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680 results on '"Faubert, Jocelyn"'

1. VFA: Vision Frequency Analysis of Foundation Models and Human

Author

Darvishi-Bayazi, Mohammad-Javad, Arefin, Md Rifat, Faubert, Jocelyn, and Rish, Irina

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Human-Computer Interaction
Abstract

Machine learning models often struggle with distribution shifts in real-world scenarios, whereas humans exhibit robust adaptation. Models that better align with human perception may achieve higher out-of-distribution generalization. In this study, we investigate how various characteristics of large-scale computer vision models influence their alignment with human capabilities and robustness. Our findings indicate that increasing model and data size and incorporating rich semantic information and multiple modalities enhance models' alignment with human perception and their overall robustness. Our empirical analysis demonstrates a strong correlation between out-of-distribution accuracy and human alignment.

Published
2024

3. Amplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer Learning

Author

Darvishi-Bayazi, Mohammad-Javad, Ghaemi, Mohammad Sajjad, Lesort, Timothee, Arefin, Md Rifat, Faubert, Jocelyn, and Rish, Irina

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Pathology diagnosis based on EEG signals and decoding brain activity holds immense importance in understanding neurological disorders. With the advancement of artificial intelligence methods and machine learning techniques, the potential for accurate data-driven diagnoses and effective treatments has grown significantly. However, applying machine learning algorithms to real-world datasets presents diverse challenges at multiple levels. The scarcity of labelled data, especially in low regime scenarios with limited availability of real patient cohorts due to high costs of recruitment, underscores the vital deployment of scaling and transfer learning techniques. In this study, we explore a real-world pathology classification task to highlight the effectiveness of data and model scaling and cross-dataset knowledge transfer. As such, we observe varying performance improvements through data scaling, indicating the need for careful evaluation and labelling. Additionally, we identify the challenges of possible negative transfer and emphasize the significance of some key components to overcome distribution shifts and potential spurious correlations and achieve positive transfer. We see improvement in the performance of the target model on the target (NMT) datasets by using the knowledge from the source dataset (TUAB) when a low amount of labelled data was available. Our findings indicate a small and generic model (e.g. ShallowNet) performs well on a single dataset, however, a larger model (e.g. TCN) performs better on transfer and learning from a larger and diverse dataset.

Published
2023
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6. Distortion in ophthalmic optics: A review of the principal concepts and models

Author

Hanssens, Jean-Marie, Bourdoncle, Bernard, Gresset, Jacques, Faubert, Jocelyn, and Simonet, Pierre

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Although all members of the ophthalmic community agree that distortion is an aberration affecting the geometry of an image produced by the periphery of an ophthalmic lens, there are several approaches for analyzing and quantifying this aberration. Various concepts have been introduced: ordinary distortion, stationary distortion and central static distortion are associated with a fixed eye behind the ophthalmic lens, whereas rotatory distortion, peripheral distortion, lateral static distortion, and dynamic distortion require a secondary position of gaze behind the lens. Furthermore, concept definitions vary from one author to another. The goal of this paper is to review the various concepts, analyze their effects on lens design and determine their ability to predict the deformation of an image as perceived by the lens wearer. These entities can be classified within 3 categories: the concepts associated with an ocular rotation, the concepts resulting from an optical approach, and the concepts using a perceptual approach. Among the various concepts reviewed, it appears that the Le Grand-Fry approach for analyzing and displaying distortion is preferable to others and allows modeling of the different possible types of distortions affecting the periphery of an ophthalmic lens., Comment: 38 pages, 11 figures

Published
2023

7. Significant changes in EEG neural oscillations during different phases of three-dimensional multiple object tracking task (3D-MOT) imply different roles for attention and working memory

Author

Roy, Yannick and Faubert, Jocelyn

Subjects
Quantitative Biology - Neurons and Cognition, Computer Science - Machine Learning
Abstract

