Correlating Trust with Signal Detection Theory Measures in a Hybrid Inspection System
|
|
- Peter Newton
- 5 years ago
- Views:
Transcription
1 Correlating Trust with Signal Detection Theory Measures in a Hybrid Inspection System Xiaochun Jiang Department of Industrial and Systems Engineering North Carolina A&T State University 1601 E Market St Greensboro, NC Mohammad T. Khasawneh, Sittichai Kaewkuekool, Shannon R. Bowling, Brian J. Melloy, Anand K. Gramopadhye Department of Industrial Engineering Clemson University Clemson, SC Abstract Signal detection theory provides a precise language and graphic notation for analyzing decision making in the presence of uncertainty. It has been widely used in visual inspection to evaluate system performance. Recent studies also found that trust in automation plays a very important role in visual inspection. Particularly, trust has a great influence on the use of automation in a hybrid inspection system where humans and machines work cooperatively. This study attempts to correlate trust with Signal Detection Theory measures in a hybrid inspection system. The results indicate there is a correlation between these two. Keywords Signal Detection Theory, Automation, Trust, Hybrid Inspection 1. Introduction Customer awareness regarding product quality and increased incidences of product liability litigation have increased the importance of the inspection process in manufacturing industries [1]. To remain competitive, manufacturers can accept only extremely low defect rates, often measured in parts per million. This situation requires almost perfect inspection performance in the search for nonconformities in a product, and the two functions central to inspection, visual search and decision making [2], have been shown to be the primary determinants of inspection performance [3]. If inspection is to be successful, it is critical that these functions be performed effectively and efficiently. Since consumers demand that a product be free of defects, 100% inspection has to be applied in order to achieve this zero-defect quality [4]. Unfortunately, while need for error-free detection is important, human inspectors are less than 100% reliable [5]. To overcome this deficiency and to remove errors from the system, many companies are moving towards automated systems designed for 100% inspection. This growing interest in automated visual inspection has resulted in the development of faster, more efficient image processing equipment, and advances in computer technology, sensing devices, image processing, and pattern recognition. Thus, automated systems are now not only better but less expensive as well. As a result of this trend, some of the tasks earlier performed by humans can now be allocated to computers so that the role of inspectors has changed from that of an active controller to that of a supervisor [6]. Given this change, it becomes critical to understand the role of both humans and computers in visual inspection, especially since the availability of computer-based systems and microprocessor-based optically-sophisticated devices has led designers to automate the various functions of the inspection task assuming that this is the solution to eliminating human errors from the inspection process, However as Hou [4] pointed out, humans and computers each have their own advantages and disadvantages. Humans have the innate ability to recognize patterns, make
2 rational decisions, and quickly adapt to new situations. An automated system cannot surpass the superior decisionmaking ability of the human inspector. Thus, human inspectors are more suited when complex decision making is involved [7]. However, they are limited in their computational ability and short-term memory. On the other hand, computers are good at computation, memory storage and retrieval, but are poor at detecting signals in noise and have very little capacity for creative or inductive functions [8]. Therefore, neither an entirely human nor a purely automated system may fully achieve the desired performance in an inspection task. It is possible, though, that superior performance could be achieved by a hybrid inspection system in which search and decision-making tasks are allocated to either humans, machines or both. Hou [4] proposed seven alternate hybrid inspection systems listed in Table 1 with Alternative Seven, the most complicated and flexible, chosen for use in the current study. Table 1. Allocation alternatives in hybrid inspection task Alternative Search Decision-making System Mode 1 Human Computer Hybrid 2 Computer Human Hybrid 3 Human Human + Computer Hybrid 4 Computer Human + Computer Hybrid 5 Human + Computer Human Hybrid 6 Human + Computer Computer Hybrid 7 Human + Computer Human + Computer Hybrid To measure inspection system performance, Signal Detection Theory (SDT) is often used to model the decisionmaking process in an inspection task [9]. SDT provides a precise language and graphic notation for analyzing decision making in the presence of uncertainty. It has been widely used in visual inspection to evaluate system performance. An inspector gathers data from each observation and decides if a particular item was sampled from a distribution of conforming items or a distribution of nonconforming items. Due to continuous variation in noise underlying both of these distributions, few nonconforming items will be classified as nonconforming. In this research, the simplest version of SDT is considered, which assumes the signal (nonconforming) and noise (conforming) are normally distributed and have equal variances (Figure 1). SDT defines inspection performance using two parameters: sensitivity and response criterion. 1. Sensitivity (d ): This refers to the ability of the inspector to discriminate between a conforming and a nonconforming item and is a function of the overlap of the two distributions as shown in Figure 1. This can be expressed as d ' = z( p1 ) + z( p2 ), where z(p 1 ) and z(p 2 ) are standard normal deviates corresponding to p 1 and p Response criterion (β): This refers to an inspector s response bias or the tendency of the inspector to call an item conforming or nonconforming. Given that an inspector has to call an item conforming or nonconforming, there are four possible outcomes: Hit: Saying the item is nonconforming when it is False alarm: Saying the item is nonconforming when it is conforming Miss: Saying the item is conforming when it is nonconforming Correct Rejection: Saying the item is conforming when it is Refer to Figure 1, if the response criterion was placed where the two distributions cross, beta would equal one indicating a neutral system. If the response criterion was shifted to the right, beta increases and the inspector will call an item nonconforming less often and hence will have fewer hits, but will also have fewer false alarms, indicating a conservative system. If the response criterion was shifted to the left, beta decreases and the inspector will call an item nonconforming more often and hence will have more hits, but will also have more false alarms,
3 indicating a risky system. Figure 1. Signal detection theory To address best system performance issue [6], though, it is critical that we study the issue of trust in hybrid inspection systems because human trust in automation can directly impact inspection quality and overall inspection performance. In response to this need, a trust questionnaire was developed to determine the effects of the level of trust an operator has in hybrid inspection systems [10]. As shown in Figure 2, this questionnaire incorporated the four dimensions of trust competence, predictability, reliability and faith derived from the multidimensional construct developed by Muir [11] and used them to determine which were the best predictors of trust. Using this questionnaire, a study [12] was conducted to measure trust in an inspection system, the results indicating that measuring trust in a hybrid inspection environment using trust questionnaire is feasible. Figure 2. Screen shot of the trust questionnaire Since trust can be measured using a questionnaire, the next step is to apply it to a hybrid inspection system with trust between the human and the machine becoming a primary focus in evaluating inspection performance. Since human trust can be affected by the accuracy of the machine, it is hypothesized that by manipulating the types of errors made by a system, the influence of trust can be investigated. The objective of this research is to correlate trust in a hybrid inspection system with system response criterion.
