Behavior Pattern Research Articles

A dynamic simulation model for building cooling and heating energy considering climate-building system-occupant interactions

Residential buildings have a significant potential for energy conservation. The building energy consumption process is a complex system influenced by several factors at climate, building system, and occupant levels. This study introduces a novel model based on a multi-agent system to systematically integrate these multiple influencing factors, simulate their interactions, and estimate the resultant cooling and heating energy with dynamic variations over years considered. The automatic dynamic energy simulation was conducted over 30 years considering four temporal parameters (i.e., outdoor temperature, heat transfer coefficient of the building envelope, household type, and behavior pattern of occupants). A case building was used to validate the model, and the dynamic annual results were quite different from the static ones. The dynamic cumulative cooling energy consumption during 31 years was 0.69 % lower than the static one, whereas dynamic cumulative heating energy was 7.53 % higher compared with static energy. It highlighted the importance of considering dynamic multi-agent interactions. Besides, the roles of multiple energy influencing factors and dynamic parameters were compared and discussed. This study provides critical insights into improving building energy performance and offers a rapid and automatic approach for dynamic energy simulation.

Temporal Matrix Factorization: A polynomial approach to latent factor estimation

Matrix Factorization (MF) is a simple and efficient Machine Learning (ML) technique to discover latent factors that help in explaining the underlying behaviour of actors (for instance, in the domain of recommender systems the actors could be users/items). The technique uses observed data, generated by the actors, to derive the latent factors. With time, more and more observed data that spans across a wide range of time period gets accumulated. The abundance of this data, allows us to discover new behavioural patterns. One such pattern is that, the latent factor values change with time. Most of the existing matrix factorization techniques are agnostic to time of occurrence of the event and fail to foresee the temporal changes in the actor behaviour. Deep neural networks are effective in predicting the recommendations with time, however, the ability to explain the underlying pattern and the associated rationale is poor. In our work, we propose a novel matrix factorization technique to estimate the temporal changes in the user behaviour and demonstrate its effectiveness in achieving time dependent recommendations.

Parent-Child Interaction Therapy with Separated Parents of a 3-Year-Old Child with Conduct Problems and Callous-Unemotional Traits

Callous and unemotional (CU) traits are associated with an earlier onset of conduct problems in children. The presence of CU traits typically predicts a severe, stable, and aggressive pattern of behavior, especially when the onset of such behaviors begins in childhood. Children with conduct problems and CU continue to exhibit challenging behavior despite implementation of behavior management strategies compared to children with conduct problems alone. An emerging body of research suggests that Parent-Child Interaction Therapy (PCIT) specifically tailored to address the characteristics unique to CU traits (PCIT-CU) may be an effective intervention for children with conduct problems + CU. This case study presents the treatment of a 3-year-old boy using PCIT-CU who presents with oppositional defiant disorder (ODD), attention-deficit/hyperactivity disorder—combined presentation (ADHD-C), and CU traits. Both caregivers participated but were separated. Following the completion of treatment, conduct problems improved, parent ratings of CU traits decreased, and positive parenting practices increased. This case study adds to the growing literature in support of tailoring behavioral management training interventions for children with conduct problems + CU.

Flexural behavior of coral aggregate concrete beams reinforced with BFRP bars under seawater corrosion environments

The combined utilization of fiber-reinforced polymer (FRP) composites with coral aggregate concrete (CAC) in remote island areas contributes to the reduced construction cost and construction period, offering a promising application. However, the evolution pattern and deterioration mechanism of flexural behavior for FRP bars reinforced CAC beams in marine service environments remains unclear. Thus, this paper investigates the flexural behavior of basalt-FRP (BFRP) bars reinforced CAC beams under seawater immersion and dry-wet cycle conditions using laboratory-accelerated aging methods. The research considers the effects of exposure temperature and corrosion age on their failure modes, flexural stiffness, ultimate loading capacity, and deformation deflection. The experimental results revealed that with the increase in corrosion age and exposure temperature, the amount of vertical cracks of CAC beams decreased and their crack depth also exhibited a slight reduction, but their crack width and spacing at failure significantly increased. After being attacked by seawater environments, the load-deflection curves of CAC beams experienced an enhanced slope of the ascending section (i.e., flexural stiffness), but this strengthened flexural stiffness did not result in an enhancement in the ultimate load capacity. Instead, with prolonged corrosion and higher temperatures, the ultimate load of CAC beams displayed significant degradation, accompanied by reduced mid-span deflection. After 12 months of seawater exposure to wet-dry cycle conditions at 60 °C, the ultimate load and mid-span deflection of CAC beams degraded by approximately 25.0 % and 56.6 %, respectively. The study also predicted the flexural bearing capacity of CAC beams utilizing existing specifications for FRP-reinforced normal aggregate concrete (NAC). It was concluded that the formulae for flexural loading capacity for FRP-reinforced NAC beams were still suitable for CAC beams.

