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Towards model based prediction of human error rates in interactive systems

D. Leadbetter, A. Hussey, P. Lindsay, A. Neal, M. Humphreys
Proceedings Second Australasian User Interface Conference. AUIC 2001  
We present the models of the HCI and operator in an air-traffic control (ATC) system simulation, and discuss the role of these in the prediction of human error rates.  ...  We are developing methods for determining the likelihood of operator errors which combine current theory on the psychological causes of human errors with formal methods for modelling human-computer interaction  ...  methods, such as THERP (Technique for Human Error Rate Prediction) [10] .  ... 
doi:10.1109/auic.2001.906275 dblp:conf/auic/LeadbetterHLNH01 fatcat:n5k373zyunb47g5jaugodz2afy

Speech Rate Adjustments in Conversations With an Amazon Alexa Socialbot

Michelle Cohn, Kai-Hui Liang, Melina Sarian, Georgia Zellou, Zhou Yu
2021 Frontiers in Communication  
Yet, we do not see differences in hyperarticulation or entrainment in response to ASR errors, or on the basis of user ratings of the interaction.  ...  Overall, this work has implications for human-computer interaction and theories of linguistic adaptation and entrainment.  ...  In the case of voice-AI socialbots, the cues of humanity could be even more robust since the system is designed for social interaction.  ... 
doi:10.3389/fcomm.2021.671429 fatcat:xo3q46qycnadtnlkqasear4lfe

Predicting hyperarticulate speech during human-computer error resolution

Sharon Oviatt, Margaret MacEachern, Gina-Anne Levow
1998 Speech Communication  
error base-rate.  ...  during human-computer error resolution, (3) to provide a unified theoretical model for interpreting and predicting users' spoken adaptations during system error handling, and (4) to outline the implications  ...  Recognition accuracy and user acceptance of pen interfaces. In: Proc. Conf. on Human Factors in Computing Systems, Denver CHI'95. ACM Press, New York, pp. 503-510.  ... 
doi:10.1016/s0167-6393(98)00005-3 fatcat:v6x4m5xcqfcs5jb67girvmgv4e

Designing a human computer interface system based on cognitive model

M. Mayilvaganan, D. Kalpanadevi
2014 2014 IEEE International Conference on Computational Intelligence and Computing Research  
Aim of this research to focus on Human Computer Interface (HCI) system, designed by interface style, interaction techniques, tasks based on cognitive model can be discussed.  ...  Observational evaluation can be performed based on laboratory with end user by developing of software on the basis of user interface design for examine the human factor experts determines the exact task  ...  In addition, EPIC provides the foundation for a computational model of user error, which could be used to predict potential causes of error in a system.  ... 
doi:10.1109/iccic.2014.7238347 fatcat:bmvta4zgwnflhfvu33caacusoa

Designing Medical Interactive Systems Via Assessment of Human Mental Workload

Luca Longo
2015 2015 IEEE 28th International Symposium on Computer-Based Medical Systems  
In clinical settings, Human-computer systems need to be designed in a way that medical errors are reduced and patient care is enhanced.  ...  Inspection methods are usually employed in HCI to assess usability of interactive systems.  ...  satisfaction high system success low error rate high productivity/safety high response time/error rate small mental residual capacity low performance Figure 1 .  ... 
doi:10.1109/cbms.2015.67 dblp:conf/cbms/Longo15 fatcat:s6k4qufpyrhz3lmqbow4mfivbi

Anticipatory robot control for efficient human-robot collaboration

Chien-Ming Huang, Bilge Mutlu
2016 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI)  
To work seamlessly and efficiently with their human counterparts, robots must similarly rely on predictions of their users' intent in planning their actions.  ...  In this paper, we present an anticipatory control method that enables robots to proactively perform task actions based on anticipated actions of their human partners.  ...  ] , [30] ) interactions have reported a constant error rate in observers' ability to accurately determine the gaze targets of humans or robots.  ... 
doi:10.1109/hri.2016.7451737 dblp:conf/hri/0001M16 fatcat:ljgt767aonebxp3pycj7zjt7za

User-centered modeling for spoken language and multimodal interfaces

S. Oviatt
1996 IEEE Multimedia  
By modeling difficult sources of linguistic variability in spontaneous speech and language, interfaces can be designed that transparently guide human input to match system processing capabilities.  ...  In the case of speech, especially interactive speech in which dialogue partners alternate turns, these landmark features include disfluencies, errors and repairs, confirmation requests and feedback, prosodic  ...  Acknowledgments This research has been supported in part by Grants IRI-9213472 and IRI-9530666 from the National Science Foundation.  ... 
doi:10.1109/93.556458 fatcat:s4wrmd2ifbglnbihttjnitgtry

Model-based and empirical evaluation of multimodal interactive error correction

Bernhard Suhm, Brad Myers, Alex Waibel
1999 Proceedings of the SIGCHI conference on Human factors in computing systems the CHI is the limit - CHI '99  
Our model is a first step towards formalizing multimodal (recognition-based) interaction.  ...  To extrapolate results from this user study we developed a performance model of multimodal interaction that predicts input speed including time needed for error correction.  ...  The Performance Model Our performance model of recognition-based multimodal human-computer interaction predicts interaction throughput.  ... 
doi:10.1145/302979.303165 dblp:conf/chi/SuhmMW99 fatcat:bl5gpczpufgkflrjwqevsoyyuu

