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Klapper, André, Richard Ramsey, Daniël Wigboldus, and Emily S. Cross. "The Control of Automatic Imitation Based on Bottom–Up and Top–Down Cues to Animacy: Insights from Brain and Behavior." Journal of Cognitive Neuroscience 26, no. 11 (November 2014): 2503–13. http://dx.doi.org/10.1162/jocn_a_00651.
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Humans automatically imitate other people's actions during social interactions, building rapport and social closeness in the process. Although the behavioral consequences and neural correlates of imitation have been studied extensively, little is known about the neural mechanisms that control imitative tendencies. For example, the degree to which an agent is perceived as human-like influences automatic imitation, but it is not known how perception of animacy influences brain circuits that control imitation. In the current fMRI study, we examined how the perception and belief of animacy influence the control of automatic imitation. Using an imitation–inhibition paradigm that involves suppressing the tendency to imitate an observed action, we manipulated both bottom–up (visual input) and top–down (belief) cues to animacy. Results show divergent patterns of behavioral and neural responses. Behavioral analyses show that automatic imitation is equivalent when one or both cues to animacy are present but reduces when both are absent. By contrast, right TPJ showed sensitivity to the presence of both animacy cues. Thus, we demonstrate that right TPJ is biologically tuned to control imitative tendencies when the observed agent both looks like and is believed to be human. The results suggest that right TPJ may be involved in a specialized capacity to control automatic imitation of human agents, rather than a universal process of conflict management, which would be more consistent with generalist theories of imitative control. Evidence for specialized neural circuitry that “controls” imitation offers new insight into developmental disorders that involve atypical processing of social information, such as autism spectrum disorders.
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Chatterjee, Ayan, Martin Gerdes, Andreas Prinz, and Santiago Martinez. "Human Coaching Methodologies for Automatic Electronic Coaching (eCoaching) as Behavioral Interventions With Information and Communication Technology: Systematic Review." Journal of Medical Internet Research 23, no. 3 (March 24, 2021): e23533. http://dx.doi.org/10.2196/23533.
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Background We systematically reviewed the literature on human coaching to identify different coaching processes as behavioral interventions and methods within those processes. We then reviewed how those identified coaching processes and the used methods can be utilized to improve an electronic coaching (eCoaching) process for the promotion of a healthy lifestyle with the support of information and communication technology (ICT). Objective This study aimed to identify coaching and eCoaching processes as behavioral interventions and the methods behind these processes. Here, we mainly looked at processes (and corresponding models that describe coaching as certain processes) and the methods that were used within the different processes. Several methods will be part of multiple processes. Certain processes (or the corresponding models) will be applicable for both human coaching and eCoaching. Methods We performed a systematic literature review to search the scientific databases EBSCOhost, Scopus, ACM, Nature, SpringerLink, IEEE Xplore, MDPI, Google Scholar, and PubMed for publications that included personal coaching (from 2000 to 2019) and persuasive eCoaching as behavioral interventions for a healthy lifestyle (from 2014 to 2019). The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework was used for the evidence-based systematic review and meta-analysis. Results The systematic search resulted in 79 publications, including 72 papers and seven books. Of these, 53 were related to behavioral interventions by eCoaching and the remaining 26 were related to human coaching. The most utilized persuasive eCoaching methods were personalization (n=19), interaction and cocreation (n=17), technology adoption for behavior change (n= 17), goal setting and evaluation (n=16), persuasion (n=15), automation (n=14), and lifestyle change (n=14). The most relevant methods for human coaching were behavior (n=23), methodology (n=10), psychology (n=9), and mentoring (n=6). Here, “n” signifies the total number of articles where the respective method was identified. In this study, we focused on different coaching methods to understand the psychology, behavioral science, coaching philosophy, and essential coaching processes for effective coaching. We have discussed how we can integrate the obtained knowledge into the eCoaching process for healthy lifestyle management using ICT. We identified that knowledge, coaching skills, observation, interaction, ethics, trust, efficacy study, coaching experience, pragmatism, intervention, goal setting, and evaluation of coaching processes are relevant for eCoaching. Conclusions This systematic literature review selected processes, associated methods, strengths, and limitations for behavioral interventions from established coaching models. The identified methods of coaching point toward integrating human psychology in eCoaching to develop effective intervention plans for healthy lifestyle management and overcome the existing limitations of human coaching.
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Enatescu, V. V., V. R. Enatescu, and I. Enatescu. "Computer analysis of non verbal behavioral parameters in unipolar major depression." European Psychiatry 26, S2 (March 2011): 1712. http://dx.doi.org/10.1016/s0924-9338(11)73416-2.
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Background and aimsBeside the interpretation and processing of content of communication, an important part of psychiatric diagnosis is made on behavioral signs and symptoms. While the semantic assessment of the content of thinking through communication was enriched by the development of several psychopathological scales, schedule and structured or semi-structured interviews the assessment of non verbal parameters remains uncovered. Our aims was to analyses the non verbal parameters, by an automatic system conceived by Dr. Enatescu et colab., in patients with mood disorders.MethodsThe instrument we used are: original traductors, systems of calculation and programming belonging to the artificial intelligence which create new pattern of representation of the gait, gesture, sonorous background of the speech, the dynamic of the writing which can be represented or through a matrix or in a n-dimensional space on specific clusters or to some human typology or to some psychical disorders.ResultsThe non verbal parameters processed by computer were sensible altered along with switching in the depressive states of subjects. The informatics data has had both diagnostic value and screening value for the course of unipolar depression.ConclusionsWe demonstrate that there is the chances for a new semiology which have objective paraclinic value for psychiatry field of automate analyses, nonverbal behavior parameters having the name “Extraverbale Analysis”.
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Sturman, Oliver, Lukas von Ziegler, Christa Schläppi, Furkan Akyol, Mattia Privitera, Daria Slominski, Christina Grimm, et al. "Deep learning-based behavioral analysis reaches human accuracy and is capable of outperforming commercial solutions." Neuropsychopharmacology 45, no. 11 (July 25, 2020): 1942–52. http://dx.doi.org/10.1038/s41386-020-0776-y.
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Abstract To study brain function, preclinical research heavily relies on animal monitoring and the subsequent analyses of behavior. Commercial platforms have enabled semi high-throughput behavioral analyses by automating animal tracking, yet they poorly recognize ethologically relevant behaviors and lack the flexibility to be employed in variable testing environments. Critical advances based on deep-learning and machine vision over the last couple of years now enable markerless tracking of individual body parts of freely moving rodents with high precision. Here, we compare the performance of commercially available platforms (EthoVision XT14, Noldus; TSE Multi-Conditioning System, TSE Systems) to cross-verified human annotation. We provide a set of videos—carefully annotated by several human raters—of three widely used behavioral tests (open field test, elevated plus maze, forced swim test). Using these data, we then deployed the pose estimation software DeepLabCut to extract skeletal mouse representations. Using simple post-analyses, we were able to track animals based on their skeletal representation in a range of classic behavioral tests at similar or greater accuracy than commercial behavioral tracking systems. We then developed supervised machine learning classifiers that integrate the skeletal representation with the manual annotations. This new combined approach allows us to score ethologically relevant behaviors with similar accuracy to humans, the current gold standard, while outperforming commercial solutions. Finally, we show that the resulting machine learning approach eliminates variation both within and between human annotators. In summary, our approach helps to improve the quality and accuracy of behavioral data, while outperforming commercial systems at a fraction of the cost.
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Heymann, Michael, and Asaf Degani. "Formal Analysis and Automatic Generation of User Interfaces: Approach, Methodology, and an Algorithm." Human Factors: The Journal of the Human Factors and Ergonomics Society 49, no. 2 (April 2007): 311–30. http://dx.doi.org/10.1518/001872007x312522.
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Konsolakis, Kostas, Hermie Hermens, and Oresti Banos. "A Novel Framework for the Holistic Monitoring and Analysis of Human Behaviour." Proceedings 31, no. 1 (November 20, 2019): 43. http://dx.doi.org/10.3390/proceedings2019031043.
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Recent technological advances have enabled the continuous and unobtrusive monitoring of human behaviour. However, most of the existing studies focus on detecting human behaviour under the limitation of one behavioural aspect, such as physical behaviour and not addressing human behaviour in a broad sense. For this reason, we propose a novel framework that will serve as the principal generator of knowledge on the user’s behaviour. The proposed framework moves beyond the current trends in automatic behaviour analysis by detecting and inferring human behaviour automatically, based on multimodal sensor data. In particular, the framework analyses human behaviour in a holistic approach, focusing on different behavioural aspects at the same time; namely physical, social, emotional and cognitive behaviour. Furthermore, the suggested framework investigates user’s behaviour over different periods, introducing the concept of short-term and long-term behaviours and how these change over time.