4 2. Methodology 2.1 Subjects The subjects were 6 students, both graduate and undergraduate, enrolled at Clemson University between the ages of 18 and 28. Students can be used as subjects in lieu of inspectors because as Gallwey and Drury [13] have shown, minimal differences exist between inspectors and student subjects on simulated tasks. The subjects were screened for 20/20 vision, corrected if necessary, and paid $5.00/hour for their time. 2.2 Stimulus Material The task was a simulated visual inspection task of printed circuit board inspection implemented on a Pentium III computer with a 19 high-resolution (1024 x 768) monitor. The input devices were a Microsoft standard keyboard and a Microsoft one-button mouse. The task consisted of inspecting simulated PCB images which were developed using adobe PhotoShop 5.5. The inspector searched the PCB boards for six categories of defects. Four categories of defects could occur on any of the individual components (resistors, capacitors, transistors and integrated circuit). These defect categories are missing components, wrong components, inverted components, misaligned components, trace defects and board defects. 2.3 Inspection Task A Human/computer share search and decision-making hybrid inspection system was used in this study. In this system, both computers and humans searched for defects and made the decision on the board with the human having the final decision about whether to accept or override the computer search or decision-making [14]. During visual search, PCB boards containing 1, 2, 3, or no defects were presented to the subjects, whose task was to locate all potential defects and name them. After locating the defect, they clicked the mouse on it and chose its name from a dropdown box listing all possible defects. At the same time, the computer performed the same search task. However, subjects could override the computer if they did not agree with its search results. Then, the computer made its conformance decision and the subjects made their final conformance decisions, either agreeing or disagreeing with the computer. Once the board was classified, the image of the next board would be presented to the subjects. Each inspection task consisted of 48 randomly ordered PCB boards 12 of each zero-defect, singledefect, two-defect, and three-defect boards. Figure 3 shows a typical decision-making response by the computer and the human inspector s decision to override this decision. Figure 3. Screenshot from a hybrid inspection system 2.4 Experimental Design This study used a single factor (response criterion) within subject design. The three levels of the response criterion were conservative (high false alarms / low misses), neutral (equal false alarms and misses), and risky (high misses /
5 low false alarms). The sensitivities of these three systems were designed to be very close (d is equal to 0.65), hence, the current study focus only on the relationship between the trust and the response criterion. The response criteria for the three systems are set as 1.16 for the conservative system, 1.0 for the neutral system, and 0.86 for the risky system. Two Latin squares with different orders were used to cancel out the order effects. All treatments were randomly assigned to the three Latin letters. 2.5 Procedure The study took place over a seven-day period. Day One was devoted to training the subjects and during the next six days, data were collected on the criterion tasks. A more detailed explanation of the activities conducted on each day can be seen below. On Day One, each subject was required to complete a consent form and a demographics questionnaire. Following this step, instructions were read to the subjects to ensure their understanding of the experiment. Next, all were trained and given three separate tests before beginning the experiment. After completion of defect training, the subjects underwent training sessions on defect matching, single defect inspection and multiple-defect training. Following each session, the subjects were administered a test. Only those subjects who secured a minimum score were allowed to proceed to the next step. 1. Defect matching: PCBs with a marked single defect were displayed on the screen, and subjects classified it by choosing the correct name from a dropdown box. They were provided with immediate feedback about the correctness of their responses. 2. Single-defect training: PCBs with a single defect were displayed on the screen, and subjects located and then classified it by choosing the name from a dropdown box. They were provided with immediate feedback on their search performance using speed and accuracy measures. 3. Multiple-defect training: PCBs with 1, 2, or 3 defects were displayed on the screen, and subjects first visually searched for and then classified them. They were provided with immediate feedback on their performance using speed and accuracy measures. On Day Two, to develop a baseline of a subject s trust of the system, each was administered a criterion task with 24 PCB boards to inspect using a perfect hybrid inspection system, i.e., a system did not make any errors. On Day Three, subjects were required to inspect 48 PCB boards under pre-assigned experimental conditions and to fill out a trust questionnaire on completion of the task. From Day Four to Day Seven, the subjects followed the same procedure and were assigned the other two experimental conditions. On completion of the study, each subject was debriefed. 3. Results and Analysis 3.1 Subjective Ratings of Trust To evaluate the subject s trust in the system, subjective ratings of each trust component as well as the overall trust were solicited. A continuous rating scale from 0 to 100 was used in the study. When the subject dragged the scroll bars, the score was displayed automatically (Figure 2). 3.2 Correlation Analysis As shown in Table 2, the results indicate that trust components as well as overall trust have a positive correlation with system response criterion. The larger the response criterion is, the more trust the inspectors have in automation. Clearly, inspector s trust in automation was affected by the SDT measure, response criterion. A possible explanation is that, larger response criterion means a more conservative system [15]. Although a conservative system makes fewer hits, it also makes fewer false alarms. Since false alarms are commissive errors, which the inspectors cannot overlook if they are paying attention, whereas misses are omissive errors which the inspectors are likely to miss if they are not paying attention [15]. Therefore, inspector s trust of a computer with a risky bias may be affected more than it would be for computers with a conservative or neutral bias.