Ordinal processing differences between children with persistent dyscalculia and typically performing children.

Ordinal number processing skills are important for adults and children. Recent work demonstrates that children have difficulty with judging the ordinality of sequences that are in-order but do not match the typical count-list (i.e., in-order non-adjacent sequences, such as 2-4-6). Limited evidence in the literature suggests that dyscalculic children show a similar pattern of behavior. In the present study, we sought to explicitly test the hypothesis that children with developmental dyscalculia struggle primarily with extending notions of ordinality to sequences outside of the count-list. We test this hypothesis using a sample of children with persistent developmental dyscalculia, and a comparison group of typically performing children. Both groups completed an ordinality judgment task, in which triplet sequences were judged as being in-order (e.g., 3-4-5; 2-4-6) or in mixed-order (e.g., 3-5-4; 2-6-4). In line with our prediction, results demonstrate that children with persistent developmental dyscalculia make more errors, compared to typically performing children, but only on the in-order non-adjacent trials (e.g., 2-4-6). Broadly, this finding suggests that ordinality processing abilities are impaired in children with developmental dyscalculia, and that this characteristic appears primarily in extending notions of ordinality beyond adjacent sequences. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Deep learning-based authentication for insider threat detection in critical infrastructure

In today’s cyber environment, threats such as data breaches, cyberattacks, and unauthorized access threaten national security, critical infrastructure, and financial stability. This research addresses the challenging task of protecting critical infrastructure from insider threats because of the high level of trust and access these individuals typically receive. Insiders may obtain a system administrator’s password through close observation or by deploying software to gather the information. To solve this issue, an innovative artificial intelligence-based methodology is proposed to identify a user by their password’s keystroke dynamics. This paper also introduces a new Gabor Filter Matrix Transformation method to transform numerical values into images by revealing the behavioral pattern of password typing. A siamese neural network (SNN) with the branches of convolutional neural networks is utilized for image comparison, aiming to detect unauthorized attempts to access critical infrastructure systems. The network analyzes the unique features of a user’s password timestamps transformed into images and compares them with previously submitted user passwords. The obtained results indicate that transforming the numerical values of keystroke dynamics into images and training an SNN leads to a lower equal error rate (EER) and higher user authentication accuracy than those previously reported in other studies. The methodology is validated on publicly available keystroke dynamics collections, the CMU and GREYC-NISLAB datasets, which collectively comprise over 30,000 password samples. It achieves the lowest EER value of 0.04545 compared to state-of-the-art methods for transforming non-image data into images. The paper concludes with a discussion of findings and potential future directions.

3D-CNN Method for Drowsy Driving Detection Based on Driving Pattern Recognition

Drowsiness impairs drivers’ concentration and reaction time, doubling the risk of car accidents. Various methods for detecting drowsy driving have been proposed that rely on facial changes. However, they have poor detection for drivers wearing a mask or sunglasses, and they do not reflect the driver’s drowsiness habits. Therefore, this paper proposes a novel method to detect drowsy driving even with facial detection obstructions, such as masks or sunglasses, and regardless of the driver’s different drowsiness habits, by recognizing behavioral patterns. We achieve this by constructing both normal driving and drowsy driving datasets and developing a 3D-CNN (3D Convolutional Neural Network) model reflecting the Inception structure of GoogleNet. This binary classification model classifies normal driving and drowsy driving videos. Using actual videos captured inside real vehicles, this model achieved a classification accuracy of 85% for detecting drowsy driving without facial obstructions and 75% for detecting drowsy driving when masks and sunglasses are worn. Our results demonstrate that the behavioral pattern recognition method is effective in detecting drowsy driving.