Our ability to track multiple objects in a dynamic environment enables us to perform everyday tasks such as driving, playing team sports, and walking in a crowded mall. Despite more than three decades of literature on multiple object tracking (MOT) tasks, the underlying and intertwined neural mechanisms remain poorly understood. Here we looked at the electroencephalography (EEG) neural correlates and their changes across the three phases of a 3D-MOT task, namely identification, tracking and recall. We recorded the EEG activity of 24 participants while they were performing a 3D-MOT task with either 1, 2 or 3 targets where some trials were lateralized and some were not. We observed what seems to be a handoff between focused attention and working memory processes when going from tracking to recall. Our findings revealed a strong inhibition in delta and theta frequencies from the frontal region during tracking, followed by a strong (re)activation of these same frequencies during recall. Our results also showed contralateral delay activity (CDA) for the lateralized trials, in both the identification and recall phases but not during tracking.

Published
2022

8. Is the Contralateral Delay Activity (CDA) a robust neural correlate for Visual Working Memory (VWM) tasks? A reproducibility study

Author

Roy, Yannick and Faubert, Jocelyn

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

Visual working memory (VWM) allows us to actively store, update and manipulate visual information surrounding us. While the underlying neural mechanisms of VWM remain unclear, contralateral delay activity (CDA), a sustained negativity over the hemisphere contralateral to the positions of visual items to be remembered, is often used to study VWM. To investigate if the CDA is a robust neural correlate for VWM tasks, we reproduced eight CDA-related studies with a publicly accessible EEG dataset. We used the raw EEG data from these eight studies and analyzed all of them with the same basic pipeline to extract CDA. We were able to reproduce the results from all the studies and show that with a basic automated EEG pipeline we can extract a clear CDA signal. We share insights from the trends observed across the studies and raise some questions about the CDA decay and the CDA during the recall phase, which surprisingly, none of the eight studies did address. Finally, we also provide reproducibility recommendations based on our experience and challenges in reproducing these studies.

Published
2022
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11. The Influence of an Extended Optical Mode on the Performance of Microcavity Forced Oscillator

Author

Avalos-Sánchez, H., Hernández-Méndez, E. Y., Nieto-Ruiz, E., Carmona, A. J., Palomino-Ovando, M. A., Toledo-Solano, M., Misaghian, Khashayar, Faubert, Jocelyn, Lugo, J. Eduardo, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Mahmud, Mufti, editor, Mendoza-Barrera, Claudia, editor, Kaiser, M. Shamim, editor, Bandyopadhyay, Anirban, editor, Ray, Kanad, editor, and Lugo, Eduardo, editor

Published
2023
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12. Cognitive Assessment and Trading Performance Correlations

Author

Eduardo Lugo, J., Faubert, Jocelyn, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Mahmud, Mufti, editor, Mendoza-Barrera, Claudia, editor, Kaiser, M. Shamim, editor, Bandyopadhyay, Anirban, editor, Ray, Kanad, editor, and Lugo, Eduardo, editor

Published
2023
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16. Deep learning-based electroencephalography analysis: a systematic review

Author

Roy, Yannick, Banville, Hubert, Albuquerque, Isabela, Gramfort, Alexandre, Falk, Tiago H., and Faubert, Jocelyn

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing, Statistics - Machine Learning
Abstract

Electroencephalography (EEG) is a complex signal and can require several years of training to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question. In this work, we review 156 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches in order to inform future research and formulate recommendations. Various data items were extracted for each study pertaining to 1) the data, 2) the preprocessing methodology, 3) the DL design choices, 4) the results, and 5) the reproducibility of the experiments. Our analysis reveals that the amount of EEG data used across studies varies from less than ten minutes to thousands of hours. As for the model, 40% of the studies used convolutional neural networks (CNNs), while 14% used recurrent neural networks (RNNs), most often with a total of 3 to 10 layers. Moreover, almost one-half of the studies trained their models on raw or preprocessed EEG time series. Finally, the median gain in accuracy of DL approaches over traditional baselines was 5.4% across all relevant studies. More importantly, however, we noticed studies often suffer from poor reproducibility: a majority of papers would be hard or impossible to reproduce given the unavailability of their data and code. To help the field progress, we provide a list of recommendations for future studies and we make our summary table of DL and EEG papers available and invite the community to contribute.

