Human Factors and Engineering Psychology

Cognitive load modulates attentional capture by color singletons during effortful visual search☆

2010-06-09T20:08:00.000-07:00

Cognitive load modulates attentional capture by color singletons during effortful visual search☆: "Publication year: 2010
Source: Acta Psychologica, In Press, Corrected Proof, Available online 31 May 2010
Bryan R., Burnham
Color singletons that are irrelevant to locating a visual target do not typically capture attention if visual search is effortful. In contrast, when search is efficient color singletons are often found to capture attention. Such distraction by a color singleton can be modulated by single-task vs. dual-task manipulations when visual search is efficient. This is due, presumably, to the increased cognitive load in the dual-task condition, which interferes with top-down attentional control. This study investigated whether capture by a color singleton is also modulated by single-task vs. dual-task manipulations when visual search was effortful. The results of three experiments revealed..."

import .edf files in batch into DataViewer

2009-10-14T22:15:00.001-07:00

Dear SR Researchers,

I love SR Research Product, and benifits a lot from your product. But how I wish I could import the .edf files in batch, because it take me over one hour to import the expeirment data into dataviewer. It is time cost and boring to do this manual work. To save my time, and many other researchers like me,I wrote a simple software toimport .edf in a batch into DataViewer. This will save me lots of time. You can download the software at the following link.
http://cid-bbc15003189d7799.skydrive.live.com/browse.aspx/Public/BatchImporter

Hope you like the tool.

As a fan of SR Research products, I love their eye tracker very much. But how I wish I could import the .edf files in batch, because it take me over one hour to import the expeirment data into dataviewer. It is time cost and boring to do this manual work. To save my time, and many other researchers like me,I wrote this simple software. I have tested the program with DataViewer 1.8.1. I guess it will work with any version of DataViewer.

------------------------------

---------------------
Function:
---------------------------------------------------
- To import the .edf files into DataViewer in batch.
- to Save your time

---------------------------------------------------
How to use?
---------------------------------------------------
1. set up config.ini
put the dataviewer directory in the second line in the config.ini.
The default directory is "c:\Program Files\SR Research\DataViewer\DataViewerW.exe"

2. set edf data location
put the directory of the edf files in edfLocation.txt
Each directory takes one line. It does not require the directory to be the folder just containing .edf. It can be any folders as long as itself or its subfolders contain the .edf file.

3. Click BatchImporter.exe to run
Then the software will ask DataViewer to import all the edf files you set in edfLocation.txt

---------------------------------------------------
License
---------------------------------------------------

I wrote this tool to help myself and my coworkers. I am not affiliated with SR Research. Use the tool at your own risk, although I do not believe there will be any risk.It is a free software.

Eprime Learning materials

2008-11-20T11:41:00.000-08:00

for those friends who are interested in e-prime programming.

Getting Started Guide.pdf

GettingStartedGuide

Reference Guide.pdf

ReferenceGuide

Users Guide.pdf

UsersGuide

and also a useful site @ CMU

http://step.psy.cmu.edu/

SurfLogger: A Logging Browser and Data Processing Method

2008-11-10T08:39:00.001-08:00

SurfLogger

SurfLogger , developed by He, Jibo at University of Illinois, is an automated data logging tool for web-based studies. It is written in python, free, open-source, cross-platform, and easy to modify. This page is devoted to information and resources about SurfLogger.

Features:

SurfLogger is a useful tool for collecting data for web-based researches. With its great features of automated data logging, free, open-source, cross-platform, and no dependence on other browsers, SurfLogger can free many researchers from the financial and time cost in data collecting. SurfLogger is expected to contribute more to the increasing interest in web-based researches.

Technical Specifics:

SurfLogger is written in Python, a scripting language, and the GUI (Graphical User Interface) is created with wxPython, which is a Python bundle of wxWidget. SurfLogger can record a variety of user actions with the web pages and the browsers. SurfLogger produces two files, logfile.txt and urlfile.txt. Logfile.txt stores action IDs (natural numbers assigned to each action, used to track the record to the responding actions), the time for each actions, interaction with the browsers (such as, clicking on the Back, Forward,Home, etc. buttons), and mouse coordination when clicking. The time record could be used to compute the time of completion for each task. The number of button press on the browsers could be used as a measure of effort in carrying out the task. SurfLogger also captures the images of each screen when the web page refreshes. Marking the mouse coordination on the screen captures could tell us what links the users clicked at. Urlfile.txt stores action IDs and URLs (Uniform Resource Locator). Action IDs are used to synchronize the record in logfile.txt and urlfile.txt. URL record is stored in a separate file because the abundant information it can provides. I will give an example about how to extract information from urlfile.txt in case study section of this paper.