Robustness and dynamical features of fractional difference spacecraft model with Mittag–Leffler stability

Abstract Spacecraft models that mimic the Planck satellite’s behaviour have produced information on cosmic microwave background radiation, assisting physicists in their understanding of the composition and expansion of the universe. For achieving the intended formation, a framework for a discrete fractional difference spacecraft model is constructed by the use of a discrete nabla operator of variable order containing the Mittag–Leffler kernel. The efficacy of the suggested framework is evaluated employing a numerical simulation of the concerning dynamic systems of motion while taking into account multiple considerations such as exterior disruptions, parameterized variations, time-varying feedback delays, and actuator defects. The implementation of the Banach fixed-point approach provides sufficient requirements for the presence of the solution as well as a distinctive feature for such mechanisms Furthermore, the consistent stability is examined. With the aid of discrete nabla operators, we monitor the qualitative behavioural patterns of spacecraft systems to provide justification for structure’s chaos. We acquire the fixed points of the proposed trajectory. At each fixed point, we calculate the eigenvalue of the spacecraft system’s Jacobian matrix and check for zones of instability. The outcomes exhibit a wide range of multifaceted behaviours resulting from the interaction with various fractional orders in the offered system. To maintain stability and synchronize the system, nonlinear controllers are additionally provided. The study highlights the technique’s vulnerability to fractional-order factors, resulting in exclusive, changing trends and equilibrium frameworks. Because of its diverse and convoluted behaviour, the spacecraft chaotic model is an intriguing and crucial subject for research.

Recession: A Conceptual Case Study Of Its Domino Effect On Economic And Psychological Capital

In this paper, the study will try to understand the conceptual framework of recession through its economic philosophy, psychology, and its narratives on society and livelihood. The behavioral pattern of a recession can be based on many variables like assets, credit volume, unemployment, etc. but these also can be the effect of a recession rather than the cause of the recession. Prediction of the severity of a recession is still a far-fetched concept as experts are still finding it difficult to unfold the formula of the accurate forecast of the same. The word recession itself has a domino sequel on all aspects of functional, execution, and consumption-oriented stakeholders of any country.

Comparing spatial differences in behavioral pattern transition of Black-tailed gull during post breeding season in the Korean peninsula

Parents adjust their foraging effort according to the chick and their own body condition and dual foraging strategy is one of the foraging tactics parents replenish their own reserves while feeding their chicks. During the post-breeding season, seabirds disperse to recover their own body condition and prepare for the next breeding season. Recently, we discovered Black-tailed gulls (Larus crassirostris) breeding around the Korean Peninsula occasionally foraging long trips during the late fledging, however, our understanding of the behavioral patterns of Black-tailed gulls during the late fledging and post-breeding, as well as its inter-colonial differences, remains considerably limited. Here, we employed 92 GPS trackers to adult Black-tailed gulls (Larus crassirostris) from six breeding colonies around the Korean peninsula (Yellow Sea—three colonies, South Sea—one colony, and East Sea—two colonies). To determine the foraging investment during the fledging, we suggested the flight efficiency in each trip as the ratio of maximum foraging distance (i.e., straight line distance) to total foraging distance (i.e., sum of all consecutive distance for each trip). Overall, the mean flight efficiency of the long foraging trips were lower than 57% (40.76 ± 13.07%) whereas that of the short foraging trips were over 74% (80.87 ± 4.03%). This may suggest that Black-tailed gulls may visited more than one foraging site during the long foraging trip while they flew directly between the foraging site and breeding colony during the short foraging trip to invest more in their juvenile. Moreover, longer maximum foraging distance with higher flight efficiency observed in the East Sea may indicate a balance between the costs (such as energy expended during foraging or food competition near breeding sites) and the benefits (quantity and quality of food obtained). Our findings revealed the flight behavior of Black-tailed gulls during the late fledging and post breeding, across six breeding colonies, which have different competition pressures and proximity to foraging site.

