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1. Feedback-Related ERPs Predict Learning Speed

   Moore, I; Larson, C; Fox, A.; Arbel, Y. (March 2019, Cognitive Neuroscience Society ... Annual Meeting abstract program)

   The efficiency with which a learner processes external feedback has implications for both learning speed and performance. A growing body of literature suggests that the feedback-related negativity (FRN) event-related potential (ERP) and the fronto-central positivity (FCP) ERP reflect the extent to which feedback is used by a learner to improve performance. To determine whether the FRN and FCP predict learning speed, 82 participants aged 7:6 - 11:0 learned the non-word names of 20 novel objects in a two-choice feedback-based declarative learning task. Participants continued the task until reaching the learning criterion of 2 consecutive training blocks with accuracy greater than 90%, or until 10 blocks were completed. Learning speed was determined by the total number of incorrect responses before reaching the learning criterion. Using linear regression models, the FRN amplitude in response to positive feedback was found to be a significant predictor of learning speed when controlling for age. The FCP amplitude in response to negative feedback was significantly negatively associated with learning speed, meaning that large FCP amplitudes in response to negative feedback predicted faster learning. An interaction between FCP and age suggested that for older children in this sample, smaller FCP amplitude in response to positive feedback was associated with increased speed, while for younger children, larger FCP amplitude predicted faster learning. These results suggest that the feedback related ERP components are associated with learning speed, and can reflect developmental changes in feedback-based learning. 

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2. The effect of feedback validity on learning in children and adults: an electrophysiological study

   Larson, C.; Moore, I.; Fox, A.; Arbel, Y. (March 2019, Cognitive Neuroscience Society ... Annual Meeting abstract program)

   The present study evaluated at the behavioral and neurophysiological level the effect of feedback validity on learning in adults and children. Participants (82 children aged 7-11; 42 adults aged 18-25) completed a two-choice classification task, in which they sorted items from eight different categories into one of two bins, by pressing one of two buttons on a response box. Each response was followed by positive or negative feedback. Four of the eight categories were mapped consistently to a specific response, leading to consistent valid feedback. The other four were mapped to a specific response 80% of the time; in 20% of these trials, participants received invalid feedback. As participants performed the task, their EEG data were recorded. Behaviorally, accuracy was greater for the consistently valid condition than the inconsistently valid condition for both adults and children. There were no significant differences in accuracy between adults and children. Feedback-related event related potentials (ERPs) were evaluated and compared between the two groups. The amplitudes of the feedback related negativity (FRN) and fronto-central positivity (FCP) were sensitive to valence and age group, with FRN being larger in children, and FCP larger in adults. Interaction effects suggested that FRN response to positive feedback was sensitive to feedback validity in both age groups. However, the FCP was sensitive to validity for only for positive feedback in children and only for negative feedback in adults. These results further evidence of differing neurophysiological reactions to feedback in learning between children and adults. 

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3. Assaying neural activity of children during video game play in public spaces: a deep learning approach

   https://doi.org/10.1088/1741-2552/ab1876  

   Sujatha Ravindran, Akshay; Mobiny, Aryan; Cruz-Garza, Jesus G.; Paek, Andrew; Kopteva, Anastasiya; Contreras Vidal, José L. (May 2019, Journal of Neural Engineering)

   Abstract Objective. Understanding neural activity patterns in the developing brain remains one of the grand challenges in neuroscience. Developing neural networks are likely to be endowed with functionally important variability associated with the environmental context, age, gender, and other variables. Therefore, we conducted experiments with typically developing children in a stimulating museum setting and tested the feasibility of using deep learning techniques to help identify patterns of brain activity associated with different conditions.Approach. A four-channel dry EEG-based Mobile brain-body imaging data of children at rest and during videogame play (VGP) was acquired at the Children’s Museum of Houston. A data-driven approach based on convolutional neural networks (CNN) was used to describe underlying feature representations in the EEG and their ability to discern task and gender. The variability of the spectral features of EEG during the rest condition as a function of age was also analyzed.Main results. Alpha power (7–13 Hz) was higher during rest whereas theta power (4–7 Hz) was higher during VGP. Beta (13–18 Hz) power was the most significant feature, higher in females, when differentiating between males and females. Using data from both temporoparietal channels to classify between VGP and rest condition, leave-one-subject-out cross-validation accuracy of 67% was obtained. Age-related changes in EEG spectral content during rest were consistent with previous developmental studies conducted in laboratory settings showing an inverse relationship between age and EEG power.Significance. These findings are the first to acquire, quantify and explain brain patterns observed during VGP and rest in freely behaving children in a museum setting using a deep learning framework. The study shows how deep learning can be used as a data driven approach to identify patterns in the data and explores the issues and the potential of conducting experiments involving children in a natural and engaging environment. 

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4. Predictive Value of Early Autism Detection Models Based on Electronic Health Record Data Collected Before Age 1 Year

   https://doi.org/10.1001/jamanetworkopen.2022.54303  

   Engelhard, Matthew M.; Henao, Ricardo; Berchuck, Samuel I.; Chen, Junya; Eichner, Brian; Herkert, Darby; Kollins, Scott H.; Olson, Andrew; Perrin, Eliana M.; Rogers, Ursula; et al (February 2023, JAMA Network Open)

   Importance Autism detection early in childhood is critical to ensure that autistic children and their families have access to early behavioral support. Early correlates of autism documented in electronic health records (EHRs) during routine care could allow passive, predictive model-based monitoring to improve the accuracy of early detection. Objective To quantify the predictive value of early autism detection models based on EHR data collected before age 1 year. Design, Setting, and Participants This retrospective diagnostic study used EHR data from children seen within the Duke University Health System before age 30 days between January 2006 and December 2020. These data were used to train and evaluate L2-regularized Cox proportional hazards models predicting later autism diagnosis based on data collected from birth up to the time of prediction (ages 30-360 days). Statistical analyses were performed between August 1, 2020, and April 1, 2022. Main Outcomes and Measures Prediction performance was quantified in terms of sensitivity, specificity, and positive predictive value (PPV) at clinically relevant model operating thresholds. Results Data from 45 080 children, including 924 (1.5%) meeting autism criteria, were included in this study. Model-based autism detection at age 30 days achieved 45.5% sensitivity and 23.0% PPV at 90.0% specificity. Detection by age 360 days achieved 59.8% sensitivity and 17.6% PPV at 81.5% specificity and 38.8% sensitivity and 31.0% PPV at 94.3% specificity. Conclusions and Relevance In this diagnostic study of an autism screening test, EHR-based autism detection achieved clinically meaningful accuracy by age 30 days, improving by age 1 year. This automated approach could be integrated with caregiver surveys to improve the accuracy of early autism screening. 

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5. Exploring Different Facilitator Roles in Maker-based Learning

   Zhang, E. (June 2022, International Society of the Learning Sciences)

   The purpose of this study is to investigate how different facilitator roles (educator, adult caregiver, peer and self-facilitation) influence children’s learning engagement--specifically, the learning practices of seeking and sharing resources (SSR)--in a museum’s makerspace. We address two research questions: 1). In what ways do learners' engagement vary when facilitated by different facilitator roles? 2). In what ways do different types of facilitators influence SSR practices across age ranges? The results show that facilitation from caregivers and self-directed facilitation is associated with significantly more SSR practices. Additionally, we found that the influence of different types of facilitators on learning engagement varies across ages. Self-directed learning is associated with an increase of SSR as children grow, while facilitation from caregivers is associated with a decline of SSR as children age. Based on these findings, we discuss implications for facilitation in museum makerspace. 

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