Analysis of goal, feedback and rewards on sustained attention via machine learning
IntroductionSustaining attention is a notoriously difficult task as shown in a recent experiment where reaction times (RTs) and pupillometry data were recorded from 350 subjects in a 30-min vigilance task. Subjects were also presented with different types of goal, feedback, and reward.MethodsIn this...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Behavioral Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbeh.2024.1386723/full |
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| author | Nethali Fernando Nethali Fernando Matthew Robison Matthew Robison Pedro D. Maia Pedro D. Maia |
| author_facet | Nethali Fernando Nethali Fernando Matthew Robison Matthew Robison Pedro D. Maia Pedro D. Maia |
| author_sort | Nethali Fernando |
| collection | DOAJ |
| description | IntroductionSustaining attention is a notoriously difficult task as shown in a recent experiment where reaction times (RTs) and pupillometry data were recorded from 350 subjects in a 30-min vigilance task. Subjects were also presented with different types of goal, feedback, and reward.MethodsIn this study, we revisit this experimental data and solve three families of machine learning problems: (i) RT-regression problems, to predict subjects' RTs using all available data, (ii) RT-classification problems, to classify responses more broadly as attentive, semi-attentive, and inattentive, and (iii) to predict the subjects' experimental conditions from physiological data.ResultsAfter establishing that regressing RTs is in general a difficult task, we achieve better results classifying them in broader categories. We also successfully disambiguate subjects who received goals and rewards from those who did not. Finally, we quantify changes in accuracy when coarser features (averaged throughout multiple trials) are used. Interestingly, the machine learning pipeline selects different features depending on their resolution, suggesting that predictive physiological features are also resolution-specific.DiscussionThese findings highlight the potential of machine learning to advance research on sustained attention and behavior, particularly in studies incorporating pupillometry or other physiological measurements, offering new avenues for understanding and analysis. |
| format | Article |
| id | doaj-art-f3c67faedf71493bbacdbb6ffd781372 |
| institution | OA Journals |
| issn | 1662-5153 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Behavioral Neuroscience |
| spelling | doaj-art-f3c67faedf71493bbacdbb6ffd7813722025-08-20T02:31:37ZengFrontiers Media S.A.Frontiers in Behavioral Neuroscience1662-51532024-12-011810.3389/fnbeh.2024.13867231386723Analysis of goal, feedback and rewards on sustained attention via machine learningNethali Fernando0Nethali Fernando1Matthew Robison2Matthew Robison3Pedro D. Maia4Pedro D. Maia5Department of Mathematics, University of Texas at Arlington, Arlington, TX, United StatesDivision of Data Science, College of Science, University of Texas at Arlington, Arlington, TX, United StatesDepartment of Psychology, University of Notre Dame, Notre Dame, IN, United StatesDepartment of Psychology, University of Texas at Arlington, Arlington, TX, United StatesDepartment of Mathematics, University of Texas at Arlington, Arlington, TX, United StatesDivision of Data Science, College of Science, University of Texas at Arlington, Arlington, TX, United StatesIntroductionSustaining attention is a notoriously difficult task as shown in a recent experiment where reaction times (RTs) and pupillometry data were recorded from 350 subjects in a 30-min vigilance task. Subjects were also presented with different types of goal, feedback, and reward.MethodsIn this study, we revisit this experimental data and solve three families of machine learning problems: (i) RT-regression problems, to predict subjects' RTs using all available data, (ii) RT-classification problems, to classify responses more broadly as attentive, semi-attentive, and inattentive, and (iii) to predict the subjects' experimental conditions from physiological data.ResultsAfter establishing that regressing RTs is in general a difficult task, we achieve better results classifying them in broader categories. We also successfully disambiguate subjects who received goals and rewards from those who did not. Finally, we quantify changes in accuracy when coarser features (averaged throughout multiple trials) are used. Interestingly, the machine learning pipeline selects different features depending on their resolution, suggesting that predictive physiological features are also resolution-specific.DiscussionThese findings highlight the potential of machine learning to advance research on sustained attention and behavior, particularly in studies incorporating pupillometry or other physiological measurements, offering new avenues for understanding and analysis.https://www.frontiersin.org/articles/10.3389/fnbeh.2024.1386723/fullsustained attentionpupillometry datarewardreaction timemachine learningclassification |
| spellingShingle | Nethali Fernando Nethali Fernando Matthew Robison Matthew Robison Pedro D. Maia Pedro D. Maia Analysis of goal, feedback and rewards on sustained attention via machine learning Frontiers in Behavioral Neuroscience sustained attention pupillometry data reward reaction time machine learning classification |
| title | Analysis of goal, feedback and rewards on sustained attention via machine learning |
| title_full | Analysis of goal, feedback and rewards on sustained attention via machine learning |
| title_fullStr | Analysis of goal, feedback and rewards on sustained attention via machine learning |
| title_full_unstemmed | Analysis of goal, feedback and rewards on sustained attention via machine learning |
| title_short | Analysis of goal, feedback and rewards on sustained attention via machine learning |
| title_sort | analysis of goal feedback and rewards on sustained attention via machine learning |
| topic | sustained attention pupillometry data reward reaction time machine learning classification |
| url | https://www.frontiersin.org/articles/10.3389/fnbeh.2024.1386723/full |
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