Recreational ball games are effective in improving social communication impairments among preschoolers diagnosed with autism spectrum disorder: a multi-arm controlled study

BackgroundThis study aimed to compare the effects of two 12-week training intervention experimental ball games combined with standard behavioral rehabilitation against a control group solely utilizing standard behavioral rehabilitation on social communication impairments (SCI) in preschool children with Autism Spectrum Disorder (ASD).MethodsA multi-arm controlled study design was implemented, involving 41 children diagnosed with ASD (mean age: 4.99 ± 0.76 years). 41 participants were randomized assigned to two experimental groups and a control group, The experimental group carried out ball combination training program group (BCTP) and mini-basketball training program group (MBTP) on the basis of routine behavioral rehabilitation, which underwent 12-week training interventions 5 times a week. The control group (n = 14) received only standard behavioral rehabilitation. Evaluations were conducted before and after interventions using the Social Responsiveness Scale, Second Edition (SRS-2).ResultsThe results suggest that both 12-week interventions, BCTP, and MBTP, led to significant improvements in social communication impairment among children with ASD (p < 0.05). Despite enhancing the overall scores on the SRS-2, these interventions displayed varying impacts across different sub-dimensions. BCTP primarily exhibited significant enhancements in social awareness and behavior pattern (p < 0.05), whereas MBTP significantly improved social cognition and social communication (p < 0.05). Both interventions showed slight improvements in social motivation.ConclusionsThe utilization of recreational ball games has showed to be effective in decreasing the impairment levels of children with ASD, while the control group experienced a worsening of outcomes. This suggests that irrespective of the specific ball game strategy employed, both can be employed on a weekly basis to complement standard behavioral rehabilitation and enhance the ability to improve the quality of life for children diagnosed with ASD.Trial registrationThe trial is retrospectively registered on the Chinese Clinical Trial Registry (ChiCTR1900024973;August 5, 2019).

TripChain2RecDeepSurv: A novel framework to predict transit users’ lifecycle behavior status transitions for user management

Transit users’ lifecycle behavior pattern transition reflects the continuous and multi-phase changes in how frequently and regularly users utilize public transit over their lifetime. Predicting transit users’ lifecycle behavior pattern transition is vital for enhancing the efficiency and responsiveness of transportation systems. Thus, this study incorporates lifecycle analysis in predicting long-term sequential behavioral pattern transition processes to go beyond just examining user churning at a single point in time. Specifically, this study proposes the TripChain2RecDeepSurv, a novel model that pioneers the individual-level analysis of lifecycle behavior status transitions (LBST) within public transit systems. The TripChain2RecDeepSurv is composed of (1) the TripChain2Vec module for encoding transit users’ trip chains; (2) the self-attention Transformer module for exploring the latent features related to spatiotemporal patterns; (3) the recurrent deep survival analysis module for predicting LBSTs. We demonstrate TripChain2RecDeepSurv’s predictive performance for empirical analysis by employing Shenzhen Bus data. Our model achieves a 74.39% accuracy rate in churn determination and over 80% accuracy in status sequence identification on the churn path. In addition, our findings highlight the segmented nature of Kaplan-Meier curves and identify the optimal intervention time against the user churning process. Meanwhile, the proposed model provides individual-level heterogeneity analysis, which emphasizes the significance of customizing user engagement strategies, advocating for interventions that extend users’ engagement in patterns with high-frequency transit usage to curb the transition to less frequent travel usage.

Lateralization of Feeding Behaviour in White-Fronted Lemur (Eulemur albifrons) and Ring-Tailed Lemur (Lemur catta) in Captivity

Introduction: Functional cerebral asymmetry is reflected in the lateralization of some behavioural patterns in many vertebrate species. In primates, behavioural lateralization has been related to both life style and age and sex, and it affects behaviours such as feeding and other tasks that require precision movements. Methods: We have studied feeding lateralization concerning the use of right and left hand to take the food in two species of lemurs in captivity, the mainly arboreal white-fronted lemur and the more terrestrial ring-tailed lemur, taking also account the age and the sex of the individuals. We calculated for each individual the hand preference (if it was the case) by means of z scores, and the strength of such preference using the handedness index (HI). Finally, we determined for each species the existence of right/left bias at the group level with the t Student test. Results: Half of the white-fronted lemurs (7 of 14) showed lateralization in feeding, while only a few ring-tailed lemurs (3 of 19) showed it. In the first species, a light bias seems to emerge (5 individuals used mostly the right hand for taking the food, while only 2 used mainly the left hand), while in the second species no bias could really be appreciated. Conclusion: Feeding lateralization was more accentuated in white-fronted lemur, in which a light bias towards the use of the right hand seems to be evidenced. No clear effect of age and sex on the presence and direction of lateralization could be evidenced. The results somehow contrast with what the postural theory of lateralization postulates about the preferential use of the right hand in terrestrial species.

