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Francis2003b

Course: PSY 2003, Fall 2008
School: Purdue
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simulations Online of models for backward masking Gregory Francis1 Purdue University Department of Psychological Sciences 703 Third Street West Lafayette, IN 47907-2004 11 July 2002 Revised: 30 January 2003 Final revision: 03 April 2003 Key words: masking, metacontrast, models, simulation, vision Running head: Online simulations for masking 1 E-mail: gfrancis@psych.purdue.edu; phone: 765-494-6934. This material...

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simulations Online of models for backward masking Gregory Francis1 Purdue University Department of Psychological Sciences 703 Third Street West Lafayette, IN 47907-2004 11 July 2002 Revised: 30 January 2003 Final revision: 03 April 2003 Key words: masking, metacontrast, models, simulation, vision Running head: Online simulations for masking 1 E-mail: gfrancis@psych.purdue.edu; phone: 765-494-6934. This material is based upon work sup- ported by the National Science Foundation under Grant No. 0108905. Online simulations of masking Abstract 2 Five simulations of quantitative models of visual backward masking are available on the Internet at http://www.psych.purdue.edu/gfrancis/Publications/BackwardMasking/. The simulations can be run in a web browser that supports the Java programming language. This paper describes the motivation for making the simulations available and gives a brief introduction to how the simulations are used. The source code is available on the web page, and the paper describes how the code is organized. Online simulations of masking 3 Introduction Backward masking occurs when a briey presented visual target stimulus becomes dicult to see because of the appearance of a mask stimulus that follows the target. Backward masking has been investigated in thousands of studies with a variety of experimental manipulations g (see Breitmeyer & Omen (2000) and Enns & Di Lollo (2000) for recent reviews). There are three reasons that interest in the properties of masking has been strong for decades. First, vision scientists use masking to explore the interaction of the target and mask signals and identify key properties of the mechanisms involved in visual perception. Second, cognitive psychologists use backward masking as a means of interrupting the processing of target information. It is known that processing of a target does not stop with the physical disappearance of the target stimulus, but that processing can continue for at least a second after the stimulus has turned o (Sperling, 1960). The presentation of a strong mask seems to halt further processing of the target stimulus as soon as the mask appears. Thus, by varying the timing between the oset of the target and the onset of the mask, the duration of processing can be controlled and the details of cognitive mechanisms analyzed. Third, the properties of masking have been used to investigate aspects of various types of mental diseases (e.g., Bra & Saccuzzo, 1981; Green, Nuechterlein & Mintz, 1994; Slaghuis & Curran, 1999). Patients sometimes respond quite dierently than normals under masking conditions. Given the strong interest in masking and the frequency of its use as a tool for investigating perceptual, cognitive, and beha...
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Purdue - PSY - 2003
Cognitive PsychologyCognitive Psychology 46 (2003) 198226 www.elsevier.com/locate/cogpsychDeveloping a new quantitative account of backward maskingGregory Francis*Department of Psychological Sciences, Purdue University, 1364 Psychological Scienc
Purdue - PSY - 2002
590Journal of Experimental Psychology: General2002, Vol. 131, No. 4, pp. 590593.COMMENTS Comment on Competition for consciousness among visual events: The psychophysics of reentrant visual processes (Di Lollo, Enns and Rensink, 2000)Gregory Fr
Purdue - PSY - 2001
Purdue - PSY - 2000
768Psychological Review2000, Vol. 107, No. 4, pp. 768685.Quantitative Theories of Metacontrast MaskingGregory Francis Purdue University, Department of Psychological SciencesIn metacontrast masking, the eect of a visual mask stimulus on the pe
Purdue - PSY - 2000
Spatial Vision, Vol. 13, No. 1, pp. 6786 (2000). c VSP 2000. This formatted manuscript was created by the second author and does not correspond exactly to the manuscript published in the journal. References to page numbers should refer to the publish
Purdue - PSY - 1999
Perception, 1999, volume 28, pages 12431255 This formatted manuscript was created by the rst author and does not correspond exactly to the manuscript published in the journal. References to page numbers should refer to the published article.Motion
Purdue - PSY - 1998
Perception, 1998, volume 27, pages 785797 This formatted manuscript was created by the second author and does not correspond exactly to the manuscript published in the journal. References to page numbers should refer to the published article.A comp
Purdue - PSY - 2002
3B2v7:51c GML4:3:1IJHC : 20020527Prod:Type: com pp:1224col:fig:Fig 2 colour for onlineED:Ravi=Br PAGN: thilakam SCAN: shobhaInt. J. Human-Computer Studies (2002) 56, 000000 doi:10.1006/ijhc.2002.0527 Available online at http:/www.idealibrary.
