Behavioral Data Collection Research Articles

Few-shot Algorithms for Consistent Neural Decoding (FALCON) Benchmark.

Intracortical brain-computer interfaces (iBCIs) can restore movement and communication abilities to individuals with paralysis by decoding their intended behavior from neural activity recorded with an implanted device. While this activity yields high-performance decoding over short timescales, neural data are often nonstationary, which can lead to decoder failure if not accounted for. To maintain performance, users must frequently recalibrate decoders, which requires the arduous collection of new neural and behavioral data. Aiming to reduce this burden, several approaches have been developed that either limit recalibration data requirements (few-shot approaches) or eliminate explicit recalibration entirely (zero-shot approaches). However, progress is limited by a lack of standardized datasets and comparison metrics, causing methods to be compared in an ad hoc manner. Here we introduce the FALCON benchmark suite (Few-shot Algorithms for COnsistent Neural decoding) to standardize evaluation of iBCI robustness. FALCON curates five datasets of neural and behavioral data that span movement and communication tasks to focus on behaviors of interest to modern-day iBCIs. Each dataset includes calibration data, optional few-shot recalibration data, and private evaluation data. We implement a flexible evaluation platform which only requires user-submitted code to return behavioral predictions on unseen data. We also seed the benchmark by applying baseline methods spanning several classes of possible approaches. FALCON aims to provide rigorous selection criteria for robust iBCI decoders, easing their translation to real-world devices.

Harnessing the Strengths of Computational Advertising and Experimental Methods to Enhance Research Design

Computational methods such as those used in computational advertising research are often considered at odds with experimental methods. This divergent view of these two paths has frequently led to researchers examining these methodologies in isolation. In this article, we take the view that each path has value and that the strengths of one can inform the other. To this end, we provide a guide to conducting research that focuses on four strengths inherent to experimental research—the principle of control, random assignment of treatments, the exploration of underlying mechanisms, and transparency—that can provide lessons to those using computational advertising data. Similarly, we focus on three strengths inherent to computational advertising research—the collection of behavioral data, the realistic nature in which the data are obtained, and the ability to study dynamic changes over time—that can provide lessons on how to enhance experimental research. In doing so, we assist researchers in strengthening their study designs regardless of their preferred methodology.

Development of L1-L2 naming skills in a monolingual context: Evidence from children and adolescents

Adolescence is marked by significant developmental changes that can influence language processing and control. This study aimed to uncover developmental differences in language co-activation and control in unbalanced Spanish (L1)-English (L2) bilinguals. Children and adolescents attending bilingual schools within a L1 monolingual context completed a picture-naming task including cognates and non-cognates nouns, with collection of behavioral and ERP data. The study consistently found a cognate facilitation effect (CFE) in L2, evident in enhanced accuracy, faster reaction times, and reduced N400 negativity for cognates in comparison with no-cognate nouns. However, in L1, CFE was only observed in the N400 component, indicating weaker transfer from L2 to L1. Additionally, children exhibited greater N200 negativity when naming cognates in L1, while adolescents showed no N200 modulations, suggesting differences in frontal control region involvement and potential differences in control strategies. Language co-activation appears independent of maturation, while language control depends on development.

Exploring Campus Configuration and Patient Arrival Behaviors at Large Hospitals: An Integrated Post-Occupancy Evaluation (POE) Protocol.

This study addresses the challenges in hospital navigation, particularly focusing on the sequence of arrival experiences through foot traffic or driving within a medical campus. While previous research has emphasized wayfinding in interiors, this study recognizes the importance of transition from outdoor to indoor spaces in the hospital arrival zone. Twofold research efforts include: (1) Exploring vehicular and pedestrian behaviors in a hospital arrival zone using Space Syntax Analyses (SSA), and (2) Validating the SSA results using empirical data collected from onsite observations and behavior mapping. A series of axial map analyses were conducted on the hospital campus and building arrival zone regarding spatial connectivity and integration, depth of spaces, and wayfinding intelligibility based on vehicular and pedestrian movement paths. Systematic behavioral data collection includes vehicle shadowing (10 h) and pedestrian mapping (20 h) in the building arrival zone. Space Syntax Analyses indicates high intelligibility scores for vehicles (R = .91) and pedestrian (R = .78) circulation patterns; traffic situations were visualized via axial maps. A dataset of 219 vehicles and 2,096 pedestrian behaviors was analyzed and visualized using diagrams and heatmaps, identifying different occupancy patterns within the space. Correlational analyses indicated strong correlations between the results: connectivity and integration scores are significantly correlated with both vehicular and pedestrian traffic volumes. Preliminary findings of the study validated an integrated protocol to evaluate the impacts of campus configuration and building approach design on patient arrival and spatial navigation at large hospitals, serving as a POE protocol involving SSA.