6 Table 2. Results of the correlation analysis Trust Competence Predictability Faith Reliability Overall Correlation with Response Criterion (<.05) (<.05) (<.05) (<.05) (<.05) Another interesting finding is that all four trust components are positively correlated with the system response criterion, which further indicates the trust questionnaire is a very useful tool to measure trust in a hybrid inspection environment. 4. Conclusion This study used a trust questionnaire to measure inspector s trust in automation in three hybrid inspection systems with different response criteria. To explore the relationship of the SDT measure, response criterion, and trust in automation, a correlation analysis was conducted. The results showed that trust measures were positively correlated with system response criteria, indicating that the signal detection theory measure, response criterion, has great influence in inspector s trust in automation. References 1. Thapa, V. B., Gramopadhye, A. K. and Melloy, B. J., 1996, Evaluation of different training strategies to improve decision-making performance in inspection, The International Journal of Human Factors in Manufacturing, 6(3), Drury, C.G., 1992, Inspection performance, In Handbook of Industrial Engineering (second edition), G. Salvendy, John Wiley and Sons, New York. 3. Sinclair, M. A., 1984, Ergonomics of quality control, workshop document, International Conference on Occupational Ergonomics (Toronto). 4. Hou T., Lin L., Drury, C. G., 1993, An empirical study of hybrid inspection systems and allocation of inspection functions, International Journal of Human Factors in Manufacturing, Chin, R., 1988, Automated visual inspection: 1981 to 1987, Computer Vision, Graphics and Image Process, 41, Jiang, X., Gramopadhye, A. K., Melloy, B., and Grimes, L., 2003, Evaluation of best system performance: human, automated, and hybrid inspection systems, International Journal of Human Factors in Manufacturing (In press). 7. Drury, C. G. and Sinclair, M. A., 1983, Human and machine performance in an inspection task, Human Factors. 25, Kantowitz, B. H. and Sorkin, R. D., 1987, Allocation of functions, In Handbook of Human Factor, John Wiley and Sons: New York. 9. Jiang, X., Srinivasan, A., Gramopadhye, A., K., and Ferrell. W. G., 2002, Modeling Errors in Sampling Inspection: Effect of Degraded Performance, Quality Engineering, 15(1), Master, R., Bingham, J., Jiang, X., Gramopadhye, A. K., Melloy, B. J., Measurement of trust in hybrid inspection systems, Proceedings of the 10 th Annual industrial engineering research conference, May 20-22, 2001, Dallas, Texas, in press. 11. Muir, B. M., 1994, Trust in automation Part 1: Theoretical Issues in the study of trust and human intervention in automated systems, Ergonomics, 37, Jiang, X., Gramopadhye, A. K., Melloy, B. J., Grimes, L., Measuring trust in a hybrid inspection system, International Journal of Industrial Ergonomics, in review. 13. Gallwey, T. J., 1982, Selection Test for visual inspection on a multiple fault type task, Ergonomics, 25(11), Jiang, X., Bingham, J., Master R., Gramopadhye, A. K and Melloy B., 2002, A Visual inspection simulator for hybrid environments, International Journal of Industrial Engineering: Theory, Applications and Practice, 9(2): Sanders, M. S., and McCormick, E. J., 1993, Human Factors in Engineering and Design, McGraw-Hill, Inc., New York,
Measurement of human trust in a hybrid inspection system based on signal detection theory measures
International Journal of Industrial Ergonomics 34 (2004) 407 419 Measurement of human trust in a hybrid inspection system based on signal detection theory measures Xiaochun Jiang a, *, Mohammad T. Khasawneh
More informationThe Effects of Eye Movements on Visual Inspection Performance
The Effects of Eye Movements on Visual Inspection Performance Mohammad T. Khasawneh 1, Sittichai Kaewkuekool 1, Shannon R. Bowling 1, Rahul Desai 1, Xiaochun Jiang 2, Andrew T. Duchowski 3, and Anand K.