SurfLogger also calls external software to record the whole process of user actions. Currently, I used Michael Urman’s Screen Recorder named cankiri as my external software for recording, because it is also written in python and shares the same spirit of open source. With video record, the researchers could know more about users’ actions. If quality of recording is emphasized, SurfLogger could easily switch to call other recording software, and only one line of the code has to change to refer to the path of the external software.

Case Study

To demonstrate how SurfLogger could benefit web-based research, I will briefly explain the usability analysis of IGroup as a case study (Wang, Jing, He, and Yang, 2007). IGroup is an image search engine, presenting the results in semantic clusters. To test whether IGroup can increase search efficiency compared to MSN, we developed the predecessor of SurfLogger, which functioned similarly like SurfLogger, but less flexible. We developed a measure of Search Effort to compare IGroup and MSN objectively. Search Effort was defined as the number of query input, and number of links and cluster names clicked by the users. Query input, links and cluster names clicked were extracted from URLs recorded by our automated logging tool. A sample URL recorded in this study was listed as follows:

Wednesday, August 30, 2006 3:06:54 PM

http://msra-vss50-b/igroup2/search.aspx?q=Disney#g,14,1,-1

The characters in bold, “Disney”, “14”, and “1” were the input query, ID of cluster name, and result page. The information could be extracted from the URL by simple text processing. For code of data reduction, URL extraction and source code of SurfLogger please refer to my project page of SurfLogger.

Resources:

1. Paper
Potential users could get more information and description about SurfLogger from my draft paper [click to download] . This paper will be presented at SCiP (Society of Computer in Psychology)' 2008 at Chicago.

2. Codes

nSource code

qSurfLogger.pyw and run.py

qDependencies:

nPsychoPy http://www.psychopy.org/

nwxPython http://www.wxpython.org

nOr download the dependency bundle at here

nExecutable program

qSurfLogger.exe

3. Data Processing Methods:
To be added soon. :-P

Rights

You are free to use this tool for non-commercial purpose. Use by commercial companies or organizations must get permission from the author, He, Jibo.

Download

nSource code:

qSurfLogger.pyw and run.py

qDependencies:

nPsychoPy http://www.psychopy.org/

nwxPython http://www.wxpython.org

nExecutable program

qSurfLogger.exe

Contact

Feel free to contact me for help and information. If you would like to help me with the development, do please let me know.

Department of Psychology,
Beckman Institute for Advanced Science and Technology
University of Illinois, Urbana Champaign,
603 East Daniel St.,
Champaign, IL 61820
Tel: 217-244-4461(office)
217-244-6763(lab)
Email: hejibo@gmail.com
MSN: hejibopku@hotmail.com

####Matlab Programming Notes####

2008-10-30T09:46:00.001-07:00

####Matlab Programming Notes####

by He, Jibo for Psych 593

Course Link:

http://www.psych.uiuc.edu/~alleras/classwelcome.htm

exam:

http://www.psych.uiuc.edu/~alleras/exam.htm

Resources for psychtoolbox

Psychtoolbox Wiki:

http://psychtoolbox.org/wikka.php?wakka=HomePage

Psychtoolbox 2.0

http://psychtoolbox.org/PTB-2/

Eyelink toolbox

http://www.psychtoolbox.org/wikka.php?wakka=EyelinkToolboxCredits

PTB tutorial @ Silver lab - University of California, Berkeley

http://argentum.ucbso.berkeley.edu/PTBtutorial

Matlab for the Behavioral Sciences: How to program your own experiment.

http://faculty.washington.edu/ionefine/MatlabCourseNotes08.html

An Introduction to Matlab

http://www.maths.dundee.ac.uk/~ftp/na-reports/MatlabNotes.pdf

Matlab style guidelines

http://www.datatool.com/downloads/matlab_style_guidelines.pdf

Help for Psychtoolbox

For beginners: Psychtoolbox Tutorial and Ione Fine's Psychtoolbox Tutorial.
The Psychtoolbox includes built-in help available from the MATLAB command line.
- Enter "help Psychtoolbox" at the MATLAB prompt for a list of Psychtoolbox function categories.
- To display a list of Psychtoolbox functions within a category ask for help with the category, e.g. "help PsychBasic."
- To get help for a Psychtoolbox function ask for help on the function, e.g. "help GetChar."
- Psychtoolbox functions such as Screen,which accept a subcommand as an argument, include built-in help for there subcommands.
  - For a list of all subcommands issue the function call with no arguments, e.g. enter "Screen" at the command line.
  - To display documentation for a subcommand invoke the subcommand with a trailing question mark, e.g. Screen('OpenWindow?')
Read our answers to frequently asked questions.
Your friends and colleagues might help. Check out the Psychtoolbox forum.