Published
2019

17. Effect of 3D-Multiple Object Tracking Training on Manual Dexterity in Elderly Adults with Dementia and Mild Cognitive Impairment

Author

Santana-Vargas, Ángel Daniel, Pérez-Pacheco, Argelia, Arreola-Mora, Claudia, Chavez-Negrete, Adolfo, Morelos, Laura Paulina Burgos, de Jesús R. Sánchez, José, Lugo, J. Eduardo, Faubert, Jocelyn, Bandyopadhyay, Anirban, Series Editor, Ray, Kanad, Series Editor, Poon, Chi-Sang, Series Editor, Kaiser, M. Shamim, editor, Mahmud, Mufti, editor, and Al Mamun, Shamim, editor

Published
2022
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18. Radiation Pressure in Opal-Based Microcavities

Author

Toledo-Solano, M., Palomino-Ovando, M. A., Sánchez-Mora, E., Faubert, Jocelyn, Lugo, J. Eduardo, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kaiser, M. Shamim, editor, Bandyopadhyay, Anirban, editor, Ray, Kanad, editor, Singh, Raghvendra, editor, and Nagar, Vishal, editor

Published
2022
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19. An Implementational-Level Model of the Human Dorsal Pathway for Biological Motion Perception of High Complexity

Author

Misaghian, Khashayar, Lugo, J. Eduardo, Faubert, Jocelyn, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kaiser, M. Shamim, editor, Bandyopadhyay, Anirban, editor, Ray, Kanad, editor, Singh, Raghvendra, editor, and Nagar, Vishal, editor

Published
2022
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20. Rotational Opponent Motion Detection Impact in Biological Motion Perception

Author

Misaghian, Khashayar, Lugo, J. Eduardo, Faubert, Jocelyn, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kaiser, M. Shamim, editor, Bandyopadhyay, Anirban, editor, Ray, Kanad, editor, Singh, Raghvendra, editor, and Nagar, Vishal, editor

Published
2022
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21. Tuning Wavelength of the Localized Mode Microcavity by Applying Different Oxygen Flows

Author

Jiménez-Vivanco, María R., García, Godofredo, Morales-Morales, Franciso, Coyopol, Antonio, Martínez, Lizeth, Faubert, Jocelyn, Lugo, J. E., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kaiser, M. Shamim, editor, Ray, Kanad, editor, Bandyopadhyay, Anirban, editor, Jacob, Kavikumar, editor, and Long, Kek Sie, editor

Published
2022
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22. Use of a Computational Tool for the Assessment of Attention of Medical Residents After a day on Duty

Author

Pérez-Pacheco, Argelia, García-García, José A., Lugo, J. Eduardo, Faubert, Jocelyn, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kaiser, M. Shamim, editor, Ray, Kanad, editor, Bandyopadhyay, Anirban, editor, Jacob, Kavikumar, editor, and Long, Kek Sie, editor

Published
2022
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25. Increasing Light-Induced Forces with Magnetic Photonic Glasses.

Author

Avalos-Sánchez, Hugo, Carmona-Carmona, Abraham J., Palomino-Ovando, Martha A., Desirena, Benito Flores, Palomino-Merino, Rodolfo, Misaghian, Khashayar, Faubert, Jocelyn, Toledo-Solano, Miller, and Lugo, Jesus Eduardo

Subjects
ELECTROMAGNETIC induction, MAGNETISM, ELECTROMAGNETIC forces, SPIN glasses, FORCE density
Abstract