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Konstantinidis, Dimitrios, Kosmas Dimitropoulos, Billy Langlet, Petros Daras, and Ioannis Ioakimidis. "Validation of a Deep Learning System for the Full Automation of Bite and Meal Duration Analysis of Experimental Meal Videos." Nutrients 12, no. 1 (January 13, 2020): 209. http://dx.doi.org/10.3390/nu12010209.
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Eating behavior can have an important effect on, and be correlated with, obesity and eating disorders. Eating behavior is usually estimated through self-reporting measures, despite their limitations in reliability, based on ease of collection and analysis. A better and widely used alternative is the objective analysis of eating during meals based on human annotations of in-meal behavioral events (e.g., bites). However, this methodology is time-consuming and often affected by human error, limiting its scalability and cost-effectiveness for large-scale research. To remedy the latter, a novel “Rapid Automatic Bite Detection” (RABiD) algorithm that extracts and processes skeletal features from videos was trained in a video meal dataset (59 individuals; 85 meals; three different foods) to automatically measure meal duration and bites. In these settings, RABiD achieved near perfect agreement between algorithmic and human annotations (Cohen’s kappa κ = 0.894; F1-score: 0.948). Moreover, RABiD was used to analyze an independent eating behavior experiment (18 female participants; 45 meals; three different foods) and results showed excellent correlation between algorithmic and human annotations. The analyses revealed that, despite the changes in food (hash vs. meatballs), the total meal duration remained the same, while the number of bites were significantly reduced. Finally, a descriptive meal-progress analysis revealed that different types of food affect bite frequency, although overall bite patterns remain similar (the outcomes were the same for RABiD and manual). Subjects took bites more frequently at the beginning and the end of meals but were slower in-between. On a methodological level, RABiD offers a valid, fully automatic alternative to human meal-video annotations for the experimental analysis of human eating behavior, at a fraction of the cost and the required time, without any loss of information and data fidelity.
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Yamani, Yusuke, and Jason S. McCarley. "Effects of Task Difficulty and Display Format on Automation Usage Strategy: A Workload Capacity Analysis." Human Factors: The Journal of the Human Factors and Ergonomics Society 60, no. 4 (February 22, 2018): 527–37. http://dx.doi.org/10.1177/0018720818759356.
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Objective An experiment used workload capacity analysis to quantify automation usage strategy across different task difficulty and display format types in a speeded task. Background Workload capacity measures the efficiency of concurrent information processing and can serve as a gauge of automation usage strategy in speeded decision tasks. The present study used workload capacity analysis to investigate automation usage strategy while information display format and task difficulty were manipulated. Method Subjects performed a speeded judgment task assisted by an automated aid that issued decision cues at varying onset times. Response time distributions were converted to measures of workload capacity. Results Two variants of a workload capacity measure, CzOR and CzAND, gave evidence that operators moderated their own decision times both in anticipation of and following the arrival of the aid’s diagnosis under difficult task conditions regardless of display format. Conclusion Assistance from an automated decision aid may cause operators to delay their own responses in a speeded decision task, producing joint response time distributions that are slower than optimal. Application Even when it renders its own judgments quickly and with high accuracy, an automated decision aid may slow responses from a user. Automation designers should consider the relative costs and benefits of response accuracy and time when choosing whether and how to implement an automated decision aid.
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Velmans, Max. "Is human information processing conscious?" Behavioral and Brain Sciences 14, no. 4 (December 1991): 651–69. http://dx.doi.org/10.1017/s0140525x00071776.
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AbstractInvestigations of the function of consciousness in human information processing have focused mainly on two questions: (1) Where does consciousness enter into the information processing sequence, and (2) how does conscious processing differ from preconscious and unconscious processing? Input analysis is thought to be initially “preconscious” and “pre-attentive” - fast, involuntary, and automatic. This is followed by “conscious,” “focal-attentive” analysis, which is relatively slow, voluntary, and flexible. It is thought that simple, familiar stimuli can be identified preconsciously, but conscious processing is needed to identify complex, novel stimuli. Conscious processing has also been thought to be necessary for choice, learning and memory, and the organization of complex, novel responses, particularly those requiring planning, reflection, or creativity.The present target article reviews evidence that consciousness performs none of these functions. Consciousness nearly alwaysresultsfrom focal-attentive processing (as a form of output) but does not itselfenter intothis or any other form of human information processing. This suggests that the term “conscious process” needs reexamination. Consciousnessappearsto be necessary in a variety of tasks because they require focal-attentive processing; if consciousness is absent, focal-attentive processing is absent. From afirst-person perspective, however, conscious statesarecausally effective. First-person accounts arecomplementaryto third-person accounts. Although they can be translated into third-person accounts, they cannot be reduced to them.
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Balfe, Nora, Sarah Sharples, and John R. Wilson. "Understanding Is Key: An Analysis of Factors Pertaining to Trust in a Real-World Automation System." Human Factors: The Journal of the Human Factors and Ergonomics Society 60, no. 4 (April 3, 2018): 477–95. http://dx.doi.org/10.1177/0018720818761256.
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Objective: This paper aims to explore the role of factors pertaining to trust in real-world automation systems through the application of observational methods in a case study from the railway sector. Background: Trust in automation is widely acknowledged as an important mediator of automation use, but the majority of the research on automation trust is based on laboratory work. In contrast, this work explored trust in a real-world setting. Method: Experienced rail operators in four signaling centers were observed for 90 min, and their activities were coded into five mutually exclusive categories. Their observed activities were analyzed in relation to their reported trust levels, collected via a questionnaire. Results: The results showed clear differences in activity, even when circumstances on the workstations were very similar, and significant differences in some trust dimensions were found between groups exhibiting different levels of intervention and time not involved with signaling. Conclusion: Although the empirical, lab-based studies in the literature have consistently found that reliability and competence of the automation are the most important aspects of trust development, understanding of the automation emerged as the strongest dimension in this study. The implications are that development and maintenance of trust in real-world, safety-critical automation systems may be distinct from artificial laboratory automation. Application: The findings have important implications for emerging automation concepts in diverse industries including highly automated vehicles and Internet of things.
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Li, Haoqi, Brian Baucom, and Panayiotis Georgiou. "Linking emotions to behaviors through deep transfer learning." PeerJ Computer Science 6 (January 6, 2020): e246. http://dx.doi.org/10.7717/peerj-cs.246.
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Human behavior refers to the way humans act and interact. Understanding human behavior is a cornerstone of observational practice, especially in psychotherapy. An important cue of behavior analysis is the dynamical changes of emotions during the conversation. Domain experts integrate emotional information in a highly nonlinear manner; thus, it is challenging to explicitly quantify the relationship between emotions and behaviors. In this work, we employ deep transfer learning to analyze their inferential capacity and contextual importance. We first train a network to quantify emotions from acoustic signals and then use information from the emotion recognition network as features for behavior recognition. We treat this emotion-related information as behavioral primitives and further train higher level layers towards behavior quantification. Through our analysis, we find that emotion-related information is an important cue for behavior recognition. Further, we investigate the importance of emotional-context in the expression of behavior by constraining (or not) the neural networks’ contextual view of the data. This demonstrates that the sequence of emotions is critical in behavior expression. To achieve these frameworks we employ hybrid architectures of convolutional networks and recurrent networks to extract emotion-related behavior primitives and facilitate automatic behavior recognition from speech.
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Theis, Julian, and Houshang Darabi. "Behavioral Petri Net Mining and Automated Analysis for Human-Computer Interaction Recommendations in Multi-Application Environments." Proceedings of the ACM on Human-Computer Interaction 3, EICS (June 13, 2019): 1–16. http://dx.doi.org/10.1145/3331155.
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Su, Xin, Qingbo Gong, Yi Zheng, Xuchong Liu, and Kuan-Ching Li. "An Informative and Comprehensive Behavioral Characteristics Analysis Methodology of Android Application for Data Security in Brain-Machine Interfacing." Computational and Mathematical Methods in Medicine 2020 (March 10, 2020): 1–14. http://dx.doi.org/10.1155/2020/3658795.
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Recently, brain-machine interfacing is very popular that link humans and artificial devices through brain signals which lead to corresponding mobile application as supplementary. The Android platform has developed rapidly because of its good user experience and openness. Meanwhile, these characteristics of this platform, which cause the amazing pace of Android malware, pose a great threat to this platform and data correction during signal transmission of brain-machine interfacing. Many previous works employ various behavioral characteristics to analyze Android application (or app) and detect Android malware to protect signal data secure. However, with the development of Android app, category of Android app tends to be diverse, and the Android malware behavior tends to be complex. This situation makes existing Android malware detections complicated and inefficient. In this paper, we propose a broad analysis, gathering as many behavior characteristics of an app as possible and compare these behavior characteristics in several metrics. First, we extract static and dynamic behavioral characteristic from Android app in an automatic manner. Second, we explain the decision we made in each kind of behavioral characteristic we choose for Android app analysis and Android malware detection. Third, we design a detailed experiment, which compare the efficiency of each kind of behavior characteristic in different aspects. The results of experiment also show Android malware detection performance of these behavior characteristics combine with well-known machine learning algorithms.
