Naturalistic Environment Research Articles

Effects of more natural housing conditions on the muscular and skeletal characteristics of female C57BL/6J mice

BackgroundEnrichment of home cages in laboratory experiments offers clear advantages, but has been criticized in some respects. First, there is a lack of definition, which makes methodological uniformity difficult. Second, there is concern that the enrichment of home cages may increase the variance of results in experiments. Here, the influence of more natural housing conditions on physiological parameters of female C57BL/6J mice was investigated from an animal welfare point of view. For this purpose, the animals were kept in three different housing conditions: conventional cage housing, enriched housing and the semi naturalistic environment. The focus was on musculoskeletal changes after long-term environmental enrichment.ResultsThe housing conditions had a long-term effect on the body weight of the test animals. The more complex and natural the home cage, the heavier the animals. This was associated with increased adipose deposits in the animals. There were no significant changes in muscle and bone characteristics except for single clues (femur diameter, bone resorption marker CTX-1). Additionally, the animals in the semi naturalistic environment (SNE) were found to have the fewest bone anomalies. Housing in the SNE appears to have the least effect on stress hormone concentrations. The lowest oxygen uptake was observed in enriched cage housing.ConclusionsDespite increasing values, observed body weights were in the normal and strain-typical range. Overall, musculoskeletal parameters were slightly improved and age-related effects appear to have been attenuated. The variances in the results were not increased by more natural housing. This confirms the suitability of the applied housing conditions to ensure and increase animal welfare in laboratory experiments.

Attention to emotional stimuli across adulthood and older age: A novel application of eye-tracking within the home.

Previous research has used stationary eye-tracking in the lab to examine age differences in attentional deployment, showing that older adults display gaze patterns toward positive stimuli. This positive gaze preference sometimes improves older adults' mood compared to their younger counterparts. However, the lab environment may lead to different emotion regulation behavior among older adults compared to what they do in their everyday life. We, therefore, present the first use of stationary eye-tracking within participants' homes to examine gaze patterns toward video clips of varying valence and to study age differences in emotional attention among younger, middle-aged, and older adults in a more naturalistic environment. We also compared these results to in-lab gaze preferences among the same participants. Older adults deployed attention more to positive stimuli in the lab but more to negative stimuli in the home. This increased attention to negative content in the home predicted higher self-reported arousal outcomes among middle-aged and older adults. Gaze preferences toward emotional stimuli may thus differ depending on the context, emphasizing the need to explore more naturalistic settings within emotion regulation and aging research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The Development of Technology-Enhanced Performance-Based Peer Feedback to Support Teacher Candidate Practice and Child Outcomes

We used a multi-method design to determine the quality of technology-enhanced performance-based peer feedback, teacher candidate strategy use, and child behaviors over time. We also explored teacher candidates’ perceived social validity of technology-enhanced performance-based peer feedback. Eleven teacher candidates enrolled in an early childhood certification program and their focus children that were at-risk or identified with disabilities engaged in naturalistic early childhood environments (home, community, and classroom settings). Teacher candidates were grouped by their environmental assignment and reviewed video interactions to provide feedback to their peers. Teacher candidates' quality of feedback changed over time which impacted teacher candidate use of strategies as well as focus child use of skills. Additionally, the teacher candidates perceived the intervention package to be feasible and acceptable. Implications for research and practice are discussed.

The Co-Regulatory Coaching Interface Model: A Case Study of a Figure Skating Dyad

Very little research has investigated co-regulated learning (CRL; Hadwin et al., 2011) in the context of sport coaching for skill acquisition. Although research indicates self-regulated learning (SRL) helps elite competitive athletes optimize their skill acquisition (McCardle et al., 2019), coaching literature has yet to examine how co-regulated learning experiences in joint work between a coach and athlete are associated with SRL competencies in an athlete. Thus, the objective of this instrumental case study was to describe the nature of joint work between an experienced female coach (aged 53, national level) and a male figure skater (aged 15, provincial level) in a naturalistic environment. Season-long data collection involved analysis of recorded dialogue at 16 practices and three interviews with each participant. Using inductive reflexive thematic analysis, we developed higher-order themes related to macro- and micro-levels of CRL, and implications of the coach’s progression on the development of SRL. The Co-regulatory Coaching Interface Model, representing micro CRL interactions, outlines contributions from each member and dialogue processes facilitating skill acquisition. SRL was both an expected contributor to, and a consequence of interface interactions. We discuss coach-athlete dyadic processes, what they mean for athletes’ self-practice time, and how the model contributes a new perspective on collaborative work between coaches and athletes that has not been emphasized in the coaching science on talent development.

