More Like this

1. Promoting social attention in 3‐year‐olds with ASD through gaze‐contingent eye tracking

   https://doi.org/10.1002/aur.2199  

   Wang, Quan ; Wall, Carla A. ; Barney, Erin C. ; Bradshaw, Jessica L. ; Macari, Suzanne L. ; Chawarska, Katarzyna ; Shic, Frederick ( August 2019 , Autism Research)

   Young children with autism spectrum disorder (ASD) look less toward faces compared to their non‐ASD peers, limiting access to social learning. Currently, no technologies directly target these core social attention difficulties. This study examines the feasibility of automated gaze modification training for improving attention to faces in 3‐year‐olds with ASD. Using free‐viewing data from typically developing (TD) controls (n = 41), we implemented gaze‐contingent adaptive cueing to redirect children with ASD toward normative looking patterns during viewing of videos of an actress. Children with ASD were randomly assigned to either (a) an adaptive Cue condition (Cue,n = 16) or (b) a No‐Cue condition (No‐Cue,n = 19). Performance was examined at baseline, during training, and post‐training, and contrasted with TD controls (n = 23). Proportion of time looking at the screen (%Screen) and at actresses' faces (%Face) was analyzed. At Pre‐Training, Cue and No‐Cue groups did not differ in %Face (P > 0.1). At Post‐Training, the Cue group had higher %Face than the No‐Cue group (P = 0.015). In the No‐Cue group %Face decreased Pre‐ to Post‐Training; no decline was observed in the Cue group. These results suggest gaze‐contingent training effectively mitigated decreases of attention toward the face of onscreen social characters in ASD. Additionally, larger training effects were observed in children with lower nonverbal ability, suggesting a gaze‐contingent approach may be particularly relevant for children with greater cognitive impairment. This work represents development toward new social attention therapeutic systems that could augment current behavioral interventions.Autism Res2020, 13: 61–73. © 2019 International Society for Autism Research, Wiley Periodicals, Inc.

   In this study, we leverage a new technology that combines eye tracking and automatic computer programs to help very young children with ASD look at social information in a more prototypical way. In a randomized controlled trial, we show that the use of this technology prevents the diminishing attention toward social information normally seen in children with ASD over the course of a single experimental session. This work represents development toward new social attention therapeutic systems that could augment current behavioral interventions.

    

   more » « less
2. Investigating Receptivity and Affect Using Machine Learning: Ecological Momentary Assessment and Wearable Sensing Study

   https://doi.org/10.2196/46347  

   King, Zachary D ; Yu, Han ; Vaessen, Thomas ; Myin-Germeys, Inez ; Sano, Akane ( January 2024 , JMIR mHealth and uHealth)

   As mobile health (mHealth) studies become increasingly productive owing to the advancements in wearable and mobile sensor technology, our ability to monitor and model human behavior will be constrained by participant receptivity. Many health constructs are dependent on subjective responses, and without such responses, researchers are left with little to no ground truth to accompany our ever-growing biobehavioral data. This issue can significantly impact the quality of a study, particularly for populations known to exhibit lower compliance rates. To address this challenge, researchers have proposed innovative approaches that use machine learning (ML) and sensor data to modify the timing and delivery of surveys. However, an overarching concern is the potential introduction of biases or unintended influences on participants’ responses when implementing new survey delivery methods.

   This study aims to demonstrate the potential impact of an ML-based ecological momentary assessment (EMA) delivery system (using receptivity as the predictor variable) on the participants’ reported emotional state. We examine the factors that affect participants’ receptivity to EMAs in a 10-day wearable and EMA–based emotional state–sensing mHealth study. We study the physiological relationships indicative of receptivity and affect while also analyzing the interaction between the 2 constructs.

   We collected data from 45 healthy participants wearing 2 devices measuring electrodermal activity, accelerometer, electrocardiography, and skin temperature while answering 10 EMAs daily, containing questions about perceived mood. Owing to the nature of our constructs, we can only obtain ground truth measures for both affect and receptivity during responses. Therefore, we used unsupervised and supervised ML methods to infer affect when a participant did not respond. Our unsupervised method used k-means clustering to determine the relationship between physiology and receptivity and then inferred the emotional state during nonresponses. For the supervised learning method, we primarily used random forest and neural networks to predict the affect of unlabeled data points as well as receptivity.

   Our findings showed that using a receptivity model to trigger EMAs decreased the reported negative affect by >3 points or 0.29 SDs in our self-reported affect measure, scored between 13 and 91. The findings also showed a bimodal distribution of our predicted affect during nonresponses. This indicates that this system initiates EMAs more commonly during states of higher positive emotions.

   Our results showed a clear relationship between affect and receptivity. This relationship can affect the efficacy of an mHealth study, particularly those that use an ML algorithm to trigger EMAs. Therefore, we propose that future work should focus on a smart trigger that promotes EMA receptivity without influencing affect during sampled time points.