Immersive human–computer interaction and digital entertainment new media application in English e-learning mode

With the rapid development of e-learning, more and more learners use electronic devices to learn English. The traditional way of learning is often lack of interest and interaction, therefore, this paper discusses how to improve the effect of English learning through the application of immersive human–computer interaction and digital entertainment new media. Research on the selection of appropriate virtual reality, augmented reality or game platforms, design interactive content and scenes according to learning objectives, and use elements such as virtual scenes, virtual characters and audio-visual effects to improve learners’ immersion. Through careful design and production, learners can be presented with a lively, interesting and challenging learning environment to stimulate their interest and enthusiasm in learning. Develop interactive learning tools and applications that enable learners to interact with virtual environments or games, make appropriate adjustments and feedback based on learner feedback and behavior, and provide personalized learning support and guidance. Through the collection of learner feedback and behavioral data, combined with the evaluation indicators of learning outcomes, the immersive learning environment and content can be evaluated and improved and optimized according to the evaluation results, so as to further enhance learners’ learning motivation and learning outcomes.

Integration of passive sensing technology to enhance delivery of psychological interventions for mothers with depression: the StandStrong study

Psychological interventions delivered by non-specialist providers have shown mixed results for treating maternal depression. mHealth solutions hold the possibility for unobtrusive behavioural data collection to identify challenges and reinforce change in psychological interventions. We conducted a proof-of-concept study using passive sensing integrated into a depression intervention delivered by non-specialists to twenty-four adolescents and young mothers (30% 15–17 years old; 70% 18–25 years old) with infants (< 12 months old) in rural Nepal. All mothers showed a reduction in depression symptoms as measured with the Beck Depression Inventory. There were trends toward increased movement away from the house (greater distance measured through GPS data) and more time spent away from the infant (less time in proximity measured with the Bluetooth beacon) as the depression symptoms improved. There was considerable heterogeneity in these changes and other passively collected data (speech, physical activity) throughout the intervention. This proof-of-concept demonstrated that passive sensing can be feasibly used in low-resource settings and can personalize psychological interventions. Care must be taken when implementing such an approach to ensure confidentiality, data protection, and meaningful interpretation of data to enhance psychological interventions.

Exploring Digital Biomarkers for Depression Using Mobile Technology

Introduction &amp; BackgroundWith the advent of ubiquitous sensors and mobile technologies, wearables and smartphones offer a cost-effective means for monitoring mental health conditions, particularly depression. These devices enable the continuous collection of behavioral data, providing novel insights into the daily manifestations of depressive symptoms. Objectives &amp; ApproachThe present study summarizes findings from our five recent investigations that explored the relationships between depression severity and digital biomarkers captured by wearables and smartphones. These studies analyzed data from RADAR-MDD, a multinational mobile health program, involving 623 participants and tracked for up to two years. Participants' depression severity was measured biweekly using the PHQ-8 questionnaire conducted via smartphones. Concurrently, participants’ Fitbit and smartphone data were also collected. Given the longitudinal nature and repeated measurements for each participant, multilevel modeling techniques were employed to analyze the data. Relevance to Digital FootprintsOur approach involved extracting features from passive data that reflect various aspects of daily behavior—such as sleep quality, social interaction, physical activity, and walking patterns—akin to digital footprints. ResultsWe found several significant links between depression severity and various behavioral biomarkers: elevated depression levels were associated with diminished sleep quality (assessed through Fitbit metrics), reduced sociability (approximated by Bluetooth), decreased levels of physical activity (quantified by step counts and GPS data), a slower cadence of daily walking (captured by smartphone accelerometers), and disturbances in circadian rhythms (analyzed across various data streams). Conclusions &amp; ImplicationsLeveraging digital biomarkers for assessing and continuously monitoring depression introduces a new paradigm in early detection and development of customized intervention strategies. Findings from these studies not only enhance our comprehension of depression in real-world settings but also underscore the potential of mobile technologies in the prevention and management of mental health issues.