Purdue - PSY - 2000
INTERNATIONAL JOURNAL OF COGNITIVE ERGONOMICS, 2000, 4(2), 107124 Copyright 2000, Lawrence Erlbaum Associates, Inc.Designing Multifunction Displays: An Optimization ApproachGregory Francis Department of Psychological Sciences Purdue UniversityAB
Purdue - PSY - 2002
Developing a new quantitative account of backward maskingGregory Francis1 Purdue University Department of Psychological Sciences 1364 Psychological Sciences Building West Lafayette, IN 47907-1364 1 November 2001 Revised: June 4, 2002 Accepted: 10 J
LSU - EE - 7700
Value Locality and Load Value PredictionMikko H. Lipasti, Christopher B. Wilkerson1, and John Paul Shen Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA, 15213 {mhl,shen}@ece.cmu.eduAbstractSince the intr
LSU - EE - 7700
Inside the 4 Processor Pentium Micro-architectureNext Generation IA-32 Micro-architectureFall 2000Doug Carmean Principal Architect Intel Architecture GroupAugust 24, 2000IntelCopyright 2000 Intel Corporation.LabsAgendal IA-32Fall 200
LSU - EE - 7700
AMDs Next Generation Microprocessor ArchitectureFred WeberOctober 2001"Hammer" Goals Build a next-generation system architecture which serves as the foundation for future processor platforms Enable a full line of server and workstation products
LSU - EE - 7700
April 21, 20041The Itanium Architecture A Technical OverviewThomas Siebold Technical Consultant Transition Engineering & Consulting Business Critical Server Division thomas.siebold@hp.com Rev. 6.5 2004 Hewlett-Packard Development Company, L.P.
LSU - EE - 4720
11-1This Set11-1These slides do not give detailed coverage of the material. See class notes and solved problems (last page) for more information. Text covers multiple-issue machines in Chapter 4, but does not cover most of the topics presented
LSU - STAT - 7034
EXST7034 Regression Techniques Multiple Regression 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Fall 2004 SAS examplesGeaghan Page 1*; * EXST7034 Multiple Regression Example *; * Problem from Neter, Kutner, Nachtsheim & Wasserman 1996, #6.18 *; *; OPTIO
LSU - STAT - 7034
EXST7034 Regression Techniques Multiple RegressionFall 2004 SAS examplesGeaghan Page 11 *; 2 * EXST7034 Multiple Regression Example *; 3 * Problem from Neter, Kutner, Nachtsheim & Wasserman 1996, #6.18 *; 4 *; 5 6 OPTIONS LS=99 PS=80 NOCENTER
LSU - STAT - 7034
EXST7034 Regression Techniques Multiple RegressionFall 2004 SAS examplesGeaghan Page 11 *; 2 * EXST7034 Multiple Regression Example *; 3 * Problem from Neter, Kutner, Nachtsheim & Wasserman 1996, #6.18 *; 4 *; 5 6 OPTIONS LS=99 PS=80 NOCENTER
LSU - STAT - 7034
EXST7034 Chapter 11Multiple Regression Bootstrapping (Toluca example)Geaghan Page 1Toluca Company Example (Problem from Neter, Kutner, Nachtsheim & Wasserman 1996,1.21) A particular part needed for refigeration equipment replacement parts are p
LSU - STAT - 7034
EXST7035 : Regression Techniques Analysis of Covariance & PiecewiseFall 2004 SAS exampleGeaghan Page 11 *; 2 * EXST7034 Homework Example 1 *; 3 * Problem from Neter, Wasserman & Kuttner 1989, #11.16 *; 4 *; 5 OPTIONS LS=82 PS=61 NOCENTER NODATE
LSU - STAT - 7034
EXST7034 Chapter 12Time series Microcomputer exampleGeaghan Page 11 *; 2 * EXST7034 Homework Example *; 3 * Problem from Neter, Wasserman & Kuttner 1989, 13.92 *; 4 **; 5 6 dm'log;clear;output;clear'; 7 options nodate nocenter nonumber ps=512 l
LSU - STAT - 7034
EXST7015 : Statistical Techniques II Random coefficients regressionWeight Lifting exampleGeaghan Page 11 /*-2 SAS System for Mixed Models (1996) 3 by Ramon C. Littell, Ph.D., George A. Milliken, Ph.D., 4 Walter W. Stroup, Ph.D., and Russell D.