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Lee, John D., and Kristin Kolodge. "Exploring Trust in Self-Driving Vehicles Through Text Analysis." Human Factors: The Journal of the Human Factors and Ergonomics Society 62, no. 2 (September 10, 2019): 260–77. http://dx.doi.org/10.1177/0018720819872672.
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Objective This study examined attitudes toward self-driving vehicles and the factors motivating those attitudes. Background Self-driving vehicles represent potentially transformative technology, but achieving this potential depends on consumers’ attitudes. Ratings from surveys estimate these attitudes, and open-ended comments provide an opportunity to understand their basis. Method A nationally representative sample of 7,947 drivers in 2016 and 8,517 drivers in 2017 completed the J.D. Power U.S. Tech Choice StudySM, which included a rating for level of trust with self-driving vehicles and associated open-ended comments. These open-ended comments are qualitative data that can be analyzed quantitatively using structural topic modeling. Structural topic modeling identifies common themes, extracts prototypical comments for each theme, and assesses how the survey year and rating affect the prevalence of these themes. Results Structural topic modeling identified 13 topics, such as “Tested for a long time,” which was strongly associated with positive ratings, and “Hacking & glitches,” which was strongly associated with negative ratings. The topics of “Self-driving accidents” and “Trust when mature” were more prominent in 2017 compared with 2016. Conclusion Structural topic modeling reveals reasons underlying consumer attitudes toward vehicle automation. These reasons align with elements typically associated with trust in automation, as well as elements that mediate perceived risk, such as the desire for control as well as societal, relational, and experiential bases of trust. Application The analysis informs the debate concerning how safe is safe enough for automated vehicles and provides initial indicators of what makes such vehicles feel safe and trusted.
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Patterson, Robert Earl. "Intuitive Cognition and Models of Human–Automation Interaction." Human Factors: The Journal of the Human Factors and Ergonomics Society 59, no. 1 (February 2017): 101–15. http://dx.doi.org/10.1177/0018720816659796.
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Objective: The aim of this study was to provide an analysis of the implications of the dominance of intuitive cognition in human reasoning and decision making for conceptualizing models and taxonomies of human–automation interaction, focusing on the Parasuraman et al. model and taxonomy. Background: Knowledge about how humans reason and make decisions, which has been shown to be largely intuitive, has implications for the design of future human–machine systems. Method: One hundred twenty articles and books cited in other works as well as those obtained from an Internet search were reviewed. Works were deemed eligible if they were published within the past 50 years and common to a given literature. Results: Analysis shows that intuitive cognition dominates human reasoning and decision making in all situations examined. The implications of the dominance of intuitive cognition for the Parasuraman et al. model and taxonomy are discussed. A taxonomy of human–automation interaction that incorporates intuitive cognition is suggested. Application: Understanding the ways in which human reasoning and decision making is intuitive can provide insight for future models and taxonomies of human–automation interaction.
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Geng, Wei, Pamela Cosman, Joong-Hwan Baek, Charles C. Berry, and William R. Schafer. "Quantitative Classification and Natural Clustering of Caenorhabditis elegans Behavioral Phenotypes." Genetics 165, no. 3 (November 1, 2003): 1117–26. http://dx.doi.org/10.1093/genetics/165.3.1117.
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Abstract Genetic analysis of nervous system function relies on the rigorous description of behavioral phenotypes. However, standard methods for classifying the behavioral patterns of mutant Caenorhabditis elegans rely on human observation and are therefore subjective and imprecise. Here we describe the application of machine learning to quantitatively define and classify the behavioral patterns of C. elegans nervous system mutants. We have used an automated tracking and image processing system to obtain measurements of a wide range of morphological and behavioral features from recordings of representative mutant types. Using principal component analysis, we represented the behavioral patterns of eight mutant types as data clouds distributed in multidimensional feature space. Cluster analysis using the k-means algorithm made it possible to quantitatively assess the relative similarities between different behavioral phenotypes and to identify natural phenotypic clusters among the data. Since the patterns of phenotypic similarity identified in this study closely paralleled the functional similarities of the mutant gene products, the complex phenotypic signatures obtained from these image data appeared to represent an effective diagnostic of the mutants' underlying molecular defects.
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Snehitha, Kanaparthi. "Facial Expression Recognition with Appearance Based Features of Facial Landmarks." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 3343–48. http://dx.doi.org/10.22214/ijraset.2021.35702.
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Artificial intelligence technology has been trying to bridge the gap between humans and machines. The latest development in this technology is Facial recognition. Facial recognition technology identifies the faces by co-relating and verifying the patterns of facial contours. Facial recognition is done by using Viola-Jones object detection framework. Facial expression is one of the important aspects in recognizing human emotions. Facial expression also helps to determine interpersonal relation between humans. Automatic facial recognition is now being used very widely in almost every field, like marketing, health care, behavioral analysis and also in human-machine interaction. Facial expression recognition helps a lot more than facial recognition. It helps the retailers to understand their customers, doctors to understand their patients, and organizations to understand their clients. For the expression recognition, we are using the landmarks of face which are appearance-based features. With the use of an active shape model, LBP (Local Binary Patterns) derives its properties from face landmarks. The operation is carried out by taking into account pixel values, which improves the rate of expression recognition. In an experiment done using previous methods and 10-fold cross validation, the accuracy achieved is 89.71%. CK+ Database is used to achieve this result.
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Min, Weidong, Hao Cui, Qing Han, and Fangyuan Zou. "A Scene Recognition and Semantic Analysis Approach to Unhealthy Sitting Posture Detection during Screen-Reading." Sensors 18, no. 9 (September 16, 2018): 3119. http://dx.doi.org/10.3390/s18093119.
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Behavior analysis through posture recognition is an essential research in robotic systems. Sitting with unhealthy sitting posture for a long time seriously harms human health and may even lead to lumbar disease, cervical disease and myopia. Automatic vision-based detection of unhealthy sitting posture, as an example of posture detection in robotic systems, has become a hot research topic. However, the existing methods only focus on extracting features of human themselves and lack understanding relevancies among objects in the scene, and henceforth fail to recognize some types of unhealthy sitting postures in complicated environments. To alleviate these problems, a scene recognition and semantic analysis approach to unhealthy sitting posture detection in screen-reading is proposed in this paper. The key skeletal points of human body are detected and tracked with a Microsoft Kinect sensor. Meanwhile, a deep learning method, Faster R-CNN, is used in the scene recognition of our method to accurately detect objects and extract relevant features. Then our method performs semantic analysis through Gaussian-Mixture behavioral clustering for scene understanding. The relevant features in the scene and the skeletal features extracted from human are fused into the semantic features to discriminate various types of sitting postures. Experimental results demonstrated that our method accurately and effectively detected various types of unhealthy sitting postures in screen-reading and avoided error detection in complicated environments. Compared with the existing methods, our proposed method detected more types of unhealthy sitting postures including those that the existing methods could not detect. Our method can be potentially applied and integrated as a medical assistance in robotic systems of health care and treatment.
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Garau, Maia, Doron Friedman, Hila Ritter Widenfeld, Angus Antley, Andrea Brogni, and Mel Slater. "Temporal and Spatial Variations in Presence: Qualitative Analysis of Interviews from an Experiment on Breaks in Presence." Presence: Teleoperators and Virtual Environments 17, no. 3 (June 1, 2008): 293–309. http://dx.doi.org/10.1162/pres.17.3.293.
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This paper presents qualitative findings from an experiment designed to investigate breaks in presence. Participants spent approximately five minutes in an immersive Cave-like system depicting a virtual bar with five virtual characters. On four occasions the projections were made to go white to trigger clearly identifiable anomalies in the audiovisual experience. Participants' physiological responses were measured throughout to investigate possible physiological correlates of these experienced anomalies. Analysis of subsequent interviews with participants suggests that these anomalies were subjectively experienced as breaks in presence. This is significant in the context of our research approach, which considers presence as a multilevel construct ranging from higher-level subjective responses to lower-level behavioral and automatic responses. The fact that these anomalies were also associated with an identifiable physiological signature suggests future avenues for exploring less intrusive ways of studying temporal fluctuations in presence during the course of the mediated experience itself. The findings also reveal that breaks in presence have multiple causes and can range in intensity, resulting in varying recovery times. In addition, presence can vary in intensity within the same space, suggesting that presence in an immersive virtual environment can fluctuate temporally and that spatial behavior is consistent with what would be expected in an equivalent real environment.