More informationTHE EFFECT OF EXPECTATIONS ON VISUAL INSPECTION PERFORMANCE
THE EFFECT OF EXPECTATIONS ON VISUAL INSPECTION PERFORMANCE John Kane Derek Moore Saeed Ghanbartehrani Oregon State University INTRODUCTION The process of visual inspection is used widely in many industries
More informationCombined Factors Effect of Menstrual Cycle and Background Noise on Visual Inspection Task Performance: a Simulation-based Task
Combined Factors Effect of Menstrual Cycle and Background Noise on Visual Inspection Task Performance: a Simulation-based Task Titis Wijayanto 1),3), Yutaka Tochihara 2), Andi R. Wijaya 3) and Setia Hermawati
More informationEmpirical Research Methods for Human-Computer Interaction. I. Scott MacKenzie Steven J. Castellucci
Empirical Research Methods for Human-Computer Interaction I. Scott MacKenzie Steven J. Castellucci 1 Topics The what, why, and how of empirical research Group participation in a real experiment Observations
More informationThe impact of inspector's cognitive style on performance in various visual inspection display tasks
Graduate Theses and Dissertations Graduate College 2010 The impact of inspector's cognitive style on performance in various visual inspection display tasks Chen-shuang Wei Iowa State University Follow
More informationCHAPTER 3. Methodology
CHAPTER 3 Methodology The purpose of this chapter is to provide the research methodology which was designed to achieve the objectives of this study. It is important to select appropriate method to ensure
More informationCHAPTER III RESEARCH METHODOLOGY
CHAPTER III RESEARCH METHODOLOGY In this chapter, the researcher will elaborate the methodology of the measurements. This chapter emphasize about the research methodology, data source, population and sampling,
More informationTopics. Experiment Terminology (Part 1)
Topics The what, why, and how of empirical research Group participation in a real experiment Observations and measurements Research questions Experiment terminology Experiment design Hypothesis testing
More informationExperimental Research in HCI. Alma Leora Culén University of Oslo, Department of Informatics, Design
Experimental Research in HCI Alma Leora Culén University of Oslo, Department of Informatics, Design almira@ifi.uio.no INF2260/4060 1 Oslo, 15/09/16 Review Method Methodology Research methods are simply
More informationEvaluation of CBT for increasing threat detection performance in X-ray screening
Evaluation of CBT for increasing threat detection performance in X-ray screening A. Schwaninger & F. Hofer Department of Psychology, University of Zurich, Switzerland Abstract The relevance of aviation
More informationPsychology Research Process
Psychology Research Process Logical Processes Induction Observation/Association/Using Correlation Trying to assess, through observation of a large group/sample, what is associated with what? Examples:
More informationAC : USABILITY EVALUATION OF A PROBLEM SOLVING ENVIRONMENT FOR AUTOMATED SYSTEM INTEGRATION EDUCA- TION USING EYE-TRACKING
AC 2012-4422: USABILITY EVALUATION OF A PROBLEM SOLVING ENVIRONMENT FOR AUTOMATED SYSTEM INTEGRATION EDUCA- TION USING EYE-TRACKING Punit Deotale, Texas A&M University Dr. Sheng-Jen Tony Hsieh, Texas A&M
More informationEvaluation of CBT for increasing threat detection performance in X-ray screening
Evaluation of CBT for increasing threat detection performance in X-ray screening A. Schwaninger & F. Hofer Department of Psychology, University of Zurich, Switzerland Abstract The relevance of aviation
More informationPsychology Research Process
Psychology Research Process Logical Processes Induction Observation/Association/Using Correlation Trying to assess, through observation of a large group/sample, what is associated with what? Examples:
More informationHuman Computer Interaction - An Introduction
NPTEL Course on Human Computer Interaction - An Introduction Dr. Pradeep Yammiyavar Professor, Dept. of Design, IIT Guwahati, Assam, India Dr. Samit Bhattacharya Assistant Professor, Dept. of Computer
More informationCHAPTER 3 METHOD AND PROCEDURE
CHAPTER 3 METHOD AND PROCEDURE Previous chapter namely Review of the Literature was concerned with the review of the research studies conducted in the field of teacher education, with special reference
More informationFramework Partners Incorporated
Framework Partners Incorporated Summary of Findings from the 2011 Ontario Association of Architects Intern Survey It is the purpose of this summary to describe the methodology, the reliability, the findings,
More informationThe role of recurrent CBT for increasing aviation security screeners visual knowledge and abilities needed in x-ray screening
The role of recurrent CBT for increasing aviation security screeners visual knowledge and abilities needed in x-ray screening Diana Hardmeier University of Zurich, Department of Psychology, Visual Cognition
More informationFramework for Comparative Research on Relational Information Displays
Framework for Comparative Research on Relational Information Displays Sung Park and Richard Catrambone 2 School of Psychology & Graphics, Visualization, and Usability Center (GVU) Georgia Institute of
More informationSEMINAR ON SERVICE MARKETING
SEMINAR ON SERVICE MARKETING Tracy Mary - Nancy LOGO John O. Summers Indiana University Guidelines for Conducting Research and Publishing in Marketing: From Conceptualization through the Review Process
More informationCHAPTER VI RESEARCH METHODOLOGY
CHAPTER VI RESEARCH METHODOLOGY 6.1 Research Design Research is an organized, systematic, data based, critical, objective, scientific inquiry or investigation into a specific problem, undertaken with the
More informationA PROCESS MODEL OF TRUST IN AUTOMATION: A SIGNAL DETECTION THEORY BASED APPROACH
A PROCESS MODEL OF TRUST IN AUTOMATION: A SIGNAL DETECTION THEORY BASED APPROACH Jorge Zuniga 1, Malcolm McCurry 2, J. Gregory Trafton 3 George Mason University 1 Fairfax, VA Exelis, Inc 2 Alexandria,
More informationItem Analysis Explanation
Item Analysis Explanation The item difficulty is the percentage of candidates who answered the question correctly. The recommended range for item difficulty set forth by CASTLE Worldwide, Inc., is between
More informationA NEW DIAGNOSIS SYSTEM BASED ON FUZZY REASONING TO DETECT MEAN AND/OR VARIANCE SHIFTS IN A PROCESS. Received August 2010; revised February 2011
International Journal of Innovative Computing, Information and Control ICIC International c 2011 ISSN 1349-4198 Volume 7, Number 12, December 2011 pp. 6935 6948 A NEW DIAGNOSIS SYSTEM BASED ON FUZZY REASONING
More informationObservational Category Learning as a Path to More Robust Generative Knowledge
Observational Category Learning as a Path to More Robust Generative Knowledge Kimery R. Levering (kleveri1@binghamton.edu) Kenneth J. Kurtz (kkurtz@binghamton.edu) Department of Psychology, Binghamton
More informationDesigning Experiments... Or how many times and ways can I screw that up?!?