Help for MATLAB

Typing "doc" in MATLAB will activate their browser-based help system, which is quite handy.
You can search the Mathworks web site.
There's an active MATLAB newsgroup (mostly Windows and unix users).
Typing "help" and "help help" at the MATLAB command line will list help topics and explain MATLAB help.

--2008/8/28--

--Lecture 1--

1. Monitor and graphics card (VRAM)

•2. Dynamic Random Access Memory (D)RAM

–# L1 cache - 10 nanoseconds, 4 kilobytes to 16 kilobytes in size

–# L2 cache - around 20 to 30 nanoseconds, 128 kilobytes to 512 kilobytes in size) (some L3)

–# Main memory - Memory access of type RAM (around 60 nanoseconds, 32 megabytes to Gigabytes in size)

3. CRT Monitors and VGA:

Red, Green, and Blue Phosphor(磷光体;磷光剂) dots

Analog ergo the video/graphics adapter

Dot pitch (distance of two adjacent same color ) and resolution

The gun shots light on the monitor line by line.

4. LCD Monitors

•Digital

Notes: We can not make sure the exact time for stimuli on display and energy of the stimuli for LCD monitors.

5. Matlab

•MATrix LABoratory:
–High-level language •slow to interpret by machine •easy to understand by humans

• –I will teach you “syntax” •Algorithm based

• –Our vocabulary will be MATRICES.
MATLAB is optimized for matrix-based calculations

6.A few important commands

•SET PATH!

- Window or in command line:

–Setting the path in your code: –

>p=path;

–>path(p,‘o:\MatlabCourse’);

•pwd: Where are you?

•cd: Change Directory

•dir: lists contents of folder

•mkdir: makes a new directory •

•who: lists of variables in workspace

•whos: who + size of variables

•clear X: erases X

•clear all: erases all.

•help topic

•lookfor string

7. Matrices

First mentioned dimension is ALWAYS the number of rows
Second dimension = number of columns

•8. Name your matrices always lowercase

--2008/9/4--

--Lecture 2--

1. Attention: , and ; differs!!

>> e=['world','hello']

e =

worldhello

>> e=['world';'hello']

e =

world
hello

2. mod(x,y): remainder of the division of x by y:

•mod(10,3) = 1

•mod(9,3) = 0

• •SUPER USEFUL COMMAND (counterbalancing conditions)

floor.

Rounds towards minus infinity. •floor(3.4) = 3

ceil:

rounds towards plus infinity

round

rounds to nearest integer

fix

rounds towards zero

CLEAR ALL : clear the workspace

3. Script file

> edit moo.m

•FOR EXAMPLE: UofI.m

•FOR EXAMPLE: UofI.m ØWRITE A SCRIPT TO CALCULATE THE FIRST 10 numbers in the sequence:
u(n) = u(n-1) x (n+1)
with u(1) = 1

---solution 1: u.m-----------

function

[u]=u(n)

u(1)=1;

for

i=2:n

u(i)=u(i-1)*(i+1);

end

---solution 2:uofi.m----

u=zeros(10,1);

u(1)=1;

for

n=2:10

u(n,1)=u(n-1,1).*(n+1);

end

It would be even nicer, if we asked the user the first value of the series. –

–Use ‘input’ – –Syntax:
VAR = input(‘your text here’); – (EVALUATED INPUT)
–For entering strings of characters
VAR = input(‘Your text here’,’s’); – (NOT EVALUATED INPUT)

4. logic expression

LOGIC p.8

•AND: &

•OR: |

•Complement: ~

•Exclusive OR: xor (when a is true or b is true, but not true at the same time)

5. loop control

SWITCH:
syntax

•More generally, if testing the value inside variable 'n'

•switch n

• case n1

• …

• case n2

• …

• otherwise

• …

•end

HOMEWORK

--2008/9/11--

--Lecture 3--

disp('hello world')

% display a string or expression, equals print or printf in python or C

>> disp('type ''q''') % double single quotes to input ' in the string
type 'q'

%Write a program that asks user for PIN number, until user gets it right.

correctPin = 1234;

true =1;

counter=0;

while true

guessPin=input('Please input your PIN,type q to quit:\n')

counter=counter+1;

if guessPin==correctPin

disp('you input the correct PIN\n')

break;

elseif guessPin=='q'

disp('you quited successfully\n')

break;

else

disp('you input the wrong PIN\n')

end

if counter==3

disp('you failed for three times!\n')

break

end

find

–FINDS a specific value in an array (or matrix) and returns the

–SYNTAX:
indeces = find(expression);

–returns indeces for which expression is true.