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Marmpena, Mina, Angelica Lim, and Torbjørn S. Dahl. "How does the robot feel? Perception of valence and arousal in emotional body language." Paladyn, Journal of Behavioral Robotics 9, no. 1 (July 1, 2018): 168–82. http://dx.doi.org/10.1515/pjbr-2018-0012.
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Abstract Human-robot interaction in social robotics applications could be greatly enhanced by robotic behaviors that incorporate emotional body language. Using as our starting point a set of pre-designed, emotion conveying animations that have been created by professional animators for the Pepper robot, we seek to explore how humans perceive their affect content, and to increase their usability by annotating them with reliable labels of valence and arousal, in a continuous interval space. We conducted an experiment with 20 participants who were presented with the animations and rated them in the two-dimensional affect space. An inter-rater reliability analysis was applied to support the aggregation of the ratings for deriving the final labels. The set of emotional body language animations with the labels of valence and arousal is available and can potentially be useful to other researchers as a ground truth for behavioral experiments on robotic expression of emotion, or for the automatic selection of robotic emotional behaviors with respect to valence and arousal. To further utilize the data we collected, we analyzed it with an exploratory approach and we present some interesting trends with regard to the human perception of Pepper’s emotional body language, that might be worth further investigation.
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Bringas Vega, Maria L., Shengnan Liu, Min Zhang, Ivonne Pedroso Ibañez, Lilia M. Morales Chacon, Lidice Galan Garcia, Vanessa Perez Bocourt, Marjan Jahanshahi, and Pedro A. Valdes-Sosa. "Flanker Task-Elicited Event-Related Potential Sources Reflect Human Recombinant Erythropoietin Differential Effects on Parkinson’s Patients." Parkinson's Disease 2020 (May 22, 2020): 1–10. http://dx.doi.org/10.1155/2020/8625794.
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We used EEG source analysis to identify which cortical areas were involved in the automatic and controlled processes of inhibitory control on a flanker task and compared the potential efficacy of recombinant-human erythropoietin (rHuEPO) on the performance of Parkinson’s Disease patients. The samples were 18 medicated PD patients (nine of them received rHuEPO in addition to their usual anti-PD medication through random allocation and the other nine patients were on their regular anti-PD medication only) and 9 age and education-matched healthy controls (HCs) who completed the flanker task with simultaneous EEG recordings. N1 and N2 event-related potential (ERP) components were identified and a low resolution tomography (LORETA) inverse solution was employed to localize the neural generators. Reaction times and errors were increased for the incongruent flankers for PD patients compared to controls. EEG source analysis identified an effect of rHuEPO on the lingual gyri for the early N1 component. N2-related sources in middle cingulate and precuneus were associated with the inhibition of automatic responses evoked by incongruent stimuli differentiated PD and HCs. From our results rHuEPO seems to mediate an effect on N1 sources in lingual gyri but not on behavioural performance. N2-related sources in middle cingulate and precuneus were evoked by incongruent stimuli differentiated PD and HCs.
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Tatasciore, Monica, Vanessa K. Bowden, Troy A. W. Visser, Steph I. C. Michailovs, and Shayne Loft. "The Benefits and Costs of Low and High Degree of Automation." Human Factors: The Journal of the Human Factors and Ergonomics Society 62, no. 6 (August 19, 2019): 874–96. http://dx.doi.org/10.1177/0018720819867181.
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Objective The objective of this study is to examine the effects of low and high degree of automation (DOA) on performance, subjective workload, situation awareness (SA), and return-to-manual control in simulated submarine track management. Background Theory and meta-analytic evidence suggest that as DOA increases, operator performance improves and workload decreases, but SA and return-to-manual control declines. Research also suggests that operators have particular difficulty regaining manual control if automation provides incorrect advice. Method Undergraduate student participants completed a submarine track management task that required them to track the position and behavior of contacts. Low DOA supported information acquisition and analysis, whereas high DOA recommended decisions. At a late stage in the task, automation was either unexpectedly removed or provided incorrect advice. Results Relative to no automation, low DOA moderately benefited performance but impaired SA and non-automated task performance. Relative to no automation and low DOA, high DOA benefited performance and lowered workload. High DOA did impair non-automated task performance compared with no automation, but this was equivalent to low DOA. Participants were able to return-to-manual control when they knew low or high DOA was disengaged, or when high DOA provided incorrect advice. Conclusion High DOA improved performance and lowered workload, at no additional cost to SA or return-to-manual performance when compared with low DOA. Application Designers should consider the likely level of uncertainty in the environment and the consequences of return-to-manual deficits before implementing low or high DOA.
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Hancock, Peter A. "On the Nature of Vigilance." Human Factors: The Journal of the Human Factors and Ergonomics Society 59, no. 1 (February 2017): 35–43. http://dx.doi.org/10.1177/0018720816655240.
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Objective: I explore the origins, theoretical underpinnings, applications, and importance of vigilance in a world ever more dominated by semiautomated, automated, and autonomous machines. Background: The empirical genesis of vigilance is taken as a case study in the etiology of the application of the behavioral sciences to the human culture of technology. The subsequent taxonomic ordering and theoretical clarification of its causal antecedents are set in the overall context of contemporary human–machine systems research. Method: The methods exercised in this work are historical analysis and informational synthesis in combination with projected theoretical implications and impact. Results: The profile of evolution of the concept of vigilance is clarified and cast in the light of critical events, such as the promulgation of the vigilance taxonomy, its linkage to attentional resource theory, and the recognition that the attendant performance decrement is as indicative of iatrogenic sources as it is a shortfall or limitation of the observer’s processing capacity. Conclusion: Vigilance is alive and growing in importance. Understanding sustained attention will become ever more critical in the humanization of automation-dominated systems. Application: The application of vigilance is widespread and potentially ubiquitous for semiautomated, automated, and autonomous system interaction.
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Bisantz, Ann M., and Amy R. Pritchett. "Measuring the Fit between Human Judgments and Automated Alerting Algorithms: A Study of Collision Detection." Human Factors: The Journal of the Human Factors and Ergonomics Society 45, no. 2 (June 2003): 266–80. http://dx.doi.org/10.1518/hfes.45.2.266.27243.
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Methodologies for assessing human judgment in complex domains are important for the design of both displays that inform judgment and automated systems that suggest judgments. This paper uses the n-system lens model to evaluate the impact of displays on human judgment and to explicitly assess the similarity between human judgments and a set of potential judgment algorithms for use in automated systems. First, the need for and concepts underlying judgment analysis are outlined. Then the n-system lens model and its parameters are formally described. This model is then used to examine a previously conducted study of aircraft collision detection that had been analyzed using standard analysis of variance methods. Our analysis found the same main effects as did the earlier analysis. However, n-system lens model analysis was able to provide greater insight into the information relied upon for judgments and the impact of displays on judgment. Additionally, the analysis was able to identify attributes of human judgments that were---and were not---similar to judgments produced by automated systems. Potential applications of this research include automated aid design and operator training.
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Zheng, Yafeng, Haogang Bao, Jun Shen, and Xuesong Zhai. "Investigating Sequence Patterns of Collaborative Problem-Solving Behavior in Online Collaborative Discussion Activity." Sustainability 12, no. 20 (October 15, 2020): 8522. http://dx.doi.org/10.3390/su12208522.
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Collaborative problem solving (CPS) is an influential human behavior affecting working performance and well-being. Previous studies examined CPS behavior from the perspective of either social or cognitive dimensions, which leave a research gap from the interactive perspective. In addition, the traditional sequence analysis method failed to combine time sequences and sub-problem sequences together while analyzing behavioral patterns in CPS. This study proposes a developed schema for the multidimensional analysis of CPS. A combination sequential analysis approach that comprises time sequences and sub-problem sequences is also employed to explore CPS patterns. A total of 191 students were recruited and randomly grouped into 38 teams (four to six students per team) in the online collaborative discussion activity. Their discussion transcripts were coded while they conducted CPS, followed by the assessment of high- and low- performance groups according to the developed schema and sequential analysis. With the help of the new analysis method, the findings indicate that a deep exploratory discussion is generated from conflicting viewpoints, which promotes improved problem-solving outcomes and perceptions. In addition, evidence-based rationalization can motivate collaborative behavior effectively. The results demonstrated the potential power of automatic sequential analysis with multidimensional behavior and its ability to provide quantitative descriptions of group interactions in the investigated threaded discussions.
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Hussein, Aya, Sondoss Elsawah, and Hussein A. Abbass. "Trust Mediating Reliability–Reliance Relationship in Supervisory Control of Human–Swarm Interactions." Human Factors: The Journal of the Human Factors and Ergonomics Society 62, no. 8 (October 7, 2019): 1237–48. http://dx.doi.org/10.1177/0018720819879273.
