| Autor: |
Rahnuma T; Department of Psychology, University of Calgary, Calgary, AB T2N 1N4, Canada.; Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada., Jothiraj SN; Department of Psychology, University of Calgary, Calgary, AB T2N 1N4, Canada.; Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada., Kuvar V; Department of Educational Psychology, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA., Faber M; Department of Cognitive Science and Artificial Intelligence, Tilburg University, 5037 AB Tilburg, The Netherlands.; Donders Centre for Cognitive Neuroimaging, Radboud University, 6525 EN Nijmegen, The Netherlands., Knight RT; Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, USA.; Department of Psychology, University of California, Berkeley, CA 94704, USA., Kam JWY; Department of Psychology, University of Calgary, Calgary, AB T2N 1N4, Canada.; Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada. |
| Abstrakt: |
One key aspect of the human experience is our ongoing stream of thoughts. These thoughts can be broadly categorized into various dimensions, which are associated with different impacts on mood, well-being, and productivity. While the past literature has often identified eye movements associated with a specific thought dimension (task-relatedness) during experimental tasks, few studies have determined if these various thought dimensions can be classified by oculomotor activity during naturalistic tasks. Employing thought sampling, eye tracking, and machine learning, we assessed the classification of nine thought dimensions (task-relatedness, freely moving, stickiness, goal-directedness, internal-external orientation, self-orientation, others orientation, visual modality, and auditory modality) across seven multi-day recordings of seven participants during self-selected computer tasks. Our analyses were based on a total of 1715 thought probes across 63 h of recordings. Automated binary-class classification of the thought dimensions was based on statistical features extracted from eye movement measures, including fixation and saccades. These features all served as input into a random forest (RF) classifier, which was then improved with particle swarm optimization (PSO)-based selection of the best subset of features for classifier performance. The mean Matthews correlation coefficient (MCC) values from the PSO-based RF classifier across the thought dimensions ranged from 0.25 to 0.54, indicating above-chance level performance in all nine thought dimensions across participants and improved performance compared to the RF classifier without feature selection. Our findings highlight the potential of machine learning approaches combined with eye movement measures for the real-time prediction of naturalistic ongoing thoughts, particularly in ecologically valid contexts. |