Lookfor:

to look for help with keywords

--2008/9/17--

--Lecture 4--

Functions

•- can take input (passing variables)

•- can return outputs.

-variables are internal.

-NAME OF M-FILE AND FUNCTION HAVE TO BE THE SAME.

------example of function:-----------

function output = nameoffunction(passvar1,passvar2…);

Persistent Variables

•If you want a function to keep accessing a variable, every time you run it, declare the variable as persistent.

•In other words, Matlab will NOT erase it upon exiting the function, so it will be able to access it again.

GLOBAL variables:

• -> can be used ANYWHERE…

• modified anywhere too!

•

•PERSISTENT variables:

• -> allow you to have some values persist in memory from one function use to the next.

• -> UNTOUCHABLES!! outside of function.

•

• -> beware of what's lurking under the surface.

For further reading…

•> anonymous functions (do not require an m-file)

• > f = @(arglist)expression

• > sqr = @(x) x.^2;

•> subfunctions are created within a function

•> private functions: only visible to their parent directory scripts.

•> nested functions: share variables.

SAVING AND READING MATRICES

•save NAME

–saves ALL the variables in your workplace, regardless of differences in format, on file NAME.mat

–Type:

–> clear all %CHECK WORKSPACE

– %CHECK CURRENT DIR.

–> load Worlds

save NAME var1 var2

–specifies which variables to save in file NAME.mat

•if you want to ADD stuff to a current mat file:

–save NAME var1 -append

wilcard *

•save avariables a* %saves all variables starting with a in file avariables.mat

reading text is different than reading numbers, but you can transform one into the other:

–num2str: transforms numbers into strings

–int2str: transforms integers into strings

–mat2str: transforms 2D matrices into strings

–char: convert to character array.

–print to screen: sprintf

–print to file: fprintf

–read from string: sscanf

–read from file: fscanf

---------file operation---------

fid = fopen(filename)

–fclose(fid) %0 means success.

•%don’t suppress output.

firstline = fgetl(fid)

•%don’t suppress output.

–nextline = fgets(fid)

% fgetl reads a line but does NOT copy the end of line character to the string. fgets does.

feof(fid) : 0 while not end of file , 1 once it is found.

fprintf(fileID, PRINTING FORMAT, variable).

•Most common conversion characters.

–%c single character

–%d decimal notation

–%s string of characters

–INSIDE PRINTING area:

•\n new line

•\t horizontal tab

Rather than printing the text to the screen, let’s transfer it to another file.

–newfid = fopen(‘newgoldi.txt’,’w’);

–fprintf(newfid, ‘%s \n’, newline);

–

–‘w’ means write to this new file.

–‘r’ means open to read.

–‘rt’ reads as text.

–‘a’ means append (add at the end).

FIND SPACE CHARACTERS IN FIRST LINE.

–> spaces = find(firstline == ' ')

strcmp: compare whether two strings are the same

--2008/9/25--

--Lecture 5--

Intermixing text and variables

•fprintf('This is trial %2d.\n', trial);

•for count=1:10

–fprintf('This is trial %2d, and condition %d\n.',trial(count), condition(count));

–end;

Exercise --result display:

•Create a three column matrix with:

•first column: numbers from 1-10.

•second column: alternating 0-1.

•third column: random number between 150 and 1000.

•WRITE TO screen:

–think trial number, condition, RT.

Answer--

data = zeros(10,3);

data(:,1)=1:10;

data(:,2)=mod(data(:,1),2);

data(:,3)=rand(1,10)*850 +150;

%writes data column-wise.

%Treats matrix as comma-delimited list.

%CONTINUES EXECUTION until all the specified variables HAVE BEEN PRINTED.

%what we want is:
%data': the transposition of data

fprintf('%2d %d %3.1f\n',data');

Last issue.

•How do you print a ' or % or \ with fprintf?
ex: it's a beautiful day!
ex: I'm 100% certain 2\4=2.