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Objective This work aims to further test the theory that trust mediates the interdependency between automation reliability and the rate of human reliance on automation. Background Human trust in automation has been the focus of many research studies. Theoretically, trust has been proposed to impact human reliance on automation by mediating the relationship between automation reliability and the rate of human reliance. Experimentally, however, the results are contradicting as some confirm the mediating role of trust, whereas others deny it. Hence, it is important to experimentally reinvestigate this role of trust and understand how the results should be interpreted in the light of existing theory. Method Thirty-two subjects supervised a swarm of unmanned aerial vehicles (UAVs) in foraging missions in which the swarm provided recommendations on whether or not to collect potential targets, based on the information sensed by the UAVs. By manipulating the reliability of the recommendations, we observed changes in participants’ trust and their behavioral responses. Results A within-subject mediation analysis revealed a significant mediation role of trust in the relationship between swarm reliability and reliance rate. High swarm reliability increased the rate of correct acceptances, but decreased the rate of correct rejections. No significant effect of reliability was found on response time. Conclusion Trust is not a mere by-product of the interaction; it possesses a predictive power to estimate the level of reliance on automation. Application The mediation role of trust confirms the significance of trust calibration in determining the appropriate level of reliance on swarm automation.
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Harel, David, Guy Katz, Rami Marelly, and Assaf Marron. "First Steps Towards a Wise Development Environment for Behavioral Models." International Journal of Information System Modeling and Design 7, no. 3 (July 2016): 1–22. http://dx.doi.org/10.4018/ijismd.2016070101.
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The authors present an initial wise development framework: a development environment that proactively and interactively assists the software engineer in modeling complex reactive systems. Their framework repeatedly analyzes models of the system under development at various levels of abstraction, and then reasons about these models in order to detect possible errors, to derive emergent properties of interest, and to assist in system testing and debugging. Upon request, the environment can instrument the system model in order to monitor or test the execution for certain behaviors, or even augment it in order to repair or avoid detected behavior that is undesired. The direction and prioritization of the analysis and related tasks is based on the relevance of the observed properties and the expected impact of actions to be taken, and is performed by specialized automated and human-assisted techniques that have been incorporated into the framework. The authors' development environment is an initial step in the direction of their recent Wise Computing vision, which calls for turning the computer (namely, the development environment) into an equal member of the development team: knowledgeable, independent, concerned and proactively involved in the development process. They have implemented their tool within the context of behavioral programming (BP) – a scenario-based modeling approach, in which components are aligned with how humans often describe desired system behavior. The authors' work thus further enhances the naturalness and incrementality of developing in BP.
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Wang, Haibo, Ting Pan, Haiqing Si, Yao Li, and Naiqi Jiang. "Research on Influencing Factor Selection of Pilot’s Intention." International Journal of Aerospace Engineering 2020 (March 14, 2020): 1–13. http://dx.doi.org/10.1155/2020/4294538.
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The physiological, psychological, and physical characteristics of the pilot will have an impact on flight safety, mainly in the pilot’s intention. In another word, this means the pilot’s psychological experience of flight status under the influence of various factors and the preference for decision-making or behavioral value that is displayed. The pilot’s intention is to reflect the cognitive state that the pilot showed during the maneuvering of the aircraft. The exploration of intention is very important for the study of automatic pilot and flight control active safety system. Also, it is an important concept often involved in the study of human factors in flight, especially the microbehavior of pilots. Pilot’s intention is taken as the study object in this paper; physiological-psychological-physical parameters are obtained through analyzing their influencing factors from the simulating flight experiments designed. The random forest analysis method is used to rank the main influencing factors affecting the pilot’s intention, and the factor sequence is formed. The results provide a good foundation for further research on the pilot’s intention identification.
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Kirlik, Alex. "Modeling Strategic Behavior in Human-Automation Interaction: Why an "Aid" Can (and Should) Go Unused." Human Factors: The Journal of the Human Factors and Ergonomics Society 35, no. 2 (June 1993): 221–42. http://dx.doi.org/10.1177/001872089303500203.
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Task-offload aids (e.g., an autopilot, an "intelligent" assistant) can be selectively engaged by the human operator to dynamically delegate tasks to automation. Introducing such aids eliminates some task demands but creates new ones associated with programming, engaging, and disengaging the aiding device via an interface. The burdens associated with managing automation can sometimes outweigh the potential benefits of automation to improved system performance. Aid design parameters and features of the overall multitask context combine to determine whether or not a task-offload aid will effectively support the operator. A modeling and sensitivity analysis approach is presented that identifies effective strategies for human-automation interaction as a function of three task-context parameters and three aid design parameters. The analysis and modeling approaches provide resources for predicting how a well-adapted operator will use a given task-offload aid, and for specifying aid design features that ensure that automation will provide effective operator support in a multitask environment.
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Fritz, Roschelle L., Marian Wilson, Gordana Dermody, Maureen Schmitter-Edgecombe, and Diane J. Cook. "Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis." Journal of Medical Internet Research 22, no. 11 (November 6, 2020): e23943. http://dx.doi.org/10.2196/23943.
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Background Poorly managed pain can lead to substance use disorders, depression, suicide, worsening health, and increased use of health services. Most pain assessments occur in clinical settings away from patients’ natural environments. Advances in smart home technology may allow observation of pain in the home setting. Smart homes recognizing human behaviors may be useful for quantifying functional pain interference, thereby creating new ways of assessing pain and supporting people living with pain. Objective This study aimed to determine if a smart home can detect pain-related behaviors to perform automated assessment and support intervention for persons with chronic pain. Methods A multiple methods, secondary data analysis was conducted using historic ambient sensor data and weekly nursing assessment data from 11 independent older adults reporting pain across 1-2 years of smart home monitoring. A qualitative approach was used to interpret sensor-based data of 27 unique pain events to support clinician-guided training of a machine learning model. A periodogram was used to calculate circadian rhythm strength, and a random forest containing 100 trees was employed to train a machine learning model to recognize pain-related behaviors. The model extracted 550 behavioral markers for each sensor-based data segment. These were treated as both a binary classification problem (event, control) and a regression problem. Results We found 13 clinically relevant behaviors, revealing 6 pain-related behavioral qualitative themes. Quantitative results were classified using a clinician-guided random forest technique that yielded a classification accuracy of 0.70, sensitivity of 0.72, specificity of 0.69, area under the receiver operating characteristic curve of 0.756, and area under the precision-recall curve of 0.777 in comparison to using standard anomaly detection techniques without clinician guidance (0.16 accuracy achieved; P<.001). The regression formulation achieved moderate correlation, with r=0.42. Conclusions Findings of this secondary data analysis reveal that a pain-assessing smart home may recognize pain-related behaviors. Utilizing clinicians’ real-world knowledge when developing pain-assessing machine learning models improves the model’s performance. A larger study focusing on pain-related behaviors is warranted to improve and test model performance.
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Maloney, Maureen, and Alma McCarthy. "Automatic enrolment to pension plans in small organisations: a research agenda." Employee Relations 41, no. 1 (January 7, 2019): 142–57. http://dx.doi.org/10.1108/er-06-2017-0138.
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PurposeThe purpose of this paper is to analyse how firm size impacts pension workforce coverage with a particular focus on automatic enrolment (AE) to pension plans in small organisations.Design/methodology/approachThe paper examines the alignment of government AE interests with those of small employers, their employees and pension providers to better understand how firm size impacts pension workforce coverage.FindingsThe alignment of interests between stakeholders (government, pension providers, employers and employees) differs between large and small organisations, and empirical findings from large organisations cannot be assumed to apply in small organisations.Research limitations/implicationsThe paper calls attention to the need for future empirical research and identifies a number of research questions for further analysis to examine how AE impacts pension participation in small organisations and advance the field.Originality/valueThe policy of automatically enroling employees into occupational pension plans, recently legislated for all eligible workers in the UK and under consideration in the USA and Ireland, was developed from research conducted in a small number of large organisations. Pension coverage is particularly inadequate for the large number of employees working in small organisations (1–49 employees). However, little research attention has been focussed on pensions in small organisations with pension policy makers assuming that legislated AE will work as effectively in small organisations as it did in large organisations. This paper addresses this gap in the field.
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Horrey, William J., and John D. Lee. "Preface to the Special Issue on Human Factors and Advanced Vehicle Automation: Of Benefits, Barriers, and Bridges to Safe and Effective Implementation." Human Factors: The Journal of the Human Factors and Ergonomics Society 62, no. 2 (March 2020): 189–93. http://dx.doi.org/10.1177/0018720820901542.