A Survey on the Use of Large Language Models (LLMs) in Fake News

The proliferation of fake news and fake profiles on social media platforms poses significant threats to information integrity and societal trust. Traditional detection methods, including rule-based approaches, metadata analysis, and human fact-checking, have been employed to combat disinformation, but these methods often fall short in the face of increasingly sophisticated fake content. This review article explores the emerging role of Large Language Models (LLMs) in enhancing the detection of fake news and fake profiles. We provide a comprehensive overview of the nature and spread of disinformation, followed by an examination of existing detection methodologies. The article delves into the capabilities of LLMs in generating both fake news and fake profiles, highlighting their dual role as both a tool for disinformation and a powerful means of detection. We discuss the various applications of LLMs in text classification, fact-checking, verification, and contextual analysis, demonstrating how these models surpass traditional methods in accuracy and efficiency. Additionally, the article covers LLM-based detection of fake profiles through profile attribute analysis, network analysis, and behavior pattern recognition. Through comparative analysis, we showcase the advantages of LLMs over conventional techniques and present case studies that illustrate practical applications. Despite their potential, LLMs face challenges such as computational demands and ethical concerns, which we discuss in more detail. The review concludes with future directions for research and development in LLM-based fake news and fake profile detection, underscoring the importance of continued innovation to safeguard the authenticity of online information.

An IMU-based machine learning approach for daily behavior pattern recognition in dairy cows

Technological advancements have revolutionized livestock farming, notably in health monitoring. Traditional methods, which have been criticized for subjectivity and treatment delays, can be replaced with efficient health monitoring systems, thereby reducing costs and workload. Implementing cow behavior recognition allows for effective dairy cow health monitoring. In this research, we propose an integrated system using inertial measurement unit (IMU) devices and machine learning techniques for dairy cow behavior recognition. Six main dairy cow behaviors were studied: lying, standing, walking, drinking, feeding, and ruminating. All behavior types were manually labeled into the IMU data by reviewing the recorded footage. The labeled IMU data underwent four processing steps: selecting different window sizes, feature extraction, feature selection, and normalization. These processed data were then used to build the behavior recognition model. Various model structures, including SVM, Random Forest, and XGBoost, were tested. The top-performing model, XGBoost, with its proposed 58 features achieved an F1-score of 0.87, with specific scores of 0.93 for lying, 0.85 for walking, 0.94 for ruminating, 0.89 for feeding, 0.86 for standing, 0.93 for drinking, and 0.59 for other activities. During our online testing, we observed similar patterns for each healthy cow. The cumulative time spent on each behavior also matched the statistics from previous surveys. Additionally, our backend optimization approach resulted in a final overall percentage error of 1.55 % per day during online testing. In conclusion, our study presents an IMU-based system capable of accurately recognizing dairy cow behavior. Feature design and appropriate models are proposed herein. A functional optimization method was introduced indicating that our system has the potential with applications for estrus detection and other reproductive management practices in the dairy industry.

Quantitative index for temporal and spatial patterns of occupant behavior based on VRF big data

The energy consumption required to cool buildings has increased significantly because of occupant behavior. The air conditioning (AC) usage in residential buildings, particularly the part-time and part-space patterns, has a considerable impact on energy consumption. However, the part-time, part-space patterns of occupant behavior, which are characterized by complexity and diversity, present significant challenges when quantitatively assessing their impact on cooling energy consumption. To accurately describe occupant behavior, this study introduces the quantitative index of the part-time, part-space ratio of AC usage. We conducted clustering analysis based on part-time and part-space ratios of AC usage to obtain the typical occupant behavior patterns, user distribution, and cooling energy consumption levels of variable refrigerant flow (VRF) AC systems in various climatic zones of China based on data from 4373 VRF systems across various climatic zones from June to October 2019. The main findings were that the typical occupant behavior pattern of AC usage for residential buildings was the part-time, part-space AC mode, with less than 500 cooling hours and a simultaneous use coefficient below 0.6. The part-time, part-space ratio of AC usage effectively correlated occupant behavior with cooling energy consumption, as evidenced by an R2 value of 0.91, compared with the traditional standard of using the duration of the AC operation. The part-time, part-space ratio of AC usage and cooling energy consumption were higher in the hot summer and warm winter regions than those in the other two climatic zones. These findings can assist in the evaluation of cooling energy consumption for various occupant behavior patterns and provide technical guidance for reducing cooling energy consumption.