•

•Answer: you double the escape character to make it printable (page17)

•> fprintf('I''m 100%% certain 2\\4=2.')

###########IMAGE###########

IMAGES (1) p. 22

•Let's play with Matlab's demo:

•Type:

•> clear all

•> load durer %check workspace.

•> image(X) %what happened?

•> colormap(map) %ruminate on this…

•> axis equal

•> axis image %same as equal, but image fits tightly

•> axis off %turns off tick marks

Other types of images

•You can load TIFF, JPEG, BMP… with

•imread

•[X,map] = imread(filename,ext);

Write images to files

•Let's make and save a random b/w mask image.

• imwrite(matrix,'nameoffile','extension')
%imwrite(matrix,colormap,'nof','ext') for indexed images

mask = rand(400,400);

imwrite(mask,'mask','bmp');

clear all

[x,map]=imread('mask','bmp');

image(x);

colormap(map);

•Let's make and save a random color-noise mask image -unindexed

•> mask2 = rand(400,400,3); %why 3?

•> imwrite(mask2,'mask2','jpg');

•> clear all;

•> input('click key when ready');

•> X = imread('mask2','jpg');

•> image(X);

•> colormap(map);

•> input('click key when ready');

•> colormap(hot);

Matlab homework5

1. durernoise.m

Create a program that creates 5 different versions of the durer image with increasing levels of noise, using the same grayscale(256) CLUT, all in one single image (BMP).

Level of noise is measured the deviation of the noise from the actual value of the pixel.

use the following as an example:

>> z = mod(X + (rand(size(X)).*32 - 16),128);
>> image(z)

% Author; He, Jibo, 09/26/2008
%Goal:
%Create a program that creates 5 different versions of the durer image with
%increasing levels of noise, using the same grayscale(256) CLUT, all in one
%single image (BMP).
load durer;
colormap(gray(256));
% create the image with noise
NoiseLevel1=mod(X+(rand(size(X)).*4-2),128);
NoiseLevel2=mod(X+(rand(size(X)).*8-4),128);
NoiseLevel3=mod(X+(rand(size(X)).*16-8),128);
NoiseLevel4=mod(X+(rand(size(X)).*32-16),128);
NoiseLevel5=mod(X+(rand(size(X)).*64-32),128);
combine=[NoiseLevel1'; NoiseLevel2';NoiseLevel3';NoiseLevel4';NoiseLevel5'];
image(combine');

% to polish the graph
axis off;
axis image;
xlabel 'Figure 1. Durer image with increasing levels of noise from left to right';

% write image
imwrite(combine',gray(256),'Durer.bmp','bmp');

Professor's solution:

Indexed image:

load durer; %gives me X and map

%I am going to create five matrices Xnoi which will be copies of X with

%increasing noise

Xnoi = X + rand(648,509).*60 -30; %noise here is created by randomly

%varying the luminance of a pixel

%the total range of luminance is

%124, so 30 is about 1/4 of that.

%PROBLEM: some indeces will be lower than 1 or larger than 128...

% so we correct for that. However you would like to do it!

below = find(Xnoi < 1); %find values of Xnoi that go below the colormap %index of 1

Xnoi(below) = 1; %for those values, we reassign a low luminance %value.

% the same in all cases!

above=find(Xnoi>128); % we do the same for indeces larger than 128.

Xnoi(above)= 128;

2. rgbnoise.m

Create a programm that takes the visionlab jpg logo and presents it in 2 different levels of black and white noise and two different levels of color noise, all in one single image that includes the untouched original. 3. Submit both images and corresponding script files.

www.psych.uiuc.edu/~alleras/courseImages.htm

--2008/10/02--

--Lecture 6--

Intermixing text and variables

textread

function.

•

•SYNTAX:

•A = textread('filename') transforms data in filename into Matrix A.

•ONLY WORKS WITH HOMOGENEOUS Matrices.

= v ns = "urn:schemas-microsoft-com:vml" />= o ns = "urn:schemas-microsoft-com:office:office" />= p ns = "urn:schemas-microsoft-com:office:powerpoint" />

•SYNTAX:

•[A,B,C] = textread('filename','%s%d%f')

•reads each column into a variable, of specified type.

strings are saved in "cell" arrays (multidimensional arrays whose elements are copies of other arrays, here a table of strings of different sizes).