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Objective The aim of this special issue is to bring together the latest research related to driver interaction with various types of vehicle automation. Background Vehicle technology has undergone significant progress over the past decade, bringing new support features that can assist the driver and take on more and more of the driving responsibilities. Method This issue is comprised of eight articles from international research teams, focusing on different types of automation and different user populations, including driver support features through to highly automated driving systems. Results The papers comprising this special issue are clustered into three categories: (a) experimental studies of driver interactions with advanced vehicle technologies; (b) analysis of existing data sources; and (c) emerging human factors issues. Studies of currently available and pending systems highlight some of the human factors challenges associated with the driver–system interaction that are likely to become more prominent in the near future. Moreover, studies of more nascent concepts (i.e., those that are still a long way from production vehicles) underscore many attitudes, perceptions, and concerns that will need to be considered as these technologies progress. Conclusions Collectively, the papers comprising this special issue help fill some gaps in our knowledge. More importantly, they continue to help us identify and articulate some of the important and potential human factors barriers, design considerations, and research needs as these technologies become more ubiquitous.
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Sheridan, Thomas B. "Extending Three Existing Models to Analysis of Trust in Automation: Signal Detection, Statistical Parameter Estimation, and Model-Based Control." Human Factors: The Journal of the Human Factors and Ergonomics Society 61, no. 7 (February 27, 2019): 1162–70. http://dx.doi.org/10.1177/0018720819829951.
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Objective: The objective is to propose three quantitative models of trust in automation. Background: Current trust-in-automation literature includes various definitions and frameworks, which are reviewed. Method: This research shows how three existing models, namely those for signal detection, statistical parameter estimation calibration, and internal model-based control, can be revised and reinterpreted to apply to trust in automation useful for human–system interaction design. Results: The resulting reinterpretation is presented quantitatively and graphically, and the measures for trust and trust calibration are discussed, along with examples of application. Conclusion: The resulting models can be applied to provide quantitative trust measures in future experiments or system designs. Applications: Simple examples are provided to explain how model application works for the three trust contexts that correspond to signal detection, parameter estimation calibration, and model-based open-loop control.
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Tomasevic, Nebojsa, Tim Horberry, and Brian Fildes. "Validation of a driving simulator for research into human factors issues of automated vehicles." Journal of the Australasian College of Road Safety 30, no. 2 (May 24, 2019): 37–44. http://dx.doi.org/10.33492/jacrs-d-18-00279.
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This study evaluated the behavioural validity of the Monash University Accident Research Centre automation driving simulator for research into the human factors issues associated with automated driving. The study involved both on-road and simulated driving. Twenty participants gave ratings of their willingness to resume control of an automated vehicle and perception of safety for a variety of situations along the drives. Each situation was individually categorised and ratings were processed. Statistical analysis of the ratings confirmed the behavioural validity of the simulator, in terms of the similarity of the on-road and simulator data.
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Noval Nasrudin, Mochamad. "Preskripsi Metodologis Pengembangan Teori Ekonomi Islam dalam Menangani Ketidakpastian (Kelemahan Bawaan dari Manusia Dewasa) dalam Menjalankan Ekonomi Praktis." JEBDEER: Journal of Entrepreneurship, Business Development and Economic Educations Research 2, no. 2 (December 7, 2020): 49–58. http://dx.doi.org/10.32616/jbr.v2i2.240.
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Islam is a holistic religion that deals with every aspect of life according to human nature where it satisfies human preferences, needs, traits, desires, desires, feelings, souls and thoughts. Allah SWT created human beings who are present with a soul, emotion, care, and love for something beautiful. The purpose of this study is to identify and analyze the methodological prescriptions of the development of Islamic economic theory in dealing with innate human weaknesses. This study uses a qualitative-inductive approach. Data were collected using literature study techniques and documentation studies. Secondary data source used. Then, the data were analyzed using qualitative data analysis approaches and techniques. Based on the discussion, the results of the research can be concluded as follows: 1) The elimination of strong, centralized and integrated moral control over churches and institutions that oppress them, especially the paradigm of religious judgment in Western economic thought (such as human economic behavior) is incapable of understanding moral rules leading to the current crisis. Kahneman began studying psychology and behavioral economics. By showing that psychological motivation determines people's behavior, and this motivation is important for economic phenomena. The main point here is the discovery of law, which regulates human economic behavior through moral control imposed by primitive Islamic sources, and has become the main method dictated by the automatic control system of classical Islamic sources founded some 1400 years ago. Therefore, the essence of capitalism with moral control is adopting from Islam. 2) In Islam, all actions are based on the fact that the Qur'an and the Sunnah divide into two areas of rules and moral values, the first is to develop the rational ability to understand moral propositions and prescribed rules, and use them in negotiations. Ability to negotiate and face decisions. Second, from the depths of the individual (nafs) to the depths of the universe, organize and determine the area of ??human observation, intelligence, and reason.
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Wickens, Christopher D., Linda Onnasch, Angelina Sebok, and Dietrich Manzey. "Absence of DOA Effect but No Proper Test of the Lumberjack Effect: A Reply to Jamieson and Skraaning (2019)." Human Factors: The Journal of the Human Factors and Ergonomics Society 62, no. 4 (January 28, 2020): 530–34. http://dx.doi.org/10.1177/0018720820901957.
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Objective The aim was to evaluate the relevance of the critique offered by Jamieson and Skraaning (2019) regarding the applicability of the lumberjack effect of human–automation interaction to complex real-world settings. Background The lumberjack effect, based upon a meta-analysis, identifies the consequences of a higher degree of automation—to improve performance and reduce workload—when automation functions as intended, but to degrade performance more, as mediated by a loss of situation awareness (SA) when automation fails. Jamieson and Skraaning provide data from a process control scenario that they assert contradicts the effect. Approach We analyzed key aspects of their simulation, measures, and results which we argue limit the strength of their conclusion that the lumberjack effect is not applicable to complex real-world systems. Results Our analysis revealed limits in their inappropriate choice of automation, the lack of a routine performance measure, support for the lumberjack effect that was actually provided by subjective measures of the operators, an inappropriate assessment of SA, and a possible limitation of statistical power. Conclusion We regard these limitations as reasons to temper the strong conclusions drawn by the authors, of no applicability of the lumberjack effect to complex environments. Their findings should be used as an impetus for conducting further research on human–automation interaction in these domains. Applications The collective findings of both Jamieson and Skraaning and our study are applicable to system designers and users in deciding upon the appropriate level of automation to deploy.
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Cunningham, R. B., D. B. Lindenmayer, and B. D. Lindenmayer. "Sound recording of bird vocalisations in forests. I. Relationships between bird vocalisations and point interval counts of bird numbers - a case study in statistical modeling." Wildlife Research 31, no. 2 (2004): 195. http://dx.doi.org/10.1071/wr02062.
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Morning vocal activity data for birds were collected using automatic sound recorders at 165 sites in fragmented forests at Tumut in south-eastern Australia. A model was developed to describe the statistical properties of the vocal activity data and study the relationship between the number of 'elements' (or notes) broadcast by birds and the number of birds recorded by human observers. We discuss the practical issues of finding a model where variance heterogeneity and skewness is a feature of the data, while the context of the problem required that multiplicative relationships be preserved. A satisfactory fit to our vocal activity data was obtained by using a Gamma distribution.For most groups of birds, the observed relationships between vocal activity and the number of birds recorded by human point counts were weak but statistically significant.Our analysis suggests that these data provide limited useful information about vocal activity per bird. Automatic sound-recording data may be informative for drawing inferences about temporal patterns in vocal activity but do not seem useful as a method for estimating the abundance of birds. A significant relationship was identified between the number of species vocalising, as measured by sound recorders, and the number of species observed by point count method. However, the relationship was far from isometric and there were many more species detected by the sound recorders than by human observers.Results from analyses of vocal activity data gathered at Tumut were confirmed by additional data obtained in a subsequent, small calibration study in the Namadgi National Park in the Australian Capital Territory.
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Bollu, Tejapratap, Samuel C. Whitehead, Nikil Prasad, Jackson Walker, Nitin Shyamkumar, Raghav Subramaniam, Brian Kardon, Itai Cohen, and Jesse H. Goldberg. "Automated home cage training of mice in a hold-still center-out reach task." Journal of Neurophysiology 121, no. 2 (February 1, 2019): 500–512. http://dx.doi.org/10.1152/jn.00667.2018.
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An obstacle to understanding neural mechanisms of movement is the complex, distributed nature of the mammalian motor system. Here we present a novel behavioral paradigm for high-throughput dissection of neural circuits underlying mouse forelimb control. Custom touch-sensing joysticks were used to quantify mouse forelimb trajectories with micron-millisecond spatiotemporal resolution. Joysticks were integrated into computer-controlled, rack-mountable home cages, enabling batches of mice to be trained in parallel. Closed loop behavioral analysis enabled online control of reward delivery for automated training. We used this system to show that mice can learn, with no human handling, a direction-specific hold-still center-out reach task in which a mouse first held its right forepaw still before reaching out to learned spatial targets. Stabilogram diffusion analysis of submillimeter-scale micromovements produced during the hold demonstrate that an active control process, akin to upright balance, was implemented to maintain forepaw stability. Trajectory decomposition methods, previously used in primates, were used to segment hundreds of thousands of forelimb trajectories into millions of constituent kinematic primitives. This system enables rapid dissection of neural circuits for controlling motion primitives from which forelimb sequences are built. NEW & NOTEWORTHY A novel joystick design resolves mouse forelimb kinematics with micron-millisecond precision. Home cage training is used to train mice in a hold-still center-out reach task. Analytical methods, previously used in primates, are used to decompose mouse forelimb trajectories into kinematic primitives.
