min, std); amplitude (min, std); velocity (min) |
Goal- vs. Not Goal-Directed | fixation (min, max, RMSD); saccade (count, min, range); velocity (median, min, std); horizontal saccades |
Sticky vs. Not Sticky | fixation (count, min, std); saccade (median); amplitude (median, min, range); velocity (min, max, std) |
Self- vs. Not Self-Oriented | fixation (min, range); saccade (min, max, range, std); amplitude (median); velocity (mean, range); fixation–saccade ratio |
Others- vs. Not Others-Oriented | fixation (min, range); saccade (count, median, min); amplitude (mean, std); velocity (mean, min); fixation–saccade ratio |
Visual vs. Not Visual | fixation (min, max, std); saccade (min, max, std); amplitude (min, std); velocity (mean); horizontal saccades |
Auditory vs. Not Auditory | fixation (count, mean, median, min); saccade (count, min); amplitude (median, max); velocity (mean); horizontal saccades |
Thought Dimensions | MCC | AUC | BA | kappa | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Mean | Max | Min | Mean | Max | Min | Mean | Max | Min | Mean | Max | Min | |
On-Task vs. Off-Task | 0.23 | 0.29 | 0.17 | 0.68 | 0.71 | 0.64 | 0.61 | 0.64 | 0.58 | 0.23 | 0.28 | 0.17 |
Internal vs. External Orientation | 0.36 | 0.41 | 0.31 | 0.72 | 0.75 | 0.70 | 0.68 | 0.71 | 0.65 | 0.35 | 0.40 | 0.31 |
Freely vs. Not Freely Moving | 0.17 | 0.25 | 0.11 | 0.62 | 0.66 | 0.59 | 0.58 | 0.62 | 0.56 | 0.17 | 0.24 | 0.11 |
Goal- vs. Not Goal-Directed | 0.31 | 0.36 | 0.26 | 0.71 | 0.74 | 0.68 | 0.65 | 0.68 | 0.62 | 0.30 | 0.36 | 0.25 |
Sticky vs. Not Sticky | 0.25 | 0.33 | 0.18 | 0.65 | 0.68 | 0.61 | 0.62 | 0.66 | 0.58 | 0.24 | 0.32 | 0.17 |
Self- vs. Not Self-Oriented | 0.36 | 0.42 | 0.31 | 0.73 | 0.77 | 0.69 | 0.68 | 0.71 | 0.65 | 0.36 | 0.41 | 0.30 |
Others- vs. Not Others-Oriented | 0.47 | 0.54 | 0.41 | 0.80 | 0.82 | 0.77 | 0.73 | 0.77 | 0.70 | 0.46 | 0.54 | 0.41 |
Visual vs. Not Visual | 0.30 | 0.37 | 0.24 | 0.73 | 0.75 | 0.69 | 0.65 | 0.69 | 0.62 | 0.29 | 0.36 | 0.24 |
Auditory vs. Not Auditory | 0.33 | 0.44 | 0.23 | 0.75 | 0.79 | 0.72 | 0.66 | 0.72 | 0.61 | 0.32 | 0.43 | 0.23 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Rahnuma, T.; Jothiraj, S.N.; Kuvar, V.; Faber, M.; Knight, R.T.; Kam, J.W.Y. Gaze-Based Detection of Thoughts across Naturalistic Tasks Using a PSO-Optimized Random Forest Algorithm. Bioengineering 2024, 11, 760. https://doi.org/10.3390/bioengineering11080760
Rahnuma T, Jothiraj SN, Kuvar V, Faber M, Knight RT, Kam JWY. Gaze-Based Detection of Thoughts across Naturalistic Tasks Using a PSO-Optimized Random Forest Algorithm. Bioengineering. 2024; 11(8):760. https://doi.org/10.3390/bioengineering11080760
Chicago/Turabian StyleRahnuma, Tarannum, Sairamya Nanjappan Jothiraj, Vishal Kuvar, Myrthe Faber, Robert T. Knight, and Julia W. Y. Kam. 2024. "Gaze-Based Detection of Thoughts across Naturalistic Tasks Using a PSO-Optimized Random Forest Algorithm" Bioengineering 11, no. 8: 760. https://doi.org/10.3390/bioengineering11080760
APA StyleRahnuma, T., Jothiraj, S. N., Kuvar, V., Faber, M., Knight, R. T., & Kam, J. W. Y. (2024). Gaze-Based Detection of Thoughts across Naturalistic Tasks Using a PSO-Optimized Random Forest Algorithm. Bioengineering, 11(8), 760. https://doi.org/10.3390/bioengineering11080760