Is pay-or-consent for privacy justifiable? Evidence from different users' privacy attitudes toward behavioral data collection in South Korea

As regulators began prohibiting online platforms from collecting personal data based on the “take-it-or-leave-it” basis, platform firms must adopt more refined user consent rules such as the pay-or-consent approach. Ensuring sufficient user options could increase the welfare of privacy-sensitive users but reduce the efficiency of data-driven business models. To balance the benefits and costs of enhanced privacy protection, regulators should understand the diversity in users' attitudes toward behavioral data collection in free online platforms. Tradeoffs among privacy, conveniences, and free services based on users' heterogeneous preferences are considered to investigate the user's different privacy attitudes in free online platforms. Three distinct user groups were found: the first one reluctantly accepts the “take-it-or-leave-it” condition because of the lack of alternatives, the second one accepts it for free services, and the third one accepts it because it does not matter. These three user segments constituted 32.9%, 47.0%, and 20.1% of all the respondents, respectively. The pay-or-consent approach can be justifiable in terms of balancing the benefits and costs of the privacy regulations if it properly reflects privacy-sensitive users' willingness to pay for privacy.

Analyzing novice and competent programmers' problem-solving behaviors using an automated evaluation system

Background and ContextIn today's tech-driven world, programming courses are crucial. Yet, teaching programming is challenging, leading to high student failure rates. Understanding student learning patterns is key, but there's a lack of research utilizing tools to automatically collect and analyze interaction data for insights into student performance and behaviors. ObjectivesStudy aims to compare problem-solving behaviors of novice and competent programmers during coding tests, identifying patterns and exploring relationships with program correctness. MethodWe built an online system with programming challenges to collect behavior data from novice and competent programmers. Our system analyzed data using various metrics to explore behavior-program correctness relationships. FindingsAnalysis showed distinct problem-solving behavior patterns. Competent programmers had fewer syntax errors, spent less time fixing bugs, and had higher program correctness. Novices made more syntax errors and spent more time fixing coding errors. Both groups used tabs for code structure, but competent programmers introduced unfamiliar variables more often and commented on them afterward. Emphasizing iterative revisions and active engagement enhances problem-solving skills and programming proficiency. Radar charts are effective for identifying improvement areas in teaching programming. The relationship between behavior and program correctness was positively correlated for competent programmers but not novices. ImplicationsStudy findings have implications for programming education. Radar charts help teachers identify course improvement areas. Novices can learn from competent programmers' behavior. Instructors should encourage continuous skill improvement through revisions and engagement. Identified unfamiliar programming aspects offer insights for targeted learning.

Innovative Application and Societal Impact of AI in Student Behavior Early Warning Systems within Smart Campuses

This study explores the innovative application and societal impact of artificial intelligence (AI) in student behavior early warning systems (EWS) within smart campuses. As information technology advances, smart campuses have become integral to modern education, and AI introduces new possibilities for EWS. Through literature review and theoretical analysis, this paper systematically reviews related studies, examining the current applications and potential societal impacts of AI in EWS. The study first defines the basic concepts of smart campuses and AI, followed by an analysis of AI's specific applications in EWS, including behavior data collection, analysis, and anomaly detection. A comprehensive review of existing research reveals that AI enhances the accuracy and timeliness of warnings and provides scientific decision support for educational administrators. However, AI's application also raises data privacy and ethical concerns, necessitating the development of relevant laws and ethical standards. The paper proposes future research directions, including optimizing AI algorithms, fostering interdisciplinary collaboration, and exploring AI applications in various educational settings. This study aims to provide theoretical support for smart campus development, promote the widespread use of AI in education, and enhance educational equity and quality.

Money is power: Carpooling stimulus with evidence from an interactive long-term laboratory experiment

Carpooling is one of the travel demand management strategies to mitigate road congestion. Incentive-Based Travel Demand Management (IBTDM) strategies are pivotal for carpool promotion by providing incentives to address inconveniences and privacy apprehensions, yet their efficacy lacks validation. Considering the constraints of incentive budget as well as the penetration rate, it is important to assess the long-term impact of incentives on carpooling, improve the programs in term of information feedback and identify the individuals who are most inclined to carpool. To achieve the goals, we conducted a laboratory experiment through WeChat applet platform focused on carpooling and departure time choices. 194 participants successfully completed the entire experiment. The experiment emphasizes several crucial elements, including long term (participants making daily travel choices in one month), the interactivity and information feedback mechanisms, instruments enhancing participant engagement (redeeming incentives at the end of the experiment, etc.), exit questionnaire to collect travelers' behavioral strategies. The findings elucidate several key insights: 1) Incentives have a significant long-term influence on encouraging travelers to carpool, and compensatory mechanisms for unsuccessful carpool matches further amplify travelers' impetus. 2) IBTDM administrations can promote carpooling by improving feedback information according to travelers' learning effect in each round, such as introducing real-time displays of the numbers of carpool drivers and passengers. 3) Drawing from the analysis of travelers’ participation, behavioral compliance and psychological strategies of carpooling, it can be inferred that individuals with higher incomes, fewer commuting days who have a solid grasp of the experimental mechanism, constitute prospective candidates for carpooling programs. This work contributes to helping IBTDM administrations clarify the target participants and develop a more reasonable carpooling program. The behavioral data collection approach based on our laboratory experiment is portable for future studies.