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Gauvin, David V., Zachary J. Zimmermann, Jill A. Dalton, Theodore J. Baird, and Mary-Jeanne Kallman. "CNS Safety Screening Under ICH S7A Guidelines Requires Observations of Multiple Behavioral Units to Assess Motor Function." International Journal of Toxicology 38, no. 5 (August 30, 2019): 339–56. http://dx.doi.org/10.1177/1091581819864836.
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In the adoption of behavior as a critical end point in safety pharmacology and neurotoxicity screening, federal regulatory agencies have shifted the predominating scientific perspective from pharmacology back to the experimental analysis of behavior (psychology). Nowhere is this more evident than in tier I safety assessment of the central nervous system (CNS). The CNS and peripheral nervous system have multiple behavioral units of general activity. A complete picture of the motor control neural pathways cannot be measured by any one single approach. The CNS safety protocols under International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use S7A are required to be conducted in accordance with Good Laboratory Practices by trained technical staff. The CNS safety assessments necessitate the inclusion of a thorough and detailed behavioral analysis of home cage activity, the response to handling, and transportation to and observations within an open-field apparatus with ancillary measures of basal muscle tone, muscle strength, and tremor in a functional observation battery, as well as quantitative measurements of 3-dimensional activity in an automated photobeam arena. Cost-cutting initiatives or a radical application of the “reduce use” principle of the 3 Rs only jeopardize the spirit, intent, and predictive validity of tier I safety testing assays dictated by current drug safety guidelines.
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Santos, Carolina Leana, Paulo Rita, and João Guerreiro. "Improving international attractiveness of higher education institutions based on text mining and sentiment analysis." International Journal of Educational Management 32, no. 3 (April 9, 2018): 431–47. http://dx.doi.org/10.1108/ijem-01-2017-0027.
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Purpose The increasing competition among higher education institutions (HEI) has led students to conduct a more in-depth analysis to choose where to study abroad. Since students are usually unable to visit each HEIs before making their decision, they are strongly influenced by what is written by former international students (IS) on the internet. HEIs also benefit from such information online. The purpose of this paper is to provide an understanding of the drivers of HEIs success online. Design/methodology/approach Due to the increasing amount of information published online, HEIs have to use automatic techniques to search for patterns instead of analysing such information manually. The present paper uses text mining (TM) and sentiment analysis (SA) to study online reviews of IS about their HEIs. The paper studied 1938 reviews from 65 different business schools with Association to Advance Collegiate Schools of Business accreditation. Findings Results show that HEIs may become more attractive online if they financially support students cost of living, provide courses in English, and promote an international environment. Research limitations/implications Despite the use of a major platform with a broad number of reviews from students around the world, other sources focussed on other types of HEIs may have been used to reinforce the findings in the current paper. Originality/value The study pioneers the use of TM and SA to highlight topics and sentiments mentioned in online reviews by students attending HEIs, clarifying how such opinions are correlated with satisfaction. Using such information, HEIs’ managers may focus their efforts on promoting international attractiveness of their institutions.
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Dynich, Andrei, and Yanzhang Wang. "Analysis of novelty of a scientific text as a basis for assessment of efficiency of scientific activities." Journal of Organizational Change Management 30, no. 5 (August 14, 2017): 668–82. http://dx.doi.org/10.1108/jocm-10-2016-0226.
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Purpose The purpose of this paper is to complement an available system of qualitative analysis of efficiency of scientific activities with assessment of novelty of a subject of research that gives a more complete pattern for evaluating the efficiency of efforts of both scientists and research teams. Design/methodology/approach The approach is based on detection of specified linguistic patterns with further evaluation of similarity and novelty scores of obtained definitions at the sentence level. Findings This work presents an algorithm of automatic search for a new subject of research in scientific papers on the basis of statistical and linguistic analyses of description of new terms. Application of patterns specified in a given manuscript with further utilization of well-known methods of similarity and novelty detection scores makes it possible to evaluate the degree of novelty of a subject of research. Practical implications As a practical application of the proposed algorithm, the algorithm of determination of authority of a scientist will facilitate assessment of personal contributions of certain authors made in a certain field of study. Originality/value The main contribution of a given manuscript is in application of linguistic patterns recognition and calculation of similarity and novelty scores to the area of scientific results with further proposition of the method of automatic search for a new subject of research in scientific manuscripts.
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Smith, Thomas J. "Human Factors and Design Factors: Two Sides of the Same Coin?" Proceedings of the Human Factors and Ergonomics Society Annual Meeting 38, no. 9 (October 1994): 520. http://dx.doi.org/10.1177/154193129403800913.
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Since its emergence, the field of human factors has been engaged in an ongoing debate regarding the scientific essence of the discipline. Many observers believe that the crux of the debate centers upon the link between human factors and design factors, or performance-design interaction. Presenters in this symposium offer a series of perspectives on this theme, in relation to: (1) emphasis on behavioral considerations in design of complex automated systems (Meister); (2) emphasis on contextual specificity in teaching of design (Moray); (3) an ecological analysis of human-environment interaction as an integrated system (Flach); and (4) core principles of human factors science centered upon performance-design interaction (Smith). The rationale for this symposium rests upon the widespread belief that the relationship between performance and design represents the defining issue of human factors science. That is, through its focus on functional integration of performance and design, human factors departs from the traditional dissected treatment of these phenomena by psychology and engineering. Therefore, the status of human factors science as an integral discipline may be considered to rest upon the answer to the question of how and why human behavior and performance vary in relation to design factors in the performance environment. Papers presented in this symposium provide insight into this question from a number of different perspectives.
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Wang, Hao, Nathan Lau, and Ryan M. Gerdes. "Examining Cybersecurity of Cyberphysical Systems for Critical Infrastructures Through Work Domain Analysis." Human Factors: The Journal of the Human Factors and Ergonomics Society 60, no. 5 (April 17, 2018): 699–718. http://dx.doi.org/10.1177/0018720818769250.
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Objective: The aim of this study was to apply work domain analysis for cybersecurity assessment and design of supervisory control and data acquisition (SCADA) systems. Background: Adoption of information and communication technology in cyberphysical systems (CPSs) for critical infrastructures enables automated and distributed control but introduces cybersecurity risk. Many CPSs employ SCADA industrial control systems that have become the target of cyberattacks, which inflict physical damage without use of force. Given that absolute security is not feasible for complex systems, cyberintrusions that introduce unanticipated events will occur; a proper response will in turn require human adaptive ability. Therefore, analysis techniques that can support security assessment and human factors engineering are invaluable for defending CPSs. Method: We conducted work domain analysis using the abstraction hierarchy (AH) to model a generic SCADA implementation to identify the functional structures and means–ends relations. We then adopted a case study approach examining the Stuxnet cyberattack by developing and integrating AHs for the uranium enrichment process, SCADA implementation, and malware to investigate the interactions between the three aspects of cybersecurity in CPSs. Results: The AHs for modeling a generic SCADA implementation and studying the Stuxnet cyberattack are useful for mapping attack vectors, identifying deficiencies in security processes and features, and evaluating proposed security solutions with respect to system objectives. Conclusion: Work domain analysis is an effective analytical method for studying cybersecurity of CPSs for critical infrastructures in a psychologically relevant manner. Application: Work domain analysis should be applied to assess cybersecurity risk and inform engineering and user interface design.
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Bellini, Claudia, Roberto Guerranti, Francesca Cinci, Eva Milletti, and Carlo Scapellato. "Defining and Managing the Preanalytical Phase With FMECA: Automation and/or “Human” Control." Human Factors: The Journal of the Human Factors and Ergonomics Society 62, no. 1 (September 16, 2019): 20–36. http://dx.doi.org/10.1177/0018720819874906.
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Objective: Our scope is to provide methodological elements on how to manage effectively the preanalytical phase in the laboratory testing process, by objectively measuring the risk connected to the phases handled by man with respect to those managed by machines. Background: Preanalytical errors account for most of the mistakes related to laboratory testing and can affect patient care. Hence, it is necessary to manage the risk connected to the preanalytical phase, as required by certification and accreditation bodies. The risk assessment discloses the steps at greater risk and gives indications to make decisions. Method: We have reviewed the state of art in the automation of the preanalytical phase, addressing needs and problems. We have used the proactive risk assessment methodology FMECA (Failure Mode, Effects, and Criticality Analysis) to identify the most critical phases in our preanalytical process and have calculated the risk associated. Results: The most critical phases were the human controlled ones. In particular, the highest risk indexes were associated to manual acceptance of test orders, identification of the patients, tube labeling, and sample collection. Conclusion: Automation in the preanalytical phase is fundamental to replace, support, or extend the human contribution. Nevertheless each organization is different about workloads and competencies, so the most suitable management must be tailor-made in each context. Application: We present a method by which each organization is able to find its best balance between automation and human contribution in the control of the preanalytical phase.