Behavioural Patterns and Their Relationship to Autism Children's Eating Problems

Behavioural Patterns and Their Relationship to Autism Children's Eating Problems

Asymmetric Adaption in Social Learning: Understanding the Dilemma of Competition and Cooperation.

The constant challenge in social interactions involves making informed decisions in the face of competitive and cooperative dilemmas. The decision-making process can be influenced by various factors present in the social context. According to the behavior-pattern-categorization framework of information acquisition, potential biases may develop at all stages of decision-making as information about social context is progressively entered and integrated. In this study, employing the Chicken Game, we investigated the influence of varying information levels within the behavior-pattern-categorization framework (i.e., competitiveness of behavior choice, uncertainty of behavior pattern, and sociality of category) on decision-making in the dilemma of competition and cooperation. Combined with reinforcement learning models, our findings from three experiments showed that participants exhibited basic complementary behavior, becoming less competitive against highly competitive opponents and vice versa. Notably, individuals exhibited varying adaptation rates to different levels of opponent competitiveness and fluctuations. Specifically, participants adapted slower to highly competitive opponents and faster to cooperative opponents. This asymmetric adaptation in social learning is related to the rate at which various levels of information are updated. The current study disentangles the different levels of information acquisition and highlights the asymmetric processing that can occur during the updating of information within each level.

Physical activity based on daily steps in patients with chronic musculoskeletal pain: evolution and associated factors

BackgroundPeople with chronic musculoskeletal pain (CMSP) often have low physical activity. Various factors can influence the activity level. The aim of this study was to monitor physical activity, assessed by the number of steps per day, over time in people with CMSP and identify factors that could be associated with this activity feature.MethodsThis prospective study involved people undergoing rehabilitation following an orthopedic trauma that had led to CMSP. At entry, participants completed self-reported questionnaires assessing pain, anxiety, depression, catastrophyzing, kinesiophobia, and behavioural activity patterns (avoidance, pacing and overdoing). They also underwent functional tests, assessing walking endurance and physical fitness. To determine daily step counts, participants wore an accelerometer for 1 week during rehabilitation and 3 months post-rehabilitation. The number of steps per day was compared among three time points: weekend of rehabilitation (an estimate of pre-rehabilitation activity; T1), weekdays of rehabilitation (T2), and post-rehabilitation (T3). Linear regression models were used to analyze the association between daily steps at T2 and at T3 and self-reported and performance-based parameters.ResultsData from 145 participants were analyzed. The mean number of steps was significantly higher during T2 than T1 and T3 (7323 [3047] vs. 4782 [2689], p < 0.001, Cohen’s d = 0.769, and 4757 [2680], p < 0.001, Cohen’s d = 0.693), whereas T1 and T3 results were similar (p = 0.92, Cohen’s d = 0.008). Correlations of number of steps per day among time points were low (r ≤ 0.4). Multivariable regression models revealed an association between daily steps at T2 and pain interfering with walking, anxiety and overdoing behaviour. Daily steps at T3 were associated with overdoing behaviour and physical fitness.ConclusionsDespite chronic pain, people in rehabilitation after an orthopedic trauma increased their physical activity if they were given incentives to do so. When these incentives disappeared, most people returned to their previous activity levels. A multimodal follow-up approach could include both therapeutic and environmental incentives to help maintain physical activity in this population.

Behavioural compatibility, not fear, best predicts the looking patterns of chacma baboons

Animal vigilance is often investigated under a narrow set of scenarios, but this approach may overestimate its contribution to animal lives. A solution may be to sample all looking behaviours and investigate numerous competing hypotheses in a single analysis. In this study, using a wild group of habituated chacma baboons (Papio ursinus griseipes) as a model system, we implemented a framework for predicting the key drivers of looking by comparing the strength of a full array of biological hypotheses. This included methods for defining individual-specific social threat environments, quantifying individual tolerance to human observers, and incorporating predator resource selection functions. Although we found evidence supporting reactionary and within-group (social) vigilance hypotheses, risk factors did not predict looking with the greatest precision, suggesting vigilance was not a major component of the animals’ behavioural patterns generally. Instead, whilst some behaviours constrain opportunities for looking, many shared compatibility with looking, alleviating the pressure to be pre-emptively vigilant for threats. Exploring looking patterns in a thorough multi-hypothesis framework should be feasible across a range of taxa, offering new insights into animal behaviour that could alter our concepts of fear ecology.