~Cell Array~

names(1) is the cell itself

•so trash = name(1) makes trash a cell

•names{1} refers to the value in the cell

•so trash = name{1} makes trash a character array

•names{1}(j) is the jth element in the character array stored in the cell 1.

USE strcmp(string1,string2)
which is true if string1==string2.

name2f = input('what student?','s');

numstu = size(name,1); %number of rows

for findex=1:numstu

if (strcmp(name2f,names{findex}))

whichisit =findex;

end;

Matlab homework6

OPTION A. electricgraphiti.m Write a program that asks a user for his/her name and displays an image graphiti of their name using the gif letter files in: www.psych.uiuc.edu/~alleras/courseImages.htm

Save the name as three jpgs of poor, medium and high quality (as due to compression). Hint: beware of capitals.

OPTION B. Write a program that creates the image of a white circle (128 pixel radius) centered on a 400x400 black square. For extra credit (+2), make the brightness of the circle increase smoothly from black in the center to white at its edge. TOUGH GRADING. Hint: one piece of pie, but make sure all the pieces add up to the real thing!

OPTION C: Do both. Second one counts as extra credit. If you do both, plus the extra credit option, you'll get +4 of ec.

eval

example 1:

for

vowel=['a' 'e' 'i' 'o' 'u']

string = [

'let' vowel '.gif'] %concatenate file name string

[letter,map]= imread(eval(

' string '));

image(letter);

colormap(map);

axis

off;

axis

equal;

input(

'Ready for next? \n');

end

;

example 2:

for n=1:10

eval(['M' num2str(n) ' = zeros(n,n)'])

end

Avoid loops.

USE vectors of arrays of indeces to access all elements at once!

for example

%use find function to find elements of array satisfying a specific condition.

U=find(X<30) ;

X(U)=cos(t);

% use : to travel all elements of an array.

end: to reach the last element of a row or column

all: to test at once if all values are true/nonzero in a matrix (or along a dimension)

any: to test if ANY value is true/nonzero in a matrix

example:

string='A' :'Z'

if all(string<'a') % judge whether all are lower letter.

do-something

end

repmat: repeat same matrix a number of times.

ind2sub sub2ind: how to obtain indeces from arrays and vice-versa.

############################

Install Psychtoolbox

############################

Step 1. download at http://psychtoolbox.org/PTB-2/download.html

Psychtoolbox Win 2.54 (20 February 2004) requires Matlab 6.5 (Student or regular) or better.
Download zip archive (3.1 MB)

IMPORTANT UPDATES TO Win 2.54:

After you download Win Psychtoolbox 2.54, replace bug-ridden files with improved versions listed below:

WaitSecs.dll - Get it here.

CopyText.dll - Get it here.

Step 2: add psychtoolbox to the path folder of matlab

--2008/10/23--

--Lecture 8--

1. Structures

Multidimensional array elements accessed by textual designators. Each field can contain any type of Matlab data (numbers, strings, cells, etc).

Type:

Data.trial = 1;

Data.setsize = 3;

Data.tgtword = 'doctor';

Data.rt = 541;

Data.resp =1;

Data

Using the "struct" function:

Data = struct('label1', dummy1, 'label2',dummy2, etc);

Creates the structure:

data.label1 = dummy1

data.label2 = dummy2

Data(64) = struct('label1', dummy1, 'label2', dummy2, etc);

Access each element like a vector:

Data(34).label1 = 234;

data(n) =struct('field1',value1,'field2',value2)

initializes only the nth value. Others are set to empty matrices.

data = repmat(struct('trial',1,'rt',-1),1,64);

This way ALL values are initialized with values specified in the struct function.

Or: Use values saved in a cell array.

> a = cell(3,1);

> a{1} = 'bob';

> a{2} = 'where are you?';

> a{3} = 'I am here';

> data = struct('line',a);

What's data(2).line?

Planning an experiment

section 1:SETUP VARIABLES

-Initialize CONSTANTS (refresh rate)

-Initialize Variables (with comments so you know what each variable does)

-Load big files (images, sounds, mex…)

Section 2:BALANCE CONDITIONS

BALANCE your design:

-define conditions

-how many trials for each condition

-Randomize order of trials

Section 3: TRIAL LOOP

3.1 DRAW constant images

(fixation, blank screens)

3.2 Start Trial Loop

3.2.1 Draw trial specific stimuli (if any)

3.2.2 Present stimuli

3.2.3 Get response

3.2.4 Classify response (error?)

3.2.5 ERASE ANY TRIAL SPECIFIC STIMULI

Section 4:Save DATA

Open a subject file and write data to it. (personal preference).

save data after each block/trial to avoid data loss or software crash.