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Mülazimoğlu, Emre, Murat P. Çakır, and Cengiz Acartürk. "The Role of Visual Features in Text-Based CAPTCHAs: An fNIRS Study for Usable Security." Computational Intelligence and Neuroscience 2021 (May 4, 2021): 1–24. http://dx.doi.org/10.1155/2021/8842420.
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To mitigate dictionary attacks or similar undesirable automated attacks to information systems, developers mostly prefer using CAPTCHA challenges as Human Interactive Proofs (HIPs) to distinguish between human users and scripts. Appropriate use of CAPTCHA requires a setup that balances between robustness and usability during the design of a challenge. The previous research reveals that most usability studies have used accuracy and response time as measurement criteria for quantitative analysis. The present study aims at applying optical neuroimaging techniques for the analysis of CAPTCHA design. The functional Near-Infrared Spectroscopy technique was used to explore the hemodynamic responses in the prefrontal cortex elicited by CAPTCHA stimulus of varying types. The findings suggest that regions in the left and right dorsolateral and right dorsomedial prefrontal cortex respond to the degrees of line occlusion, rotation, and wave distortions present in a CAPTCHA. The systematic addition of the visual effects introduced nonlinear effects on the behavioral and prefrontal oxygenation measures, indicative of the emergence of Gestalt effects that might have influenced the perception of the overall CAPTCHA figure.
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Fetterman, Adam K., Nicholas D. Evans, Julie J. Exline, and Brian P. Meier. "What shall we call God? An exploration of metaphors coded from descriptions of God from a large U.S. undergraduate sample." PLOS ONE 16, no. 7 (July 12, 2021): e0254626. http://dx.doi.org/10.1371/journal.pone.0254626.
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People use numerous metaphors to describe God. God is seen as a bearded man, light, and love. Based on metaphor theories, the metaphors people use to refer to God reflect how people think about God and could, in turn, reflect their worldview. However, little work has explored the common metaphors for God. This was the purpose of the current investigation. Four trained raters coded open-ended responses from predominantly Christian U.S. undergraduates (N = 2,923) describing God for the presence or absence of numerous metaphoric categories. We then assessed the frequency of each of the metaphor categories. We identified 16 metaphor categories that were present in more than 1% of the responses. The top categories were “GOD IS POWER,” “GOD IS HUMAN,” and “GOD IS MALE.” These findings were similar across religious affiliations. We attempted to support our coding analysis using top-down and bottom-up automated language analysis. Results from these analyses provided added confidence to our conclusions. We discuss the implications of our findings and the potential for future studies investigating important psychological and behavioral outcomes of using different metaphors for God.
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Ene, Irina, Mihai-Ionuț Pop, and Bogdan Nistoreanu. "Qualitative and quantitative Analysis of consumers perception regarding anthropomorphic AI designs." Proceedings of the International Conference on Business Excellence 13, no. 1 (May 1, 2019): 707–16. http://dx.doi.org/10.2478/picbe-2019-0063.
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Abstract Business intelligence and analytics are nowadays being integrated into diverse industries, from healthcare to customer relationship management and behavioral profiling, due to the competitive advantages that they offer. Nevertheless, most companies try to integrate as many forms of business intelligence systems as possible into different internal processes. This overall digitization applied to more and more business departments is being analyzed with both curiosity and reluctance. The decision regarding the implementation of innovative forms of automation is taken in an attempt to discover and solve business challenges. However, there are several issues involved, which need to be addressed. One of the risks that are being discussed in the research environment refers to the level of acceptance of artificial intelligence systems. The tolerance and overall readiness of the consumers towards innovation and technology is one of the critical factors which need to be determined before implementing disruptive business intelligence systems. Moreover, in an effort to make devices friendlier to consumers, some developers chose to assign anthropomorphic appearances and even create individual identities for each artificial intelligence system. In this context, it is important for most companies investing in intelligent automation systems to determine to which extend the use of anthropomorphic designs impacts the customer’s perception. The objective of this research paper is to analyze the unconscious reaction of consumers towards two opposite designs of artificial intelligence systems: a robotic-like form and a human-like design. Based on this difference, a photo collage was created figuring two pictures: one with a metallic robot having a conversation with a human being and one with a robot with a strong anthropomorphic figure found in the same situation. For the analysis, an eye tracking device was used, in order to measure the point of gaze, the unconscious motion of the eyes, along with the time spent on each fixation and the order in which different elements were fixated upon by the respondents. As the eye-tracking device can generate data in various forms, this research includes both qualitative and quantitative analyses of the results, which confirm the same hypothesis, regarding the consumer’s preference towards artificial intelligence systems with robotic designs.
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Gao, Yin, Lijuan Cui, Jianjun Liu, Wei Li, and Yinru Lei. "China's coastal-wetland change analysis based on high-resolution remote sensing." Marine and Freshwater Research 71, no. 9 (2020): 1161. http://dx.doi.org/10.1071/mf19062.
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Coastal wetlands not only have abundant biodiversity and high productivity, but they also play an irreplaceable and important role in regional ecosystems. Because of the complex structure and dynamic characteristics of coastal wetlands, it is difficult to observe the spatial changes of coastal wetlands on a large scale and improve data reliability. In this study, a spatially constrained manual-interpretation method based on nationwide high-resolution images in 2017 was adopted to extract China’s coastal wetland distribution, and, then, the second national wetland-survey data from 2011 were used as a baseline for change analysis. The results showed that under the influence of natural conditions and human activities, China’s coastal wetlands have decreased in distribution in the past 6 years. The situation of coastal wetland reclamation is critical, and the hotspot regions are mainly distributed in Bohai Bay, middle of Jiangsu province and Hangzhou Bay. Farming reclamation is another factor that occupies coastal wetlands in China, and main occupied wetlands are mudflats, shallow sea, tidal flats, intertidal salt marshes and estuarine waters. Tide is the main factor affecting extraction of wetlands, the spatially constrained method had a positive effect on wetland detection, and has potential to improve automatic algorithms of complex coastal wetlands.
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Dolník, Juraj. "Lexikálny Význam V Recepcii." Journal of Linguistics/Jazykovedný casopis 69, no. 3 (December 1, 2018): 302–15. http://dx.doi.org/10.2478/jazcas-2019-0012.
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Abstract Asking first about how the lexical meaning manifests itself as we experience it in a communicative event, the author explores the background of the ways in which we are able to perceive the meaning of words in texts. One useful way of thinking about how recipients react to the words in utterances is in terms of behavioural and actional lexical meaning. The first refers to the understanding of meaning, the second corresponds to interpretations of words when the recipient does not succeed in the process of natural understanding of words. These terms lead to questions about the rationality of language. One aspect of this rationality is the function of the intentionalemergent mechanism that adjusts the interplay of automatic and deliberate use of language. This mechanism has its roots in the fundamental human nature: we are behaviouralactional beings. Pragmatic analysis sheds light on how hearers understand and interpret what they hear with regard to their conceptual knowledge associated with words.
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Stowers, Kimberly, James Oglesby, Shirley Sonesh, Kevin Leyva, Chelsea Iwig, and Eduardo Salas. "A Framework to Guide the Assessment of Human–Machine Systems." Human Factors: The Journal of the Human Factors and Ergonomics Society 59, no. 2 (March 2017): 172–88. http://dx.doi.org/10.1177/0018720817695077.
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Objective: We have developed a framework for guiding measurement in human–machine systems. Background: The assessment of safety and performance in human–machine systems often relies on direct measurement, such as tracking reaction time and accidents. However, safety and performance emerge from the combination of several variables. The assessment of precursors to safety and performance are thus an important part of predicting and improving outcomes in human–machine systems. Method: As part of an in-depth literature analysis involving peer-reviewed, empirical articles, we located and classified variables important to human–machine systems, giving a snapshot of the state of science on human–machine system safety and performance. Using this information, we created a framework of safety and performance in human–machine systems. Results: This framework details several inputs and processes that collectively influence safety and performance. Inputs are divided according to human, machine, and environmental inputs. Processes are divided into attitudes, behaviors, and cognitive variables. Each class of inputs influences the processes and, subsequently, outcomes that emerge in human–machine systems. Conclusion: This framework offers a useful starting point for understanding the current state of the science and measuring many of the complex variables relating to safety and performance in human-machine systems. Application: This framework can be applied to the design, development, and implementation of automated machines in spaceflight, military, and health care settings. We present a hypothetical example in our write-up of how it can be used to aid in project success.