www.geo.uzh.ch/microsite/icacogvis/ Designing Experiments... Or how many times and ways can I screw that up?!? Amy L. Griffin AutoCarto 2012, Columbus, OH Outline When do I need to run an experiment and
More informationControlled Experiments
CHARM Choosing Human-Computer Interaction (HCI) Appropriate Research Methods Controlled Experiments Liz Atwater Department of Psychology Human Factors/Applied Cognition George Mason University lizatwater@hotmail.com
More informationPOST GRADUATE DIPLOMA IN BIOETHICS (PGDBE) Term-End Examination June, 2016 MHS-014 : RESEARCH METHODOLOGY
No. of Printed Pages : 12 MHS-014 POST GRADUATE DIPLOMA IN BIOETHICS (PGDBE) Term-End Examination June, 2016 MHS-014 : RESEARCH METHODOLOGY Time : 2 hours Maximum Marks : 70 PART A Attempt all questions.
More informationGEX Recommended Procedure Eff. Date: 09/21/10 Rev.: D Pg. 1 of 7
GEX Recommended Procedure Eff. Date: 09/21/10 Rev.: D Pg. 1 of 7 NOTICE: This document is version controlled and was produced as a part of the GEX Information Program which requires that all Series 100
More informationTouch Behavior Analysis for Large Screen Smartphones
Proceedings of the Human Factors and Ergonomics Society 59th Annual Meeting - 2015 1433 Touch Behavior Analysis for Large Screen Smartphones Yu Zhang 1, Bo Ou 1, Qicheng Ding 1, Yiying Yang 2 1 Emerging
More informationEvaluation: Scientific Studies. Title Text
Evaluation: Scientific Studies Title Text 1 Evaluation Beyond Usability Tests 2 Usability Evaluation (last week) Expert tests / walkthroughs Usability Tests with users Main goal: formative identify usability
More informationEnhancement of Application Software for Examination of Differential Magnification Methods and Magnification Interface Factors
Enhancement of Application Software for Examination of Differential Magnification Methods and Magnification Interface Factors Fion C. H. Lee and Alan H. S. Chan Abstract The variable visual acuity nature
More informationSurvey Errors and Survey Costs
Survey Errors and Survey Costs ROBERT M. GROVES The University of Michigan WILEY- INTERSCIENCE A JOHN WILEY & SONS, INC., PUBLICATION CONTENTS 1. An Introduction To Survey Errors 1 1.1 Diverse Perspectives
More informationRapid communication Integrating working memory capacity and context-processing views of cognitive control
THE QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY 2011, 64 (6), 1048 1055 Rapid communication Integrating working memory capacity and context-processing views of cognitive control Thomas S. Redick and Randall
More informationLPU-Laguna Journal of Engineering and Computer Studies Vol. 3 No.1 September 2015
EFFECTS OF WORK ENVIRONMENT TO THE HEALTH AND PRODUCTIVITY OF THE WORKERS OF IM DIGITAL PHILIPPINES, INC. Bryan Carlo De Chavez 1, Elizer S. Malabanan 1, Joyce Anne R. Ramilo 1, HannaAngela Sarapat 1,
More informationSUPPLEMENTAL MATERIAL
1 SUPPLEMENTAL MATERIAL Response time and signal detection time distributions SM Fig. 1. Correct response time (thick solid green curve) and error response time densities (dashed red curve), averaged across
More informationISC- GRADE XI HUMANITIES ( ) PSYCHOLOGY. Chapter 2- Methods of Psychology
ISC- GRADE XI HUMANITIES (2018-19) PSYCHOLOGY Chapter 2- Methods of Psychology OUTLINE OF THE CHAPTER (i) Scientific Methods in Psychology -observation, case study, surveys, psychological tests, experimentation
More informationVaccination Setup. Immunization Set Up & Reporting
Vaccination Setup Immunization Set Up & Reporting To set up vaccination codes, waivers, links, combination vaccines, etc. The pathway is Office VaccinationsSetupCodes. See Cloning Vaccination Rules Code
More informationPushing the Right Buttons: Design Characteristics of Touch Screen Buttons
1 of 6 10/3/2009 9:40 PM October 2009, Vol. 11 Issue 2 Volume 11 Issue 2 Past Issues A-Z List Usability News is a free web newsletter that is produced by the Software Usability Research Laboratory (SURL)
More informationCHAPTER III RESEARCH METHODOLOGY
CHAPTER III RESEARCH METHODOLOGY Research methodology explains the activity of research that pursuit, how it progress, estimate process and represents the success. The methodological decision covers the
More informationCategorical Perception
Categorical Perception Discrimination for some speech contrasts is poor within phonetic categories and good between categories. Unusual, not found for most perceptual contrasts. Influenced by task, expectations,
More informationTechnical Specifications
Technical Specifications In order to provide summary information across a set of exercises, all tests must employ some form of scoring models. The most familiar of these scoring models is the one typically
More informationInfluence of Agent Type and Task Ambiguity on Conformity in Social Decision Making
Proceedings of the Human Factors and Ergonomics Society 2016 Annual Meeting 313 Influence of Agent Type and Task Ambiguity on Conformity in Social Decision Making Nicholas Hertz & Eva Wiese George Mason
More informationMammogram Analysis: Tumor Classification
Mammogram Analysis: Tumor Classification Literature Survey Report Geethapriya Raghavan geeragh@mail.utexas.edu EE 381K - Multidimensional Digital Signal Processing Spring 2005 Abstract Breast cancer is
More informationGROUP DECISION MAKING IN RISKY ENVIRONMENT ANALYSIS OF GENDER BIAS
GROUP DECISION MAKING IN RISKY ENVIRONMENT ANALYSIS OF GENDER BIAS Andrea Vasiľková, Matúš Kubák, Vladimír Gazda, Marek Gróf Abstract Article presents an experimental study of gender bias in group decisions.