LSU - STAT - 7034
EXST7034 - Regression Techniques Simple Linear Regression Data: Neter, Wasserman & Kuttner (1989), Page 57, Problem 2.19. _ X Y Y 0 8, 9, 11, 12 10 1 13, 15, 16 14.67 2 17, 19 18 3 22 22 Intermediate Calculations _ DX3 = 10; DX2 = 20; D(X3 -X)2 = 10;
LSU - STAT - 7034
EXST7034 - Regression Techniques POWER 1 " in testing regression coefficients.Page 1If we reject a null hypothesis, we need not concern ourselves with power. If we reject H! , we have a (1-!)100% chance of having made an error (called TYPE I er
LSU - STAT - 7034
LSU - STAT - 7034
EXST7034 - Regression Techniques EXAMPLE: Using SAS to test hypotheses about "! and " EXST7034 - EXAMPLE 1Page 1Program Statements *; * EXST7034 Example 1 using PC-SAS *; * Problem from Neter, Wasserman & Kuttner 1989, 2.19 *; *; OPTIONS LS=80 PS
LSU - STAT - 7034
EXST7034 - Regression Techniques Using F tests instead of t-testsPage 1We can also test the hypothesis H! :" 0 versus H" :" 0 with an F test. FMSRegression MSErrorThis test is mathematically identical to the previous test of H! :" =0 done wi
LSU - STAT - 7034
EXST7034 - Regression TechniquesPage 1Prediction of a new observation : note that this is a single observation, not the regression line. First, the variance of a generic linear combination (from Chapter 1:1.27a & b) T aW bX cZ E(T) aE(W) bE(
LSU - STAT - 7034
EXST7034 - Regression Techniques General Linear Hypothesis Test Approach (GLHT) Given a Regression Model with all variables of interest Y3 = b! b" X3 e3 we will call this the FULL model = Y3 b" (X3 - X) e3Page 1Given a second Regression Mod
LSU - STAT - 7034
EXST7034 - Regression Techniques Regression diagnostics dependent variable Y3Page 1There are a number of graphic representations which will help with problem detection and which can be used to obtain a better understanding of the dataset availab
LSU - STAT - 7034
EXST7034 - Regression Techniques Lack of Fit and Pure Error Assumed Model: Yij = "! + " Xj + %ij %ji 's NID(0,5 2 ) Question: Are we sure that E(Yij ) = "! + " XjPage 1Answer: We will NEVER be absolutely sure, we should try to check it. Procedur
LSU - STAT - 7034
EXST7034 - Regression Techniques SIMPLE LINEAR REGRESSION WITH MATRIX ALGEBRA MODEL: Y3 = "! + " X3 + %3 MATRIX MODEL: Y = XB + E Y" 1 Y 1 or # = Y8 1 X" e" X# b! e # b" e8 X8 Page 1Where, Y is the vector of dependent vari
LSU - STAT - 7034
Coefficient of Partial Determination As the R2 provides information about the SSR(X" ,X# ,X$ ), there are also Coefficients of PARTIAL Determination : this measures how much variation a variable accounts for out of the variation available to that var
LSU - STAT - 7034
POLYNOMIAL REGRESSION (Chapter 9) We have discussed curvilinear regression from transformations and polynomials 1) Transformations generally more interpretable, often more easily interpreted in terms of a possible functional relationship. (extrapolat
LSU - STAT - 7034
Building a Regression Model. 1) Think about the regression in advance. a) What variables are needed? Is it available? Is it readily measurable? Are there redundancies? b) How many observations are needed? More variables to be examined requires more c
LSU - STAT - 7034
Multicolinearity Diagnostics : Some of the diagnostics we have just discussed are sensitive to multicolinearity. For example, we know that with multicolinearity, additions and deletions of data cause shifts in the regression coefficients, this will b