In this work, we theoretically and experimentally study the induction of electromagnetic forces in an opal-based magnetic photonic glass, where light normally impinges onto a disordered arrangement of SiO2 spheres by the aggregation of Fe3O4 nanoparticles. The working wavelength is 633 nm. Experimental evidence is presented for the force that results from forced oscillations of the photonic structure. Finite-element method simulations and a theoretical model estimate the magnetic force volumetric density value, peak displacement, and velocity of oscillations. The magnetic force is of the order of 56 microN, which is approximately 500-times higher than forces induced in dielectric optomechanical photonic crystal cavities. [ABSTRACT FROM AUTHOR]

Published
2024
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26. An Effective Filtering Process for the Noise Suppression in Eye Movement Signals

Author

Mejia-Romero, Sergio, Eduardo Lugo, J., Bernardin, Delphine, Faubert, Jocelyn, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Ray, Kanad, editor, Roy, Krishna Chandra, editor, Toshniwal, Sandeep Kumar, editor, Sharma, Harish, editor, and Bandyopadhyay, Anirban, editor

Published
2021
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27. Evaluation of Bio-movements Using Nonlinear Dynamics

Author

Mejia-Romero, Sergio, Eduardo Lugo, J., Bernardin, Delphine, Faubert, Jocelyn, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Ray, Kanad, editor, Roy, Krishna Chandra, editor, Toshniwal, Sandeep Kumar, editor, Sharma, Harish, editor, and Bandyopadhyay, Anirban, editor

Published
2021
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28. The Dynamic Performance of Gaze Movement, Using Spectral Decomposition and Phasor Representation

Author

Mejia-Romero, Sergio, Eduardo Lugo, J., Bernardin, Delphine, Faubert, Jocelyn, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Ray, Kanad, editor, Roy, Krishna Chandra, editor, Toshniwal, Sandeep Kumar, editor, Sharma, Harish, editor, and Bandyopadhyay, Anirban, editor

Published
2021
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29. Gaze Movement’s Entropy Analysis to Detect Workload Levels

Author

Mejia-Romero, Sergio, Michaels, Jesse, Eduardo Lugo, J., Bernardin, Delphine, Faubert, Jocelyn, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Kaiser, M. Shamim, editor, Bandyopadhyay, Anirban, editor, Mahmud, Mufti, editor, and Ray, Kanad, editor

Published
2021
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30. Task Switching and Visual Discrimination in Pedestrian Mobile Multitasking: Influence of IT Mobile Task Type

Author

Léger, Pierre-Majorique, Labonté-Lemoyne, Elise, Fredette, Marc, Cameron, Ann-Frances, Bellavance, François, Lepore, Franco, Faubert, Jocelyn, Boissonneault, Elise, Murray, Audrey, Chen, Shang-Lin, Sénécal, Sylvain, Spagnoletti, Paolo, Series Editor, De Marco, Marco, Series Editor, Pouloudi, Nancy, Series Editor, Te'eni, Dov, Series Editor, vom Brocke, Jan, Series Editor, Winter, Robert, Series Editor, Baskerville, Richard, Series Editor, Davis, Fred D., editor, Riedl, René, editor, Léger, Pierre-Majorique, editor, Randolph, Adriane, editor, and Fischer, Thomas, editor

Published
2020
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37. Auditory Noise Facilitates Lower Visual Reaction Times in Humans.

Author

Pérez-Pacheco, Argelia, Rodríguez Morales, Fernando Yael, Misaghian, Khashayar, Faubert, Jocelyn, and Lugo Arce, Jesus Eduardo

Subjects
STOCHASTIC resonance, HUMAN experimentation, SIGNAL detection, TASK performance, NERVOUS system, REACTION time
Abstract