More informationReliability of feedback fechanism based on root cause defect analysis - case study
Annales UMCS Informatica AI XI, 4 (2011) 21 32 DOI: 10.2478/v10065-011-0037-0 Reliability of feedback fechanism based on root cause defect analysis - case study Marek G. Stochel 1 1 Motorola Solutions
More informationThe Simon Effect as a Function of Temporal Overlap between Relevant and Irrelevant
University of North Florida UNF Digital Commons All Volumes (2001-2008) The Osprey Journal of Ideas and Inquiry 2008 The Simon Effect as a Function of Temporal Overlap between Relevant and Irrelevant Leslie
More informationAPPLICATION OF FUZZY SIGNAL DETECTION THEORY TO VIGILANCE: THE EFFECT OF CRITERION SHIFTS
i.-, 1678 APPLICATION OF FUZZY SIGNAL DETECTION THEORY TO VIGILANCE: THE EFFECT OF CRITERION SHIFTS Shawn C. Stafford, James L. Szalma, 2 Peter A. Hancock,,* & Mustapha Mouloua I Department of Psychology
More information[EN-A-022] Analysis of Positive and Negative Effects of Salience on the ATC Task Performance
ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2017) [EN-A-022] Analysis of Positive and Negative Effects of Salience on the ATC Task Performance + H. Yoshida*, H. Aoyama**, D. Karikawa***, S. Inoue**,
More informationResearch Review: Multiple Resource Theory. out in multi-task environments. Specifically, multiple display layout and control design
Research Review: Multiple Resource Theory Relevance to HCI Multiple resource theory is a framework for predicting effects on performance when multiple tasks are concurrently executed. These predictions
More informationCONNERS K-CPT 2. Conners Kiddie Continuous Performance Test 2 nd Edition C. Keith Conners, Ph.D.
CONNERS K-CPT 2 Conners Kiddie Continuous Performance Test 2 nd Edition C. Keith Conners, Ph.D. Assessment Report Name/ID: Jen Sample / 334 Age: 5 Gender: Female Birth Date: June 30, 2008 Grade: Administration
More informationApplication of ecological interface design to driver support systems
Application of ecological interface design to driver support systems J.D. Lee, J.D. Hoffman, H.A. Stoner, B.D. Seppelt, and M.D. Brown Department of Mechanical and Industrial Engineering, University of
More informationStatistical analysis DIANA SAPLACAN 2017 * SLIDES ADAPTED BASED ON LECTURE NOTES BY ALMA LEORA CULEN
Statistical analysis DIANA SAPLACAN 2017 * SLIDES ADAPTED BASED ON LECTURE NOTES BY ALMA LEORA CULEN Vs. 2 Background 3 There are different types of research methods to study behaviour: Descriptive: observations,
More informationEvaluating Tactile Feedback in Graphical User Interfaces
Evaluating Tactile Feedback in Graphical User Interfaces Elina Tähkäpää and Roope Raisamo Tampere Unit for Computer-Human Interaction (TAUCHI) Department of Computer and Information Sciences FIN- University
More informationControlling the risk due to the use of gamma sources for NDT First feedback from the deployment of replacement NDT Techniques
Controlling the risk due to the use of gamma sources for NDT First feedback from the deployment of replacement NDT Techniques Etienne MARTIN (COFREND, France) 1 Content The context Status of the deployment
More informationDesigning A User Study
EECS4443 Mobile User Interfaces Designing A User Study Scott MacKenzie York University EECS4443 Mobile User Interfaces Designing A User Study 1 Scott MacKenzie York University 1 Executive summary The Complete
More information4. Model evaluation & selection
Foundations of Machine Learning CentraleSupélec Fall 2017 4. Model evaluation & selection Chloé-Agathe Azencot Centre for Computational Biology, Mines ParisTech chloe-agathe.azencott@mines-paristech.fr
More informationUsing threat image projection data for assessing individual screener performance
Safety and Security Engineering 417 Using threat image projection data for assessing individual screener performance F. Hofer & A. Schwaninger Department of Psychology, University of Zurich, Switzerland
More informationA hybrid approach for identification of root causes and reliability improvement of a die bonding process a case study
Reliability Engineering and System Safety 64 (1999) 43 48 A hybrid approach for identification of root causes and reliability improvement of a die bonding process a case study Han-Xiong Li a, *, Ming J.