Grass and gravel: Investigating visual properties preschool children and adults use when distinguishing naturalistic images

Little is known about young children's ability to visually categorize complex structures of a naturalistic environment. To investigate this, we analyzed the relevance of different visual properties and superordinate category information to 4- to 5-year-old children's (N = 76) and adults' (N = 72) card sorting decisions. Participants sorted cards depicting complex greyscale images of manmade artifacts, non-living natural elements (e.g., stones), and vegetation into groups, first according to visual similarity and then by the images' superordinate categories. We found that visual properties defined by characteristics of higher-order computations (e.g., variations in shape, distributed detail) predicted children's categorization less than adults. Both age groups' similarity judgements were influenced by the images' assigned superordinate categories—especially by vegetation. Children discriminated vegetation images better than adults when controlling for their overall lower performance. Taken together, these findings demonstrate children's adaptive and flexible processing strategies for complex naturalistic images.

Co-Design of Augmented Reality Games for Learning with Teachers: A Methodological Approach

Augmented Reality Game-Based Learning (ARGBL) is becoming increasingly relevant in Technology-Enhanced Learning. Games with AR characteristics, or even AR applications structured with rules and game elements, are proving to be effective and successful learning experiences. There is a need to include teachers in the design process. In this paper, two case studies are shown in order to validate a methodological approach for the co-design of ARGBL, in which 6 teachers participated. This is a co-design method that proposes a thorough, iterative process guided by design principles and mediated by dialogue among the stakeholders. Here, the process of co-design with teachers is analyzed and assessed using mixed-methods observations on the use of the produced ARGBL games with students on naturalistic environments. The validation process links the usefulness of the ensuing products with the use of the method and shows the benefits of using co-design methods to create ARGBL experiences.

Neural mechanisms for spatial cognition across vertebrates

The ability to navigate the world is a critical cognitive skill that most animals use to find food, shelter, and mates. Understanding the neural basis of navigation requires probing how the brain encodes spatial information through the study of the activity of single neurons and neuronal populations. Classically in vertebrates, studies have centered on the rodent hippocampal formation, which led to the discovery of place, grid, head direction and other cell types. However, since navigation skills are essential to almost all vertebrates, spatial cognition in different species also needs to be explored. In recent years, as a result of advances in technology, new data have emerged on the ways in which space is represented during navigation in the brains of vertebrates other than rodents, including teleost fish, birds, and other mammal species. Here, we review the state of the art on the neural representation of an animal’s position and motion across vertebrates at the level of single neurons. We argue that it is time to pool information across vertebrates to identify the underlying algorithms that lead to successful navigation. Although rodent-based data are important, findings in rodents are unlikely to cover the full spectrum of neural computations supporting navigation strategies in the vertebrate kingdom. Studying other species can shed light on length scales such as in large environments, and different scenarios such as naturalistic environments that are hard to carry out in rodents. In addition, a rodent-centric view may neglect the fact that different species are likely to represent positions in the world in ways that do not exist in mammals. Finally, we provide an outlook for the future which includes prediction about findings in unexplored species, and the opportunities for discoveries and understanding in this field.

Evolution of a System to Monitor Infant Neuromotor Development in the Home: Lessons from COVID-19