LSU - STAT - 7034
Qualitative indicator variables An indicator variable is a distinguishing between qualitative categories. The easiest way creating an indicator variable is to 1) choose the category to be singled out 2) In a separate column of the X matrix, put a 1 w
LSU - STAT - 7034
Autocorrelation - particularly in data taken over time We assume errors are independent. What if they are not, such that the residual at time t is correlated to the previous residual at time t-1. This may cause runs of + or in the residuals. This
LSU - STAT - 7034
REGRESSION ON AN INDICATOR VARIABLE In this technique, the dependent variable (Y) is an indicator, and takes a value of either 0 or 1. This is called a binary response variable1 Response 0 Independent Variable(s)Examples any two categories, any
LSU - STAT - 7034
EXST7034 : Regression Techniques Logistic regressionGeaghan Page 1Simple linear regression on an indicator variable a precursor to logistic regression Basically it is a simple linear regression where the dependent variable has a value of either
LSU - STAT - 7034
Statistical Techniques IIEXST7015Logistic Regression13a_LogisticReg 1Regression on an indicator variableWhat is an indicator variable? It is a variable with either the value 0 or 1.When we get to ANOVA we will see that class variables (categ
LSU - STAT - 7034
EXST7034 Regression Techniques Assignment 1Fall 2005 Page 1EXST7034 : Regression TechniquesHOMEWORK ASSIGNMENTS : General Information PC/NT Workstations are available in room 11 and room 48, both in the basement of the Ag. Admin. Building. Clas
LSU - STAT - 7034
EXST7034 Regression Techniques Homework 1Fall 2005 Answer sheetGeaghan Page 1The SAS program I used to obtain the analyses for my answers is given below.dm'log;clear;output;clear'; *; * EXST7034 Homework Example 1 *; * Problem from Neter, Was
LSU - STAT - 7034
EXST7034 Regression Techniques Homework 2Fall 2004Geaghan Page 1HOMEWORK ASSIGNMENT 2Assigned: September 20, 2005 Due: September 27 or 29, 200510 Points 1 point each day lateA) Complete the following questions using the values from probl
LSU - STAT - 7034
EXST7034 Regression Techniques Homework 2Fall 2005 Answer sheetGeaghan Page 9The SAS program I used to obtain the analyses for my answers is given below.dm'log;clear;output;clear'; *; * EXST7034 Homework Example 1 *; * Problem from Neter, Was
LSU - STAT - 7034
EXST7034 Regression Techniques Homework 2Fall 2005Geaghan Page 1HOMEWORK ASSIGNMENT 3Assigned: October 13, 2005 Due: October 20, 200510 Points 1 point each day lateComplete the following questions from your textbook. The problems come fr
LSU - STAT - 7034
EXST7034 Regression Techniques Homework 3Fall 2005 Answer sheetGeaghan Page 17The SAS program I used to obtain the analyses for my answers is given below.*; * EXST7034 Homework Example **; * Applied Linear Statistical Models, 5th Edition, 200
LSU - STAT - 7034
EXST7034 Regression TechniquesFall 2005 GeaghanAssigned: October 20, 2005 Due: October 27, 200510 Points 1 point each day lateHOMEWORK ASSIGNMENT 4Complete the following questions from your textbook. The problems come from the "Patient sati
LSU - STAT - 7034