More informationPupil Dilation as an Indicator of Cognitive Workload in Human-Computer Interaction
Pupil Dilation as an Indicator of Cognitive Workload in Human-Computer Interaction Marc Pomplun and Sindhura Sunkara Department of Computer Science, University of Massachusetts at Boston 100 Morrissey
More informationVisual Inspection Reliability for Precision Manufactured Parts
602389HFSXXX10.1177/0018720815602389Human FactorsVisual Inspection Visual Inspection Reliability for Precision Manufactured Parts Judi E. See, Sandia National Laboratories, Albuquerque, New Mexico Objective:
More informationSound Check: Essentials of a Hearing Screener
Sound Check: Passing the Test? Amanda Wolfe, Au.D., Elizabeth Galster, Au.D., & Beth Thomas, M.S. The importance of early identification and prevention of hearing loss for people of all ages is well established.
More informationEvaluation: Controlled Experiments. Title Text
Evaluation: Controlled Experiments Title Text 1 Outline Evaluation beyond usability tests Controlled Experiments Other Evaluation Methods 2 Evaluation Beyond Usability Tests 3 Usability Evaluation (last
More informationSawtooth Software. The Number of Levels Effect in Conjoint: Where Does It Come From and Can It Be Eliminated? RESEARCH PAPER SERIES
Sawtooth Software RESEARCH PAPER SERIES The Number of Levels Effect in Conjoint: Where Does It Come From and Can It Be Eliminated? Dick Wittink, Yale University Joel Huber, Duke University Peter Zandan,
More informationSupplementary experiment: neutral faces. This supplementary experiment had originally served as a pilot test of whether participants
Supplementary experiment: neutral faces This supplementary experiment had originally served as a pilot test of whether participants would automatically shift their attention towards to objects the seen
More informationAuditory Dominance: Overshadowing or Response Competition?
Auditory Dominance: Overshadowing or Response Competition? Christopher W. Robinson (robinson.777@osu.edu) Center for Cognitive Science The Ohio State University 208F Ohio Stadium East, 1961 Tuttle Park
More informationIntelligent Object Group Selection
Intelligent Object Group Selection Hoda Dehmeshki Department of Computer Science and Engineering, York University, 47 Keele Street Toronto, Ontario, M3J 1P3 Canada hoda@cs.yorku.ca Wolfgang Stuerzlinger,
More informationCultural Differences in Cognitive Processing Style: Evidence from Eye Movements During Scene Processing
Cultural Differences in Cognitive Processing Style: Evidence from Eye Movements During Scene Processing Zihui Lu (zihui.lu@utoronto.ca) Meredyth Daneman (daneman@psych.utoronto.ca) Eyal M. Reingold (reingold@psych.utoronto.ca)
More informationOlder adults associative deficit in episodic memory: Assessing the role of decline in attentional resources
Psychonomic Bulletin & Review 2004, 11 (6), 1067-1073 Older adults associative deficit in episodic memory: Assessing the role of decline in attentional resources MOSHE NAVEH-BENJAMIN University of Missouri,
More informationNational Culture Dimensions and Consumer Digital Piracy: A European Perspective
National Culture Dimensions and Consumer Digital Piracy: A European Perspective Abstract Irena Vida, irena.vida@ef.uni-lj.si Monika Kukar-Kinney, mkukarki@richmond.edu Mateja Kos Koklič, mateja.kos@ef.uni-lj.si
More informationSparse Coding in Sparse Winner Networks
Sparse Coding in Sparse Winner Networks Janusz A. Starzyk 1, Yinyin Liu 1, David Vogel 2 1 School of Electrical Engineering & Computer Science Ohio University, Athens, OH 45701 {starzyk, yliu}@bobcat.ent.ohiou.edu
More informationGlossary From Running Randomized Evaluations: A Practical Guide, by Rachel Glennerster and Kudzai Takavarasha
Glossary From Running Randomized Evaluations: A Practical Guide, by Rachel Glennerster and Kudzai Takavarasha attrition: When data are missing because we are unable to measure the outcomes of some of the
More informationControlling Stable and Unstable Dynamic Decision Making Environments
Controlling Stable and Unstable Dynamic Decision Making Environments Magda Osman (m.osman@qmul.ac.uk) Centre for Experimental and Biological Psychology, Queen Mary University of London, London, E1 4NS
More informationQuick Notes for Users of. Beef Ration and Nutrition. Decision Software & Sheep Companion Modules
Quick Notes for Users of Beef Ration and Nutrition Decision Software & Sheep Companion Modules Standard & Professional Editions 2 Table of Contents Computer Setup 3 Feeds and Feeding Concepts 16 3 System
More informationChapter 3: Information Processing
SENG 5334: Human Factors Engineering & INDH 5931: Research Topics in IH/Safety Chapter 3: Information Processing By: Magdy Akladios, PhD, PE, CSP, CPE, CSHM 1 A Model of Information Processing Def: A model
More informationMODELLING CHARACTER LEGIBILITY
Watson, A. B. & Fitzhugh, A. E. (989). Modelling character legibility. Society for Information Display Digest of Technical Papers 2, 36-363. MODELLING CHARACTER LEGIBILITY Andrew B. Watson NASA Ames Research
More informationASSIGNMENT 2. Question 4.1 In each of the following situations, describe a sample space S for the random phenomenon.
ASSIGNMENT 2 MGCR 271 SUMMER 2009 - DUE THURSDAY, MAY 21, 2009 AT 18:05 IN CLASS Question 4.1 In each of the following situations, describe a sample space S for the random phenomenon. (1) A new business
More informationDräger Alcotest Handheld Product Suite
Dräger Alcotest Handheld Product Suite D-7404-2014 Roadside alcohol testing can be challenging and unpredictable. That s why Dräger designed Alcotest handheld breath testers to be accurate and easy to
More informationClever Hans the horse could do simple math and spell out the answers to simple questions. He wasn t always correct, but he was most of the time.