In the nine months leading up to COVID-19, our biomedical engineering research group was in the very early stages of development and in-home testing of HUGS, the Hand Use and Grasp Sensor (HUGS) system. HUGS was conceived as a tool to allay parents' anxiety by empowering them to monitor their infants' neuromotor development at home. System focus was on the evolving patterns of hand grasp and general upper extremity movement, over time, in the naturalistic environment of the home, through analysis of data captured from force-sensor-embedded toys and 3D video as the baby played. By the end of March, 2020, as the COVID-19 pandemic accelerated and global lockdown ensued, home visits were no longer possible and HUGS system testing ground to an abrupt halt. In the spring of 2021, still under lockdown, we were able to resume recruitment and in-home testing with HUGS-2, a system whose key requirement was that it be contactless. Participating families managed the set up and use of HUGS-2, supported by a detailed library of video materials and virtual interaction with the HUGS team for training and troubleshooting over Zoom. Like the positive/negative poles of experience reported by new parents under the isolation mandated to combat the pandemic, HUGS research was both impeded and accelerated by having to rely solely on distance interactions to support parents, troubleshoot equipment, and securely transmit data. The objective of this current report is to chronicle the evolution of HUGS. We describe a system whose design and development straddle the pre- and post-pandemic worlds of family-centered health technology design. We identify and classify the clinical approaches to infant screening that predominated in the pre-COVID-19 milieu and describe how these procedural frameworks relate to the family-centered conceptualization of HUGS. We describe how working exclusively through the proxy of parents revealed the family's priorities and goals for child interaction and surfaced HUGS design shortcomings that were not evident in researcher-managed, in-home testing prior to the pandemic.

How Sanctuary Chimpanzees (Pan troglodytes) Use Space after Being Introduced to a Large Outdoor Habitat.

Wild chimpanzees live in large, mixed-sex groups that display a fission-fusion social organization. To provide a social environment more like that of wild chimpanzees, Chimp Haven integrated smaller groups of 3-4 individuals into one large group of 18 individuals. This large group was introduced to a 20,234.28 m2 forested habitat and associated indoor areas. This space was designed to allow the individual chimpanzees to choose their proximity to social companions and provide the group with a more natural environment in which they could express more species-typical behavior. The study took place over a 7-month period that began two weeks prior to the first groups being integrated and ended 4 months after the chimpanzees were released into the habitat. We collected data on the chimpanzees' arboreal, terrestrial, indoor, and outdoor spatial use. The chimpanzees' proximity to their nearest neighbor was also recorded, noting whether they were touching or within arm's reach. Data were collected during daytime and nighttime hours and were utilized to make management decisions about potential group formations. We examined the data using generalized linear mixed models (GLMMs) with individual subjects as a random factor and months as a repeated measure for location and proximity results. There were significant differences in the use of arboreal and outside space over the 7-month time frame, with females more likely to use the arboreal space than males. The chimpanzees were more likely to utilize the habitat over time and increased their proximity to group mates. The results of this study indicate that the chimpanzees responded positively to living in large groups in a spacious naturalistic environment.

Design of a sensor network for the quantitative analysis of sport climbing.

We describe the design of a modular sensorized climbing wall for motion analysis in a naturalistic environment. The wall is equipped with force sensors to measure interaction forces between the athlete and the wall, which can be used by experienced instructors, athletes, or therapists, to gain insights into the quality of motion. A specifically designed triaxial load cell is integrated into each hold placement, invisible to the climber, and compatible with standard climbing holds. Data collected through the sensors is sent to an app running on a portable device. The wall can be adapted to different uses. To validate our design, we recorded a repeated climbing activity of eleven climbers with varying degrees of expertise. Analysis of the interaction forces during the exercise demonstrates that the sensor network design can provide valuable information to track and analyze exercise performance changes over time. Here we report the design process as well as the validation and testing of the sensorized climbing wall.

Contribution of sustained attention abilities to real-world academic skills in children

Sustained attention is a critical cognitive ability that improves over the course of development and predicts important real-world outcomes, such as academic achievement. However, the majority of work demonstrating links between sustained attention and academic skills has been conducted in lab-based settings that lack the ecological validity of a more naturalistic environment, like school. Further, most studies focus on targeted academic measures of specific sub-skills and have not fully examined whether this relationship generalizes to broad measures of academic achievement that are used for important, real-world, academic advancement decisions, such as standardized test scores. To address this gap, we examined the role of sustained attention in predicting targeted and broad assessments of academic abilities, where all skills were assessed in group-based environments in schools. In a sample of over 700 students aged 9–14, we showed that attention was positively related to performance on targeted assessments (math fluency and reading comprehension), as well as broad academic measures (statewide standardized test scores). Moreover, we found that attention was more predictive of targeted math sub-skills compared to assessments of broad math abilities, but was equally predictive of reading for both types of measures. Our findings add to our understanding of how sustained attention is linked to academic skills assessed in more ‘real-world’, naturalistic school environments and have important implications for designing tools to support student’s academic success.