An executive-functions-based reading training enhances sensory-motor systems integration during reading fluency in children with dyslexia.

The Simple View of Reading model suggests that intact language processing and word decoding lead to proficient reading comprehension, with recent studies pointing at executive functions as an important component contributing to reading proficiency. Here, we aimed to determine the underlying mechanism(s) for these changes. Participants include 120 8- to 12-year-old children (n = 55 with dyslexia, n = 65 typical readers) trained on an executive functions-based reading program, including pre/postfunctional MRI and behavioral data collection. Across groups, improved word reading was related to stronger functional connections within executive functions and sensory networks. In children with dyslexia, faster and more accurate word reading was related to stronger functional connections within and between sensory networks. These results suggest greater synchronization of brain systems after the intervention, consistent with the "neural noise" hypothesis in children with dyslexia and support the consideration of including executive functions as part of the Simple View of Reading model.

Designing for Sustainability: Exploring the Intersection of Creative Arts in Digital Sustainable Ecology by Interactive Animation

This article presents doctoral research examining the intersection of creative arts and sustainable development, focusing on interactive animation in the digital sustainable ecology. The research problem addressed is the sustainability of cultural creativity within the realm of digital media ecology, expanding the traditional understanding of ecological sustainability. The study objective aims to investigate the interplay between interactive animation, sustainable development, and digital cultural communication, exploring potential approaches for designing sustainability within interactive animation. The methodology involves drawing upon media ecology theory, conducting content analysis, and performing a comprehensive literature review and case study analysis. Findings indicate that interactive animation expresses frontage scientific concepts through artistic language, promoting cultural reflection and humanistic communication in the digital ecological environment. Interactive animation facilitates the collection of objective behavioural data, integrating scientific concepts into creative design and iterative research for sustainability. The research highlights the significance of creative arts in addressing challenges and opportunities associated with sustainable technology advancement. Exploring the intersection of creative arts and sustainable development via interactive animation, the study deepens participants' understanding of the art form which engages them in the realms of art and science. Implications include fostering cultural spectacle, promoting humanistic meditation, and integrating scientific concepts into creative design, ultimately contributing to sustainability in the digital ecological environment. Contributions involve providing insights into the potential of interactive animation as an academic research tool, expanding ecological sustainability understanding, and emphasizing the role of creative arts in advancing sustainable technology.

Generating trajectory data without behavior modeling: An online virtual reality method in wayfinding performance evaluation for buildings

Wayfinding performance is one of the key issues in pre- or post-occupancy evaluation for large-scale public buildings and such evaluation typically revolves around participants’ wayfinding trajectories. Traditional evaluation methods, including laboratory virtual reality (VR) experiments and computer simulations, have their respective advantages in accuracy or cost-efficiency. However, they either suffer from high costs and low efficiency or sacrifice the diversity of behavioral data for higher efficiency and lower costs. Therefore, this paper proposes a novel method named “sampling as simulation” (SaS) via an online VR wayfinding experiment. This method allows for the rapid collection of behavioral data at a lower cost to generate highly accurate wayfinding trajectory data for evaluation purposes. It utilizes a model-image integrated approach to create realistic online virtual environments and employs a spatiotemporal replay algorithm to generate a crowd of true-trajectory-driven avatars, significantly enhancing the immersion and realism of the online virtual scenes. This method was demonstrated and validated with a wayfinding performance post-occupancy evaluation (POE) project at the International Departure of Satellite Terminal One (S1) in Shanghai Pudong International Airport (PVG). In this example, a total of 13,712 original trajectories were collected within a 72-h time window, serving as the original dataset for evaluation. Based on this dataset, the study effectively simulated wayfinding behavior in three different scenarios and evaluated wayfinding performance. Compared to existing methods, the SaS method not only improves data collection efficiency and reduces costs but also maintains the diversity of behavior by generating transformed trajectories directly from human data without behavior modeling.