Simple Summary: Noise can positively and negatively affect the systems it interacts with. When the right amount of noise is added to a weak signal, it can make it easier to detect, a phenomenon known as stochastic resonance. Our research focused on applying noise to improve human reaction times. We observed a significant decrease in the reaction times after placing subjects in the beneficial noise branch close to the optimal point. These findings suggest a novel approach to enhancing human performance in tasks that require faster reaction times, such as sports. Noise is commonly seen as a disturbance but can influence any system it interacts with. This influence may not always be desirable, but sometimes it can improve the system's performance. For example, stochastic resonance is a phenomenon where adding the right amount of noise to a weak signal makes it easier to detect. This is known as sub-threshold detection. This sub-threshold detection's natural fingerprint is the fact that the threshold values follow an inverse U-shaped curve as the noise intensity increases. The minimum threshold value is the point of maximum sensitivity and represents the optimal point that divides the dynamics in two. Below that point, we can find the beneficial noise branch, where the noise can facilitate better detection. Above that point, the common detrimental noise concept can be found: adding noise hinders signal detection. The nervous system controls the movements and bodily functions in the human body. By reducing the sensory thresholds, we can improve the balance of these functions. Additionally, researchers have wondered if noise could be applied to different senses or motor mechanisms to enhance our abilities. In this work, noise is used to improve human reaction times. We tested the hypothesis that visual reaction times decrease significantly when the subject's perception is in the beneficial noise branch and closer to the optimal point than outside of this condition. Auditory noise was introduced in 101 human subjects using an interface capable of searching for the right amount of noise to place the subject in the beneficial noise branch close to the optimal point. When comparing the results, the reaction times decreased when the subjects were at the optimal point compared to when the subjects were outside of such conditions. These results reveal the possibility of using this approach to enhance human performance in tasks requiring faster reaction times, such as sports. [ABSTRACT FROM AUTHOR]

Published
2024
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38. Characterization of Stochastic Resonance by the Discrete General Beta Distribution

Author

Lugo, J. E., Doti, R., Faubert, Jocelyn, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Ray, Kanad, editor, Sharan, S. N., editor, Rawat, Sanyog, editor, Jain, S. K., editor, Srivastava, Sumit, editor, and Bandyopadhyay, Anirban, editor

Published
2019
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39. StaR: An EEGLAB Framework for the Measure Projection Toolbox (MPT) Statistical Analyses to be Performed in R

Author

Roy, Yannick, Piponnier, Jean-Claude, Faubert, Jocelyn, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Ray, Kanad, editor, Sharan, S. N., editor, Rawat, Sanyog, editor, Jain, S. K., editor, Srivastava, Sumit, editor, and Bandyopadhyay, Anirban, editor

Published
2019
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47. Training with a Three-Dimensional Multiple Object-Tracking (3D-MOT) Paradigm Improves Attention in Students with a Neurodevelopmental Condition: A Randomized Controlled Trial

Author

Tullo, Domenico, Guy, Jacalyn, Faubert, Jocelyn, and Bertone, Armando

Abstract

The efficacy of attention training paradigms is influenced by many factors, including the specificity of targeted cognitive processes, accuracy of outcome measures, accessibility to specialized populations, and adaptability to user capability. These issues are increasingly significant when working with children diagnosed with neurodevelopmental conditions that are characterized by attentional difficulties. This study investigated the efficacy of training attention in students with neurodevelopmental conditions using a novel three-dimensional Multiple Object-Tracking (3D-MOT) task. All students (ages 6-18 years) performed the "Conners Continuous Performance Task (CPT-3)" as a baseline measure of attention. They were then equally and randomly assigned to one of three groups: a treatment group, (3D-MOT); an active control group (visual strategy/math-based game, "2048"); and a treatment as usual group. Students were trained on their respective tasks for a total of 15 training sessions over a five-week period and then reassessed on the "CPT-3." Results showed that post-training "CPT-3" performance significantly improved from baseline for participants in the treatment group only. This improvement indicates that training with 3D-MOT increased attentional abilities in students with neurodevelopmental conditions. These results suggest that training attention with a non-verbal, visual-based task is feasible in a school setting and accessible to atypically developing students with attentional difficulties.

Published
2018
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