Functional connectivity profiles of the default mode and visual networks reflect temporal accumulative effects of sustained naturalistic emotional experience

Determining and decoding emotional brain processes under ecologically valid conditions remains a key challenge in affective neuroscience. The current functional Magnetic Resonance Imaging (fMRI) based emotion decoding studies are mainly based on brief and isolated episodes of emotion induction, while sustained emotional experience in naturalistic environments that mirror daily life experiences are scarce. Here we used 12 different 10-minute movie clips as ecologically valid emotion-evoking procedures in n = 52 individuals to explore emotion-specific fMRI functional connectivity (FC) profiles on the whole-brain level at high spatial resolution (432 parcellations including cortical and subcortical structures). Employing machine-learning based decoding and cross validation procedures allowed to investigate FC profiles contributing to classification that can accurately distinguish sustained happiness and sadness and that generalize across subjects, movie clips, and parcellations. Both functional brain network-based and subnetwork-based emotion classification results suggested that emotion manifests as distributed representation of multiple networks, rather than a single functional network or subnetwork. Further, the results showed that the Visual Network (VN) and Default Mode Network (DMN) associated functional networks, especially VN-DMN, exhibited a strong contribution to emotion classification. To further estimate the temporal accumulative effect of naturalistic long-term movie-based video-evoking emotions, we divided the 10-min episode into three stages: early stimulation (1∼200 s), middle stimulation (201∼400 s), and late stimulation (401∼600 s) and examined the emotion classification performance at different stimulation stages. We found that the late stimulation contributes most to the classification (accuracy=85.32%, F1-score=85.62%) compared to early and middle stimulation stages, implying that continuous exposure to emotional stimulation can lead to more intense emotions and further enhance emotion-specific distinguishable representations. The present work demonstrated that sustained happiness and sadness under naturalistic conditions are presented in emotion-specific network profiles and these expressions may play different roles in the generation and modulation of emotions. These findings elucidated the importance of network level adaptations for sustained emotional experiences during naturalistic contexts and open new venues for imaging network level contributions under naturalistic conditions.

Effect of optic flow on spatial updating: insight from an immersive virtual reality study.

Self-motion information is required to keep track of where we are with respect to our environment (spatial updating). Visual signals such as optic flow are relevant to provide information about self-motion, especially in the absence of vestibular and/or proprioceptive cues generated by physical movement. However, the role of optic flow on spatial updating is still debated. A virtual reality system based on a head-mounted display was used to allow participants to experience a self-motion sensation within a naturalistic environment in the absence of physical movement. We asked participants to keep track of spatial positions of a target during simulated self-motion while manipulating the availability of optic flow coming from the lower part of the environment (ground plane). In each trial, the ground could be a green lawn (optic flow ON) or covered in snow (optic flow OFF). We observed that the lack of optic flow on the ground had a detrimental effect on spatial updating. Furthermore, we explored the interaction between the optic flow availability and different characteristics of self-motion: we observed that increasing self-motion speed had a detrimental effect on spatial updating, especially in the absence of optic flow, while self-motion direction (leftward, forward, rightward) and path (translational and curvilinear) had no statically significant effect. Overall, we demonstrated that, in the absence of some idiothetic cues, the optic flow provided by the ground has a dominant role for the self-motion estimation and, hence, for the ability to update the spatial relationships between one's position and the position of the surrounding objects.

Cable-free brain imaging for multiple free-moving animals with miniature wireless microscopes.

Although several miniature microscope systems have been developed to allow researchers to image brain neuron activities of free moving rodents, they generally require a long cable connecting to the miniature microscope. It not only limits the behavior of the animal, but also makes it challenging to study multiple animals simultaneously. The aim of this work is to develop a fully wireless miniature microscope that would break constraints from the connecting cables so that the animals could move completely freely, allowing neuroscience researchers to study more of animals' behaviors simultaneously, such as social behavior. We present a wireless mini-microscope (wScope) that enables simultaneously real-time brain imaging preview from multiple free-moving animals. The wScope has a mass of 2.7g and a maximum frame rate of 25Hz at field of view with resolution. The performance of the wScope is validated via real-time imaging of the cerebral blood flow and the activity of neurons in the primary visual cortex (V1) of different mice. The wScope provides a powerful tool for brain imaging of multiple free moving animals in their much larger spaces and more naturalistic environments.