Ethical considerations for integrating multimodal computer perception and neurotechnology.

Artificial intelligence (AI)-based computer perception technologies (e.g., digital phenotyping and affective computing) promise to transform clinical approaches to personalized care in psychiatry and beyond by offering more objective measures of emotional states and behavior, enabling precision treatment, diagnosis, and symptom monitoring. At the same time, passive and continuous nature by which they often collect data from patients in non-clinical settings raises ethical issues related to privacy and self-determination. Little is known about how such concerns may be exacerbated by the integration of neural data, as parallel advances in computer perception, AI, and neurotechnology enable new insights into subjective states. Here, we present findings from a multi-site NCATS-funded study of ethical considerations for translating computer perception into clinical care and contextualize them within the neuroethics and neurorights literatures. We conducted qualitative interviews with patients (n = 20), caregivers (n = 20), clinicians (n = 12), developers (n = 12), and clinician developers (n = 2) regarding their perspective toward using PC in clinical care. Transcripts were analyzed in MAXQDA using Thematic Content Analysis. Stakeholder groups voiced concerns related to (1) perceived invasiveness of passive and continuous data collection in private settings; (2) data protection and security and the potential for negative downstream/future impacts on patients of unintended disclosure; and (3) ethical issues related to patients' limited versus hyper awareness of passive and continuous data collection and monitoring. Clinicians and developers highlighted that these concerns may be exacerbated by the integration of neural data with other computer perception data. Our findings suggest that the integration of neurotechnologies with existing computer perception technologies raises novel concerns around dignity-related and other harms (e.g., stigma, discrimination) that stem from data security threats and the growing potential for reidentification of sensitive data. Further, our findings suggest that patients' awareness and preoccupation with feeling monitored via computer sensors ranges from hypo- to hyper-awareness, with either extreme accompanied by ethical concerns (consent vs. anxiety and preoccupation). These results highlight the need for systematic research into how best to implement these technologies into clinical care in ways that reduce disruption, maximize patient benefits, and mitigate long-term risks associated with the passive collection of sensitive emotional, behavioral and neural data.

Sociological interventions for the diphtheria outbreak in Nigeria: addressing misconceptions, cultural biases, and promoting vaccination

Diphtheria is a fatal bacterial infection primarily affecting the respiratory system, characterized by a thick membrane formation in the throat, caused by Corynebacterium diphtheriae. This manuscript elucidates sociological interventions, aimed at protecting the population of Nigeria from diphtheria, a preventable yet persisting health threat. It delves into diverse strategies that could be employed to combat misconceptions, navigate deeply ingrained cultural biases, and instill the significance of vaccination within the context of diphtheria outbreak and prevention. The research question was: "What are the sociological interventions, used to address misconceptions, cultural biases, and promote vaccination in the context of diphtheria prevention in Nigeria?" In response to this inquiry, a comprehensive review methodology was employed to systematically gather, evaluate, and synthesize relevant literature outputs. The literature search, conducted across diverse academic databases and repositories, yields a rich collection of studies, reports, and case studies. The lens of Symbolic Interactionism was applied. The sociological interventions, considered in this context, engage directly with communities, healthcare providers, and local leaders. It addresses the vital need to combat misconceptions, cultural biases, and vaccine hesitancy, fostering a deeper understanding of the dynamic interplay between sociological factors and healthcare interventions. Sociological interventions for diphtheria protection in Nigeria require culturally sensitive approaches, community engagement, health education, addressing vaccine hesitancy, offering accessible vaccination services, tackling socioeconomic barriers, targeted messaging, behavioral change communication, data collection, and research collaborations to understand and address sociocultural factors influencing vaccine uptake and prevention. The findings and implications offer valuable insights for policymakers, healthcare providers, and researchers, and lay the groundwork for more effective strategies to protect the population from this preventable disease

Applying collocation and APRIORI analyses to chimpanzee diets: Methods for investigating nonrandom food combinations in primate self-medication.