EXST7034 Regression Techniques Homework 4Fall 2005 Answer sheetGeaghan Page 24proc GLM data=Satisfaction; TITLE2 'Analysis with GLM'; MODEL Y=X2 X1 X3; RUN; proc REG data=Satisfaction; TITLE2 'Analysis with REG'; MODEL Y=X1 X2 X3 / vif stb PCO
LSU - STAT - 7034
EXST7034 Regression TechniquesHOMEWORK ASSIGNMENT 5Fall 2005Assigned: October 27, 2005 Due: November 3, 200510 Points 1 point each day lateSteroid use problem from chapter 8. The data is in dataset "http:/www.stat.lsu.edu/EXSTWeb/statlab/dat
LSU - STAT - 7034
EXST7034 Regression Techniques Homework 5Fall 2005 Answer sheetGeaghan Page 24The SAS program I used to obtain the analyses for my answers is given below.dm'log;clear;output;clear'; options nodate nocenter nonumber ps=512 ls=99 nolabel; ODS H
LSU - STAT - 7034
EXST7034 Regression TechniquesHOMEWORK ASSIGNMENT 6Fall 2003Assigned: November 3, 2005 Due: November 10, 200520 Points 1 point each day lateAll data is in "http:/www.stat.lsu.edu/EXSTWeb/statlab/datasets/KNNLdata/". Complete the following qu
LSU - STAT - 7034
EXST7034 Regression Techniques Homework 6Fall 2005 Answer sheetGeaghan Page 31The program for the following sections follows.dm'log;clear;output;clear'; *; * EXST7034 Homework Example 1 **; * Problem from Neter, Wasserman & Kuttner 1989, #2.1
LSU - STAT - 7034
EXST7034 Regression TechniquesHOMEWORK ASSIGNMENT 7Fall 2003Assigned: December 1, 2005 Due: December 8, 200510 Points 1 point each day lateAll data is in "http:/www.stat.lsu.edu/EXSTWeb/statlab/datasets/KNNLdata/". Do the car purchase exampl
LSU - STAT - 3201
EXST3201 Background material From the textbook The Statistical SleuthPage 1Mean [20]: In your text the word mean denotes a population mean () while the work average denotes a sample average (Y). Variance [20]: The variance is a measure of the di
LSU - STAT - 3201
An Introduction to SAS Programming SAS programs consists of two major type of steps The DATA step used to create or modify a SAS dataset [Contents > SAS Products > Base SAS > SAS Language concepts > Data Step Concepts] SAS dataset a file containin
LSU - STAT - 3201
EXST3201 Mousefeed01Page 13 /* 4 Examine differences among the following 6 treatments 5 N/N85 fed normally before weaning and 85 kcal/wk after 6 N/R40 fed normally before weaning and 40 kcal/wk after 7 N/R50 fed normally before weaning and 50 kc
LSU - STAT - 3201
EXST3201 Chapter 5 Analysis of Variance [Chapter 5]GeaghanFall 2005: Page 1Testing between two samples is readily done with the two-sample t-test. In this situation we compare two groups (also referred to as classes, categories, treatments or i
LSU - STAT - 3201
EXST3201 Mousefeed02Page 11 /* 2 Examine differences among the following 6 treatments 3 N/N85 fed normally before weaning and 85 kcal/wk after 4 N/R40 fed normally before weaning and 40 kcal/wk after 5 N/R50 fed normally before weaning and 50 kc
LSU - STAT - 3201
EXST3201 Anatomy of a SAS program*; * The initial part of the program will *; * often have some comments. *; * - *; * Little boxes and lines are a nice *; * way to isolate these comments and *; * make them stand out. *; * - *; * For this box a comm
LSU - STAT - 3201
EXST3201 Chapter 5 Analysis of Variance [Chapter 5, part 2]GeaghanFall 2005: Page 1A second case of analysis of variance, the Dr. Spock conspiracy trail. This case is an observational study, so the data does not come from a planned experiment,