Section 5:CLEAN UP

Clear all the variables you used and

the images you created, and close

any opened files.

AND DON'T FORGET TO COMMENT AS MUCH AS YOU CAN!!!

AND DON'T FORGET SECTION 0: Comments at beginning of file for

"help".

The Psychophysics Toolbox

A set of functions to:

-Interact with Monitor (pg. 27-32)

-Interact with Keyboard and mouse

-Interact with your OS (Here, Windows XP)

Screen function:

-Function that helps us interact with our monitor.

- Many "sub-functions".

First thing:

OpenWindow:

[windowPtr,rect]=Screen(0,'OpenWindow',[color],[rectangle],[pixelSize]);

windowptr: a pointer to the space in memory we are allocating to work on this window (kinda like fid=fopen(..) keeps track of a file), can be called "fixation display",'practice' etc.

rect: (if specified, and I suggest you do) gives you the coordinates of the window you’ll be using in pixels, on the format [Xtop-left, Ytop-left, Xbotton-right, Ybottom-right]. e.g. [0 0 1280 1024]

0: refers to the main monitor (where you'll be presenting stimuli).

color: you want the window to be: if one number: an index (CLUT, between 0-255), or a RGB triplet [r g b]. Later we'll talk about

changing the CLUT.

rectangle: ignored in windows

pixelsize: you can set the pixelsize for your screens (8 bit -> 256 colors, 24 bit…). Default is unchanged.

Close Window

Two ways:

To close all windows:

Screen('CloseAll');

To close a specific window:

Screen(windowPtr,'Close');

VERY IMPORTANT!!!

"Hello World " to psychtoolbox

warning

off MATLAB:DeprecatedLogicalAPI

[windowPtr,rect]=Screen(0,

'OpenWindow',255); % CLUT 255 for white background, 0 for black

Screen(windowPtr,

'DrawText','Hello World',500,350,100);

KbWait; %wait for the user to push a key

Screen(

'CloseAll');

DETERMINE HOW LONG IT TOOK MATLAB

TO OPEN THE WINDOW AND WRITE TEXT

TO IT.

Use: GetSecs (returns seconds since computer was turned on);

OffScreenWindows

So, we can work "offline", prepare our screens and then quickly copy

them to the screen.

image = Screen(windowPtr,'OpenoffScreenWindow',color,smallerrect)

image is pointer to refer to this offscreen window

windowPtr is pointer to the monitor window (to which all windows are related)

smallerrect is size of offscreen window (can be smaller!)

So, Once we have our offscreen window, we THEN copy it to our main window.

Screen('CopyWindow', srcWinPtr,DestWindoPtr, [srcRect],[dstRect]);

srcWinPtr: is the windowPointer (name) of the window you want to copy (source)

Bugs: The color template in open window subcommand is BGR instead of RGB. The following command will create blue background.

[window,rect]=Screen(0,'OpenWindow',[255 0 0]);

image = Screen(window,

'OpenoffScreenWindow',[255 0 0],[0 0 100 100]);

HideCursor;

beep=MakeBeep(100,5);

Snd('Open');

Snd('Play',beep); % play beep sound.

PutImage

Screen(windowPtr,'PutImage', imagearray,[rect],[copymode]);

imagearray is a matrix like the ones we created a few weeks ago:MxN or MxNx3 (if 16 or 32 bits graphic card)

Help on Screen functions?

Type:

Screen(‘NameOfFunction?’)

Try:

Screen(‘PutImage?’)

Beeps and Wav sounds

Matlab can play wav sounds and beeps WHILE doing something else

(nice for experiments).

Just like we 'Open' a window, we need to 'Open' a sound channel:

Snd('Open');

and 'close' it when you are done:

Snd('Close');

Beeps

You can create beeps with MakeBeep, which creates a vector that will be interpreted by your sound card (just like a matrix of numbers is interpreted by your graphics card as an image).

beep = MakeBeep(frequency,duration,samplingrate);

Then you can play that beep:

Snd('Play',beep,samplingrate);

Wav files

Windows Audio files:

-> wavplay(y,FS) plays sound recorded in y vector at the sampling frequency specified in FS (same sound will be different if played faster (larger FS) or slower (smaller FS). For stereo playback, Y would be N-by-2 matrix (left, right channels).

