Szalma & Hancock HFES Europe, 20061 Fuzzy Signal Detection Theory and Human Performance: A Review of Empirical Evidence for Model Validity J.L. Szalma.

Slides:

Advertisements

Similar presentations

How do we know when we know. Outline  What is Research  Measurement  Method Types  Statistical Reasoning  Issues in Human Factors.

Advertisements

Signal Detection Theory. The classical psychophysicists believed in fixed thresholds Ideally, one would obtain a step-like change from no detection to.

10 / 31 Outline Perception workshop groups Signal detection theory Scheduling meetings.

From the homework: Distribution of DNA fragments generated by Micrococcal nuclease digestion mean(nucs) = bp median(nucs) = 110 bp sd(nucs+ = 17.3.

Comparison of Spatial and Temporal Discrimination Performance across Various Difficulty Levels J.E. THROPP, J.L. SZALMA, & P.A. HANCOCK Department of Psychology.

A Meta-Analysis of Periodic Noise Stress on Human Performance J.M. Ross, G.E. Conway, J.L. Szalma, B.M. Saxton, A. Braczyk, & P.A. Hancock University of.

Decision making as a model 4. Signal detection: models and measures.

PSYCHOPHYSICS What is Psychophysics? Classical Psychophysics Thresholds Signal Detection Theory Psychophysical Laws.

Report on Intrusion Detection and Data Fusion By Ganesh Godavari.

Statistics II: An Overview of Statistics. Outline for Statistics II Lecture: SPSS Syntax – Some examples. Normal Distribution Curve. Sampling Distribution.

MARE 250 Dr. Jason Turner Hypothesis Testing II. To ASSUME is to make an… Four assumptions for t-test hypothesis testing:

Introduction to Biomedical Statistics. Signal Detection Theory What do we actually “detect” when we say we’ve detected something?

Decision making as a model 5.a. more models and measures b. costs and benefits c. the optimal criterion (Bayes is back)

Relationships Among Variables

Short Notes on Theory of Signal Detection Walter Schneider

Psychophysics 3 Research Methods Fall 2010 Tamás Bőhm.

This material is based upon work supported by the U.S. Department of Homeland Security, Science and Technology Directorate, Office of University Programs,

Report on Intrusion Detection and Data Fusion By Ganesh Godavari.

The Method of Constant Stimuli & Signal Detection Theory VISN2211 Sieu Khuu David Lewis.

Inference and Inferential Statistics Methods of Educational Research EDU 660.

Learning Theory Reza Shadmehr Linear and quadratic decision boundaries Kernel estimates of density Missing data.

49 th Annual HFES Meeting, Orlando FL Application of Theories of Motivation and Emotion to Hedonomics J.L. Szalma University of Central Florida.

Signal detection theory Appendix Takashi Yamauchi Texas A&M University.

Research Design & Analysis 2: Class 23 Announcement re. Extra class: April 10th BAC 237 Discrete Trials Designs: Psychophysics & Signal Detection.

Signal Detection Theory I. Challenges in Measuring Perception II. Introduction to Signal Detection Theory III. Applications of Signal Detection Theory.

20-24 September 2004Szalma, Oron-Gilad, & Hancock – HFES Annual Meeting1 Examination of Attentional Mechanisms Underlying Stress and Performance J.L. Szalma,

The Influence of Dispositional Optimism and Pessimism on Task Engagement for Spatial and Temporal Discrimination J.L. Szalma, J.M. Ross, & P.A. Hancock.

Analyzing Statistical Inferences How to Not Know Null.

Bayesian Decision Theory (Classification) 主講人：虞台文.

Individual differences play a dominant role in determining how well a job can be performed, accounting for more variability in performance than differences.

BCS547 Neural Decoding.

MIT2 Lab, UCF 1 The use of Meta-analysis to update the whole-body vibration stressor effects within IMPRINT Gareth Conway James Szalma MIT 2 Lab, University.

Sensation Perception = gathering information from the environment 2 stages: –Sensation = simple sensory experiences and translating physical energy from.

Chapter 2: Signal Detection and Absolute Judgement

Test Security Considerations in the Dynamic Learning Maps System: Consortium Perspective Meagan Karvonen The present publication was developed under grant.

ISE Recall the HIP model. ISE Beyond sensing & perceiving …  You are sitting at lunch and hear a familiar ring tone. Is that your.

Performance Operating Characteristics for Spatial and Temporal Discriminations: Common or Separate Capacities? J. E. Thropp, J. L. Szalma, and P. A. Hancock.

Comparison of Fuzzy and Signal Detection Theory L.L. Murphy, J.L. Szalma, and P.A. Hancock Department of Psychology Institute of Simulation and Training.

USER INTERFACE USER INTERFACE January 12, 2006 Intern 박지현 Performance analysis of filtering software using Signal Detection Theory Ashutosh Deshmukh, Balaji.

Outline of Lecture I.Intro to Signal Detection Theory (words) II.Intro to Signal Detection Theory (pictures) III.Applications of Signal Detection Theory.

GRAPPLING WITH DATA Variability in observations Sources of variability measurement error and reliability Visualizing the sample data Frequency distributions.

Psy Psychology of Hearing Psychophysics and Detection Theory Neal Viemeister

THE SCIENTIFIC METHOD: It’s the method you use to study a question scientifically.

SIGNAL DETECTION THEORY  A situation is described in terms of two states of the world: a signal is present ("Signal") a signal is absent ("Noise")  You.

Signal Detection Theory October 5, 2011 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion.

Copyright © 2012 Assessment and Accountability Comprehensive Center & North Central Comprehensive Center at McRel.