    

   more » « less
3. Analysing adaptive scaffolds that help students develop self‐regulated learning behaviours

   https://doi.org/10.1111/jcal.12761  

   Munshi, Anabil ; Biswas, Gautam ; Baker, Ryan ; Ocumpaugh, Jaclyn ; Hutt, Stephen ; Paquette, Luc ( December 2022 , Journal of Computer Assisted Learning)

   Providing adaptive scaffolds to help learners develop effective self‐regulated learning (SRL) behaviours has been an important goal for intelligent learning environments. Adaptive scaffolding is especially important in open‐ended learning environments (OELE), where novice learners often face difficulties in completing their learning tasks.

   This paper presents a systematic framework for adaptive scaffolding in Betty's Brain, a learning‐by‐teaching OELE for middle school science, where students construct a causal model to teach a virtual agent, generically named Betty. We evaluate the adaptive scaffolding framework and discuss its implications on the development of more effective scaffolds for SRL in OELEs.

   We detect key cognitive/metacognitiveinflection points, that is, moments where students' behaviours and performance change during learning, often suggesting an inability to apply effective learning strategies. At inflection points, Mr. Davis (a mentor agent in Betty's Brain) or Betty (the teachable agent) provides context‐specific conversational feedback, focusing on strategies to help the student become a more productive learner, or encouragement to support positive emotions. We conduct a classroom study with 98 middle schoolers to analyse the impact of adaptive scaffolds on students' learning behaviours and performance. We analyse how students with differential pre‐to‐post learning outcomes receive and use the scaffolds to support their subsequent learning process in Betty's Brain.

   Adaptive scaffolding produced mixed results, with some scaffolds (viz., strategic hints that supported debugging and assessment of causal models) being generally more useful to students than others (viz., encouragement prompts). Additionally, there were differences in how students with high versus low learning outcomes responded to some hints, as suggested by the differences in their learning behaviours and performance in the intervals after scaffolding. Overall, our findings suggest how adaptive scaffolding in OELEs like Betty's Brain can be further improved to better support SRL behaviours and narrow the learning outcomes gap between high and low performing students.

   This paper contributes to our understanding and impact of adaptive scaffolding in OELEs. The results of our study indicate that successful scaffolding has to combine context‐sensitive inflection points with conversational feedback that is tailored to the students' current proficiency levels and needs. Also, our conceptual framework can be used to design adaptive scaffolds that help students develop and apply SRL behaviours in other computer‐based learning environments.

    

   more » « less
4. An adaptive approach to machine learning for compact particle accelerators

   https://doi.org/10.1038/s41598-021-98785-0  

   Scheinker, Alexander ; Cropp, Frederick ; Paiagua, Sergio ; Filippetto, Daniele ( September 2021 , Scientific Reports)

   Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However, for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with which the ML models were trained. For complex systems, re-training is only possible if the changes are slow relative to the rate at which large numbers of new input-output training data can be non-invasively recorded. In this work, we present an approach to deep learning for time-varying systems that does not require re-training, but uses instead an adaptive feedback in the architecture of deep convolutional neural networks (CNN). The feedback is based only on available system output measurements and is applied in the encoded low-dimensional dense layers of the encoder-decoder CNNs. First, we develop an inverse model of a complex accelerator system to map output beam measurements to input beam distributions, while both the accelerator components and the unknown input beam distribution vary rapidly with time. We then demonstrate our method on experimental measurements of the input and output beam distributions of the HiRES ultra-fast electron diffraction (UED) beam line at Lawrence Berkeley National Laboratory, and showcase its ability for automatic tracking of the time varying photocathode quantum efficiency map. Our method can be successfully used to aid both physics and ML-based surrogate online models to provide non-invasive beam diagnostics.

    

   more » « less
5. Modeling Brain Dynamics During Virtual Reality-Based Emergency Response Learning Under Stress

   https://doi.org/10.1177/00187208211054894  

   Tyagi, Oshin ; Hopko, Sarah ; Kang, John ; Shi, Yangming ; Du, Jing ; Mehta, Ranjana K. ( December 2021 , Human Factors: The Journal of the Human Factors and Ergonomics Society)

   Background Stress affects learning during training, and virtual reality (VR) based training systems that manipulate stress can improve retention and retrieval performance for firefighters. Brain imaging using functional Near Infrared Spectroscopy (fNIRS) can facilitate development of VR-based adaptive training systems that can continuously assess the trainee’s states of learning and cognition. Objective The aim of this study was to model the neural dynamics associated with learning and retrieval under stress in a VR-based emergency response training exercise. Methods Forty firefighters underwent an emergency shutdown training in VR and were randomly assigned to either a control or a stress group. The stress group experienced stressors including smoke, fire, and explosions during the familiarization and training phase. Both groups underwent a stress memory retrieval and no-stress memory retrieval condition. Participant’s performance scores, fNIRS-based neural activity, and functional connectivity between the prefrontal cortex (PFC) and motor regions were obtained for the training and retrieval phases. Results The performance scores indicate that the rate of learning was slower in the stress group compared to the control group, but both groups performed similarly during each retrieval condition. Compared to the control group, the stress group exhibited suppressed PFC activation. However, they showed stronger connectivity within the PFC regions during the training and between PFC and motor regions during the retrieval phases. Discussion While stress impaired performance during training, adoption of stress-adaptive neural strategies (i.e., stronger brain connectivity) were associated with comparable performance between the stress and the control groups during the retrieval phase. 

   more » « less