A multiple-predictor approach to human motion prediction

Przemyslaw A. Lasota, Julie A. Shah
2017 2017 IEEE International Conference on Robotics and Automation (ICRA)  
The ability to accurately predict human motion is imperative for any human-robot interaction application in which the human and robot interact in close proximity to one another.  ...  We address this problem by introducing a multiple-predictor system (MPS) for human motion prediction.  ...  One way in which such interaction can be achieved is through robot adaptation based on prediction of human motion.  ... 
doi:10.1109/icra.2017.7989265 dblp:conf/icra/LasotaS17 fatcat:gpap7i7cmvdopc4plwoqpebuxa

Translational Bioinformatics Support for Personalized and Systems Medicine: Tasks and Challenges

Qing Yan
2013 Translational Medicine  
The elucidation of the function of enzymes in the metabolic network can help with the reconstruction of genome-scale metabolic models toward the development of personalized medicine.  ...  Based on the development of pharmacogenomics and systems biology, personalized medicine may help change the gear from reductionism-based and disease-centered medical practice toward a systems-based, integrative  ...  The Scientific Bases of Personalized and Systems Medicine The current challenges in healthcare including the high costs and high rates of Adverse Drug Reactions (ADRs) call for fundamental changes in both  ... 
doi:10.4172/2161-1025.1000e120 fatcat:62lgx6zlcnarvkinxqaqm42reu

Trust and Cognitive Load During Human-Robot Interaction [article]

Muneeb Imtiaz Ahmad, Jasmin Bernotat, Katrin Lohan, Friederike Eyssel
2019 arXiv   pre-print
We also found a triple interaction impact between robot-type, error-rate and participant's ratings of trust.  ...  Our results are interesting and call further investigation of the impact of physical anthropomorphism in combination with variable error-rates of the robot.  ...  This interaction however, is contrasting our predictions a human-like robot with a low error rate to evoke higher levels of trust towards the robot after HRI (H3a, H3b) .  ... 
arXiv:1909.05160v1 fatcat:dod36c7ydrbhto5zqzozy3neoi

Dopamine Enhances Model-Based over Model-Free Choice Behavior

Klaus Wunderlich, Peter Smittenaar, Raymond J. Dolan
2012 Neuron  
Decision making is often considered to arise out of contributions from a model-free habitual system and a model-based goal-directed system.  ...  Here, we investigated the effect of a dopamine manipulation on the degree to which either system contributes to instrumental behavior in a two-stage Markov decision task, which has been shown to discriminate  ...  In this framework, reward prediction errors that are in line with model-based predictions are enhanced, while reward prediction errors that are in opposition with model-based predictions are attenuated  ... 
doi:10.1016/j.neuron.2012.03.042 pmid:22884326 pmcid:PMC3417237 fatcat:udy2yfo5zbdefbctnfsfv6o6uu

Toward Interactive Relational Learning

Ryan Rossi, Rong Zhou
2016 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
data representation, as well as perform evaluation, analyze errors, and make adjustments and refinements in a closed-loop. iRML requires fast real-time learning and inference methods capable of interactive  ...  This paper introduces the Interactive Relational Machine Learning (iRML) paradigm in which users interactively design relational models by specifying the various components, constraints, and relational  ...  Many of the components in our i RML system may be explored using interactive visualization and analytic techniques, including the attribute to predict, initial features to use (non-relational and graph-based  ... 
doi:10.1609/aaai.v30i1.9830 fatcat:bjuqtdgzgnaddifb5jt5i7yss4

The Response Shift Paradigm to Quantify Human Trust in AI Recommendations [article]

Ali Shafti, Victoria Derks, Hannah Kay, A. Aldo Faisal
2022 arXiv   pre-print
Explainability, interpretability and how much they affect human trust in AI systems are ultimately problems of human cognition as much as machine learning, yet the effectiveness of AI recommendations and  ...  We developed and validated a general purpose Human-AI interaction paradigm which quantifies the impact of AI recommendations on human decisions.  ...  The funders were not involved in the design or publication of this study. The authors declare no competing financial interests.  ... 
arXiv:2202.08979v1 fatcat:ebeoetfzwjhbdod6ppwq2lsrxq

Automatic Prediction of Perceived Traits Using Visual Cues under Varied Situational Context

Jyoti Joshi, Hatice Gunes, Roland Goecke
2014 2014 22nd International Conference on Pattern Recognition  
Experimental results indicate that a weighted model show major improvement for automatic prediction of perceived physical and behavioral traits.  ...  Automatic assessment of human personality traits is a non-trivial problem, especially when perception is marked over a fairly short duration of time.  ...  On average, error rate of the weighted model is less (0.66) compared to the error rate of the average model (1.02).  ... 
doi:10.1109/icpr.2014.492 dblp:conf/icpr/JoshiGG14 fatcat:kzeehd3u3rc45oh6hw62f7wagq
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