How far away is the moon? ( An adventure in signal detection theory and allusive science) Harold E. Brooks NOAA/National Severe Storms Laboratory Norman,

James L. Szalma Department of Psychology and Institute for Simulation and Training University of Central Florida Analysis of Individual Differences Data:

OTHER APPROACHES TO TWO- PROCESS MODELS Remembering, Knowing, and Autonoetic Consciousness –Tulving (1983): Episodic memory based on a self-aware consciousness.

Comparison of discriminant vs. logistic regression analyses for predicting consumer acceptance and purchase decision Darryl Holliday, Ashley Bond, Witoon.

© 2009 Pearson Prentice Hall, Salkind. Chapter 5 Measurement, Reliability and Validity.

King Saud University College of Engineering IE – 341: “Human Factors” Spring – 2016 (2 nd Sem H) Chapter 3. Information Input and Processing Part.

Logistic Regression: Regression with a Binary Dependent Variable.

Fuzzy Signal Detection Theory: ROC Analysis of Stimulus and Response Range Effects J.L. Szalma and P.A. Hancock Department of Psychology and Institute.

P. Janik, Z. Leonowicz, T. Lobos, Z. Waclawek

Figure 1.16 Detecting a stimulus using the signal detection theory (SDT) approach (Part 1) wolfe2e-fig jpg.

Origins of Signal Detection Theory

A Normalized Poisson Model for Recognition Memory

Jessie Bullens prof. dr. Albert Postma

Swets et al (1961).

Information Units of Measurement

Signal Detection Theory

Chapter 9 Hypothesis Testing.

How do we make decisions about uncertain events?

Statistics II: An Overview of Statistics

Hypothesis Testing: The Difference Between Two Population Means

Satish Pradhan Dnyanasadhana College, Thane. Department of Chemistry S

Signal detection theory

Machine Learning with Clinical Data

Chapter 6 Logistic Regression: Regression with a Binary Dependent Variable Copyright © 2010 Pearson Education, Inc., publishing as Prentice-Hall.

Presentation transcript:

Szalma & Hancock HFES Europe, Fuzzy Signal Detection Theory and Human Performance: A Review of Empirical Evidence for Model Validity J.L. Szalma and P.A. Hancock University of Central Florida

Szalma & Hancock HFES Europe, How Do We Assess Performance? Signal Detection Theory Hit (H)Miss (M) False Alarm (FA) Correct Rejection (CR) Response YesNo State of the World Signal Noise

Szalma & Hancock HFES Europe, Multiple Levels of Threat in Law Enforcement Levels of Threat MinimalLowModerateHighSevere PresenceVerbalControllingIncapacitatingDeadly Levels of Response

Szalma & Hancock HFES Europe, Fuzzy Signal Detection Theory Traditional Set Theory: A ∩ Ā = 0 Fuzzy Set Theory: A ∩ Ā ≠ 0 Hancock, Masalonis, & Parasuraman (2000) Parasuraman, Masalonis, & Hancock (2000)

Szalma & Hancock HFES Europe, Fuzzy Signal Detection Theory Events can belong to the set “signal” (s) to a degree ranging from 0 to 1 Events can belong to the set “response” (r) to a degree ranging from 0 to 1 Degrees of membership possible

Szalma & Hancock HFES Europe, Mapping Functions

Szalma & Hancock HFES Europe, Assumptions of SDT  Noise and Signal+Noise distributions are normally distributed Linear ROC  Variances of the two distributions are equal Unit slope ZHZH Z FA

Szalma & Hancock HFES Europe, Comparison of Fuzzy and Crisp ROC Curves CrispFuzzy

Szalma & Hancock HFES Europe, Comparison of Fuzzy and Crisp ROC Curves Crisp Fuzzy

Szalma & Hancock HFES Europe, For a task requiring temporal discrimination:  Sensitivity is higher when data analyzed with FSDT compared to traditional analysis  Fuzzy response sets yield more accurate performance assessment than binary response sets (ratings match stimulus level more closely) What do we know?

Szalma & Hancock HFES Europe, Application of FSDT to Tank Identification

Szalma & Hancock HFES Europe, Results: Fuzzy SDT Binary Response Set Data of all participants met the Gaussian and equal variance assumptions 5 Category Response Set.  The equal variance Gaussian model fit for 3 observers.  The unequal variance model fit for one observer.  Neither model fit for two observers

Szalma & Hancock HFES Europe, Results: Fuzzy SDT 100 Category Response Set.  Equal variance Gaussian model fit for 2 observers  Unequal variance model fit the data for one observer  Neither model fit for 3 observers

Szalma & Hancock HFES Europe, Comparison of Crisp and Fuzzy ROC Functions Crisp Fuzzy

Szalma & Hancock HFES Europe, Comparison of Crisp and Fuzzy ROC Functions Crisp Fuzzy

Szalma & Hancock HFES Europe, Overall Conclusions Gaussian assumption met Equal variance assumption –May be met –Depends on effect of instruction set What is a ‘fuzzy response criterion’?

Szalma & Hancock HFES Europe, Manipulate instruction set Manipulate stimulus distribution (‘signal rate’) Establish mapping functions using multiple methodologies Question: What is the structure of the FSDT decision space? Future Directions

Szalma & Hancock HFES Europe, ACKNOWLEDGEMENT This work was supported in part by the Department of Defense Multidisciplinary University Research Initiative (MURI) program administered by the Army Research Office under Grant DAAD P.A. Hancock, Principal Investigator. The views expressed in this work are those of the authors and do not necessarily reflect official Army policy. The authors wish to thank Dr. Sherry Tove, Dr. Elmar Schmeisser, and Dr. Mike Drillings for providing administrative and technical direction for the Grant.