Slipping while counting: gaze–gait interactions during perturbed walking under dual-task conditions

Walking is a complex task. To prevent falls and injuries, gait needs to constantly adjust to the environment. This requires information from various sensory systems; in turn, moving through the environment continuously changes available sensory information. Visual information is available from a distance, and therefore most critical when negotiating difficult terrain. To effectively sample visual information, humans adjust their gaze to the terrain or—in laboratory settings—when facing motor perturbations. During activities of daily living, however, only a fraction of sensory and cognitive resources can be devoted to ensuring safe gait. How do humans deal with challenging walking conditions when they face high cognitive load? Young, healthy participants (N = 24) walked on a treadmill through a virtual, but naturalistic environment. Occasionally, their gait was experimentally perturbed, inducing slipping. We varied cognitive load by asking participants in some blocks to count backward in steps of seven; orthogonally, we varied whether visual cues indicated upcoming perturbations. We replicated earlier findings on how humans adjust their gaze and their gait rapidly and flexibly on various time scales: eye and head movements responded in a partially compensatory pattern and visual cues mostly affected eye movements. Interestingly, the cognitive task affected mainly head orientation. During the cognitive task, we found no clear signs of a less stable gait or of a cautious gait mode, but evidence that participants adapted their gait less to the perturbations than without secondary task. In sum, cognitive load affects head orientation and impairs the ability to adjust to gait perturbations.

Familiar and unfamiliar speaker recognition assessment and system emulation for cochlear implant users.

In the area of speech processing, human speaker identification under naturalistic environments is a challenging task, especially for hearing-impaired individuals with cochlear implants (CIs) or hearing aids (HAs). Motivated by the fact that electrodograms reflect direct CI stimulation of input audio, this study proposes a speaker identification (ID) investigation using two-dimensional electrodograms constructed from the responses of a CI auditory system to emulate CI speaker ID capabilities. Features are extracted from electrodograms through an identity vector (i-vector) framework to train and generate identity models for each speaker using a Gaussian mixture model-universal background model followed by probabilistic linear discriminant analysis. To validate the proposed system, perceptual speaker ID for 20 normal hearing (NH) and seven CI listeners was evaluated with a total of 41 different speakers and compared with the scores from the proposed system. A one-way analysis of variance showed that the proposed system can reliably predict the speaker ID capability of CI (F[1,10] = 0.18, p = 0.68) and NH (F[1,20] = 0, p = 0.98) listeners in naturalistic environments. The impact of speaker familiarity is also addressed, and the results show a reduced performance for speaker recognition by CI subjects using their CI processor, highlighting limitations of current speech processing strategies used in CIs/HAs.

Active visual search in naturalistic environments reflects individual differences in classic visual search performance

Visual search is a ubiquitous activity in real-world environments. Yet, traditionally, visual search is investigated in tightly controlled paradigms, where head-restricted participants locate a minimalistic target in a cluttered array that is presented on a computer screen. Do traditional visual search tasks predict performance in naturalistic settings, where participants actively explore complex, real-world scenes? Here, we leverage advances in virtual reality technology to test the degree to which classic and naturalistic search are limited by a common factor, set size, and the degree to which individual differences in classic search behavior predict naturalistic search behavior in a large sample of individuals (N = 75). In a naturalistic search task, participants looked for an object within their environment via a combination of head-turns and eye-movements using a head-mounted display. Then, in a classic search task, participants searched for a target within a simple array of colored letters using only eye-movements. In each task, we found that participants’ search performance was impacted by increases in set size—the number of items in the visual display. Critically, we observed that participants’ efficiency in classic search tasks—the degree to which set size slowed performance—indeed predicted efficiency in real-world scenes. These results demonstrate that classic, computer-based visual search tasks are excellent models of active, real-world search behavior.