Clever Hans the horse could do simple math and spell out the answers to simple questions. He wasn t always correct, but he was most of the time. While a team of scientists, veterinarians, zoologists and
More informationProblem Solving Approach of Technology Students
Problem Solving Approach of Technology Students Sophia Scott, Ph.D. sscott@semo.edu Industrial & Engineering Technology Dept. Southeast Missouri State University Doug Koch, Ph.D. dskoch@semo.edu Industrial
More informationSpreadsheet signal detection
Behavior Research Methods, Instruments, & Computers 1999, 31 (1), 46-54 Spreadsheet signal detection ROBERT D. SORKIN University of Florida, Gainesville, Florida A number of studies in perception, attention,
More informationMethods for Determining Random Sample Size
Methods for Determining Random Sample Size This document discusses how to determine your random sample size based on the overall purpose of your research project. Methods for determining the random sample
More informationToday: Statistical inference.
Model Based Statistics in Biology. Part II. Quantifying Uncertainty. Chapter 7 Statistical Inference ReCap. Part I (Chapters 1,2,3,4) ReCap Part II (Ch 5, 6) 7.0 Inferential statistics Probability and
More informationAttention to health cues on product packages
Attention to health cues on product packages 1 J Orquin & J Scholderer Institute for Marketing and Statistics, Aarhus School of Business, Aarhus University 1 jalo@asb.dk ABSTRACT The objectives of the
More informationTypes of questions. You need to know. Short question. Short question. Measurement Scale: Ordinal Scale
You need to know Materials in the slides Materials in the 5 coglab presented in class Textbooks chapters Information/explanation given in class you can have all these documents with you + your notes during
More informationSHOEBOX Audiometry Pro. Quickstart Guide. SHOEBOX Audiometry Pro
Quickstart Guide 1 Introduction Welcome to your SHOEBOX Audiometry system! This quickstart guide will help you get SHOEBOX up and running, so you can quickly and easily conduct your first tests. Once you
More informationMammogram Analysis: Tumor Classification
Mammogram Analysis: Tumor Classification Term Project Report Geethapriya Raghavan geeragh@mail.utexas.edu EE 381K - Multidimensional Digital Signal Processing Spring 2005 Abstract Breast cancer is the
More informationFactors Affecting Speed and Accuracy of Response Selection in Operational Environments
Factors Affecting Speed and Accuracy of Response Selection in Operational Environments Robert W. Proctor & Motonori Yamaguchi Purdue University, W. Lafayette, Indiana MURI Grant W9112NF-05-1-0153 from
More informationResearch Questions and Survey Development
Research Questions and Survey Development R. Eric Heidel, PhD Associate Professor of Biostatistics Department of Surgery University of Tennessee Graduate School of Medicine Research Questions 1 Research
More informationEXAMINING THE RELATIONSHIP BETWEEN ORGANIZATIONAL JUSTICE AND EFFECTIVENESS OF STRATEGY IMPLEMENTATION AT FOOD INDUSTRIES IN ARDABIL PROVINCE
EXAMINING THE RELATIONSHIP BETWEEN ORGANIZATIONAL JUSTICE AND EFFECTIVENESS OF STRATEGY IMPLEMENTATION AT FOOD INDUSTRIES IN ARDABIL PROVINCE Dr.MirzaHassan Hosseini Associate Professor, Payam e Noor University,
More informationEffect of the number of non-conforming samples on the Kappa indicator values
Effect of the number of non-conforming samples on the Kappa indicator values Pavel Klaput 1 and David Vykydal 2 1 VSB-TU Ostrava, Department of quality management, 17. listopadu 15/2172, Ostrava, Czech
More informationData Management, Data Management PLUS User Guide
Data Management, Data Management PLUS User Guide Table of Contents Introduction 3 SHOEBOX Data Management and Data Management PLUS (DM+) for Individual Users 4 Portal Login 4 Working With Your Data 5 Manually
More informationThe Stroop Effect The Effect of Interfering Colour Stimuli Upon Reading Names of Colours Serially ABSTRACT
The Stroop Effect The Effect of Interfering Colour Stimuli Upon Reading Names of Colours Serially ABSTRACT This experiment, a partial duplication of the work of Stroop (l935) l, aimed to demonstrate the
More informationExamining differences between two sets of scores
6 Examining differences between two sets of scores In this chapter you will learn about tests which tell us if there is a statistically significant difference between two sets of scores. In so doing you
More informationUsing threat image projection data for assessing individual screener performance
Safety and Security Engineering 417 Using threat image projection data for assessing individual screener performance F. Hofer & A. Schwaninger Department of Psychology, University of Zurich, Switzerland
More informationCh. 11 Measurement. Measurement
TECH 646 Analysis of Research in Industry and Technology PART III The Sources and Collection of data: Measurement, Measurement Scales, Questionnaires & Instruments, Sampling Ch. 11 Measurement Lecture
More informationA Race Model of Perceptual Forced Choice Reaction Time
A Race Model of Perceptual Forced Choice Reaction Time David E. Huber (dhuber@psyc.umd.edu) Department of Psychology, 1147 Biology/Psychology Building College Park, MD 2742 USA Denis Cousineau (Denis.Cousineau@UMontreal.CA)
More informationA study of association between demographic factor income and emotional intelligence
EUROPEAN ACADEMIC RESEARCH Vol. V, Issue 1/ April 2017 ISSN 2286-4822 www.euacademic.org Impact Factor: 3.4546 (UIF) DRJI Value: 5.9 (B+) A study of association between demographic factor income and emotional
More information