Identifying novel medicinal resources in chimpanzee diets has historically presented challenges, requiring extensive behavioral data collection and health monitoring, accompanied by expensive pharmacological analyses. When putative therapeutic self-medicative behaviors are observed, these events are often considered isolated occurrences, with little attention paid to other resources ingested in combination. For chimpanzees, medicinal resource combinations could play an important role in maintaining well-being by tackling different symptoms of an illness, chemically strengthening efficacy of a treatment, or providing prophylactic compounds that prevent future ailments. We call this concept the self-medicative resource combination hypothesis. However, a dearth of methodological approaches for holistically investigating primate feeding ecology has limited our ability to identify nonrandom resource combinations and explore potential synergistic relationships between medicinal resource candidates. Here we present two analytical tools that test such a hypothesis and demonstrate these approaches on feeding data from the Sonso chimpanzee community in Budongo Forest, Uganda. Using 4 months of data, we establish that both collocation and APRIORI analyses are effective exploratory tools for identifying binary combinations, and that APRIORI is effective for multi-item rule associations. We then compare outputs from both methods, finding up to 60% agreement, and propose APRIORI as more effective for studies requiring control over confidence intervals and those investigating nonrandom associations between more than two resources. These analytical tools, which can be extrapolated across the animal kingdom, can provide a cost-effective and efficient method for targeting resources for further pharmacological investigation, potentially aiding in the discovery of novel medicines.

Construction of Cloud Computing Resource Allocation and Ideological and Political Education Platform Based on the Theme of Party History and Party Building

Abstract Party history and party-building education is an important carrier of party-building work in colleges and universities, and it is also an important content of cultivating and promoting socialist core values while party-building work is an effective way and the main hand of ideological and political work of students in colleges and universities. This paper takes the large amount of data generated in the process of ideological and political education resource allocation on the theme of party history and party building as the research object and builds a mapping relationship between tasks and ideological and political education resources according to certain strategies based on cloud computing resource allocation. A platform for allocating resources for ideological and political education has been designed and developed, which is based on a time series clustering algorithm and constructs resource overviews, sequence collections, and clustering modules. The functions of student behavior data collection and overview, feature calculation and selection, behavior time series clustering, and group feature analysis are realized, which enhances the efficiency of the ideological and political education resource platform. Functional and performance tests of the platform were completed, and the average system response times of the three modules were 75.05, 80.39, and 69.95ms, which were able to meet the actual application standards. With the help of the platform, the behavioral record data of 6,927 platform users were evaluated and practiced, and the user group classification and group characterization were carried out according to the output of the platform, which provided scientific suggestions for the distribution of ideological and political teaching resource services.

A Real-Time Approach for Assessing Rodent Engagement in a Nose-Poking Go/No-Go Behavioral Task Using ArUco Markers.

Behavioral neuroscience requires precise and unbiased methods for animal behavior assessment to elucidate complex brain-behavior interactions. Traditional manual scoring methods are often labor-intensive and can be prone to error, necessitating advances in automated techniques. Recent innovations in computer vision have led to both marker- and markerless-based tracking systems. In this protocol, we outline the procedures required for utilizing Augmented Reality University of Cordoba (ArUco) markers, a marker-based tracking approach, to automate the assessment and scoring of rodent engagement during an established intracortical microstimulation-based nose-poking go/no-go task. In short, this protocol involves detailed instructions for building a suitable behavioral chamber, installing and configuring all required software packages, constructing and attaching an ArUco marker pattern to a rat, running the behavioral software to track marker positions, and analyzing the engagement data for determining optimal task durations. These methods provide a robust framework for real-time behavioral analysis without the need for extensive training data or high-end computational resources. The main advantages of this protocol include its computational efficiency, ease of implementation, and adaptability to various experimental setups, making it an accessible tool for laboratories with diverse resources. Overall, this approach streamlines the process of behavioral scoring, enhancing both the scalability and reproducibility of behavioral neuroscience research. All resources, including software, 3D models, and example data, are freely available at https://github.com/tomcatsmith19/ArucoDetection. Key features • The ArUco marker mounting hardware is lightweight, compact, and detachable for minimizing interference with natural animal behavior. • Requires minimal computational resources and commercially available equipment, ensuring ease of use for diverse laboratory settings. • Instructions for extracting necessary code are included to enhance accessibility within custom environments. • Developed for real-time assessment and scoring of rodent engagement across a diverse array of pre-loaded behavioral tasks; instructions for adding custom tasks are included. • Engagement analysis allows for the quantification of optimal task durations for consistent behavioral data collection without confirmation biases.

Population level behavioural responses to variation in habitat quality across multiple locations in a fragmented landscape

Population level behavioural responses to variation in habitat quality across multiple locations in a fragmented landscape