Identifying computational thinking in students through project-based problem-solving activities

PurposeThis paper aims to identify computational thinking (CT) in 4th to 6th grade students in the context of project-based problem-solving while engaged in an after-school program.Design/methodology/approachThis case study approach was selected due to its suitability for answering “how” or “why” questions about real-world phenomena within a set context (Creswell and Poth, 2018; Yin, 2018). This was an appropriate fit given the context of an after-school program and the research question asked how to identify learners’ demonstrated CT through project-based learning hands-on activities and problem-solving in a naturalistic environment.FindingsResults show that heuristics, algorithms and conditional logic were observed more than other components of CT such as data collection, simulations and modeling. Descriptions of common activities in a naturalistic learning environment are presented to illustrate how the students practiced CT over time, which could help readers develop an understanding of CT in conjunction with hands-on problem-solving activities in elementary students. Identifying and classifying CT in this study focused on students’ learning process.Originality/valueThis study contributes to the challenging field of evaluating CT while focusing on observable behaviors and problem-solving activities with various degrees of teacher’s facilitation instead of final artifacts. Implications for researchers and educators interested in integrating CT in K-12 learning and its assessment are discussed.

K-means Clustering Machine Learning Approach Reveals Groups of Homogeneous Individuals with Unique Brain Activation, Task, and Performance Dynamics using fNIRS.

Wearable functional near-infrared spectroscopy (fNIRS) for measuring brain function, in terms of hemodynamic responses, is pervading our everyday life and holds the potential to reliably classify cognitive load in a naturalistic environment. However, human's brain hemodynamic response, behavior, and cognitive and task performance vary, even within and across homogeneous individuals (with same training and skill sets), which limits the reliability of any predictive model for human. In the context of high-stakes tasks, such as in military and first-responder operations, the real-time monitoring of cognitive functions and relating it to the ongoing task, performance outcomes, and behavioral dynamics of the personnel and teams is invaluable. In this work, a portable wearable fNIRS system (WearLight) developed by the author was upgraded, and an experimental protocol was designed to image the prefrontal cortex (PFC) area of the brain of 25 healthy homogeneous participants in a naturalistic environment while participants performed n-back working memory (WM) tasks with four difficulty levels. The raw fNIRS signals were processed using a signal processing pipeline to derive the brain's hemodynamic responses. An unsupervised k-means machine learning (ML) clustering approach, utilizing the task-induced hemodynamic responses as input variables, suggested three unique participant groups. Task performance in terms of % correct, % missing, reaction time, inverse efficiency score (IES), and a proposed IES was extensively evaluated for each participant and the three groups. Results showed that, on average, brain hemodynamic response increased, whereas task performance degraded, with increasing WM load. However, the regression and correlation analysis of WM task, performance, and the brain's hemodynamic responses (TPH) revealedinteresting hidden characteristics and the variation in the TPH relationship between groups. The proposed IES also served as a better scoring method that had distinct score ranges for different load levels as opposed to the overlapping scores of the traditional IES method. Results showed that the k-means clustering has the potential to find groups of individuals in an unsupervised manner using the brain's hemodynamic responses and to study the underlying relationship between the TPH in groups. Using the method presented in this paper,real-time monitoring of cognitive and task performance of soldiers, and preferentially forming small units to accomplish tasks based on the insights and goals may be helpful. The results showed that WearLight can image PFC, and this study also suggests future directions for the multi-modal body sensor network (BSN) combining advanced ML algorithms for real-time state classification, cognitive and physical performance prediction, and the mitigation of performance degradation in the high-stakes environment.

Multimodal Neurophysiological Representations of High School Students’ Situational Interest: A Machine Learning Approach

Interest plays a vital role in students’ learning performance. Accurately measuring situational interest in the classroom environment is important for understanding the learning mechanism and improving teaching. However, self-report measurements frequently encounter issues of subjectivity and ambiguity, and it is hard to collect dynamic self-report scales without disturbance in the naturalistic environment. Thanks to the development of neuroscience and portable biosensors, it has become possible to represent psychological states with neurophysiological features in the classroom environment. In this study, multimodal neurophysiological signals, including electroencephalograph (EEG), electrodermal activity (EDA), and photoplethysmography (PPG), were applied to represent situational interest under both laboratory (Study 1) and naturalistic (Study 2) paradigms. A total of 33 features were extracted, and 7 different statistical indicators were calculated for each of them across all the epochs. Among these features, 47 in Study 1 and 49 in Study 2 demonstrated significant correlation with self-report situational interest. Employing a machine learning model, the analysis yielded a mean absolute error (MAE) of 0.772 and mean squared error (MSE) of 0.883 for the dataset in Study 1. However, the model was not robust on data from Study 2. These findings offer empirical support for the conceptual framework of situational interest, demonstrate the potential of neurophysiological data in educational assessments, and also highlight the challenges in naturalistic paradigm.