->important:

wavplay(y,FS,'async') allows you to play that sound while continuing to do stuff in Matlab (non-blocking call).

wavrecord(N,FS,CH)

Synchronizing windows and monitor

To do so, we use the 'WaitBlanking' command in Screen.

Calling:

Screen(windowptr,'WaitBlanking')

will wait until your gun moves to the top of the monitor. (only true for CRT monitors and analog LCDs)

counting time with refresh rates:

->IF you want to present a stimulus for a specific amount of time, count time in refreshes.

TIME

counting time with GetSecs:

Cool and accurate. You can do something like:

t1=GetSecs;

t2=t1;

Screen('CopyWindow'…); %or whatever…

while ((t2-t1)< presentationTime)

t2=GetSecs;

end;

%This syntax is equivalent to WaitSecs.

tic (start counting time)

toc (count the time. )

###Get the refresh rate of monitor#######

%He, Jibo @UIUC, 10/30/2008

warning off MATLAB:DeprecatedLogicalAPI

[windowptr,rectangle]=screen(0,'OpenWindow',255);

Screen(windowptr,'WaitBlanking')

t1=GetSecs;

t2=t1;

counter=0;

while ((t2-t1)< 1)

Screen(windowptr,'WaitBlanking')

counter=counter+1;

t2=GetSecs;

end;

counter

Screen('CloseAll');

##############################

frames=FrameRate(window); %Matlab's inquiry function

###wait 100ms#######

frames=FrameRate(window)%present stimuli 100ms. to be accurate, the multiply of the refreshrate

Screen(window,'WaitBlanking',floor(frames./10))

##############################

Keyboard Management

List of useful functions:

KbCheck: status of keyboard

KbWait: waits for Keypress (returns GetSecs)

GetChar: waits for character

CharAvail: checks event queue for characters

FlushEvents: help manage event queue

EventAvail: checks for events

KbCheck

IS USEFUL TO CLEAN THE KEYBOARD

BUFFER!!

while KbCheck end;

will "clean" your buffer

(Technically, wait until it is clean)

[keyisDown,secs,keyCode]=KbCheck;

find(keyCode) which key in ASCII

or you can ask directly if a given key was hit:

if keyCode('Z') (would be 1 if the z key was hit)

%note capitalization.

KbName: toolbox function that allows us to name the different keys on the keyboard (primary label) (Note: '5' vs. '5%')

Usage: KbName(arg) if arg is a string ('z'): returns the keyCode for that key if arg is the array keyCode, KbName returns the label of the key.

KbName deals with KEYS not Characters!

leftTarget = KbName('left');

MOUSE Management

List of useful functions:

HideCursor

ShowCursor

GetClicks

GetMouse

SetMouse

[x,y,buttons]=GetMouse(windowPtr);

SetMouse(100,500) # move the mouse to (100,500)

PsychDemos

If you are wondering what kind of thing you can do with PTB and how some of your

ideas can be coded up, look at the demos that ship with PTB. Try typing:

>>help PsychDemos

This will give you a list of available demos and a short description of what they do. If you are curious what a certain

demo does you can inquire further. For example, type:

>>help MandelbrotDemo

This will tell you what this script does. If you are curious how this is implemented, type:

>>edit MandelbrotDemo

This will open the file MandelbrotDemo.m in an editor window. Don’t edit this file! You might cause some damage.

Instead, save the file under a new name. For example, ‘myMandelbrotDemo.m’. Now you can twiddle things in the file

and try to see what effect these changes have on the execution of the program. But before you start doing that, let’s get

acquainted with the single most important function in Psychtoolbox.

----------------------GUI in matlab--------------------

quesdlg

ButtonName=questdlg(Question,Title,Btn1,Btn2,

DEFAULT);

-up to three buttons.

-Default is optional.

ButtonName=questdlg('What is your wish?', ...

'Genie Question', ...

'Food','Clothing','Money','Money');

INPUTDLG

ANSWER = INPUTDLG(PROMPT) creates an input dialog box where

users can enter text, saved in the cell array ANSWER.

PROMPT is a cell array containing the PROMPT strings.

2008-09-27T16:20:00.000-07:00

A Brief History of

Human Computer Interaction Technology

Brad A. Myers

Carnegie Mellon University School of Computer Science Technical
Report CMU-CS-96-163
and

Human Computer Interaction Institute
Technical Report CMU-HCII-96-103

December, 1996

Please cite this work as:

Brad A. Myers. "A Brief History of Human Computer Interaction
Technology."
ACM interactions. Vol. 5, no. 2, March, 1998. pp. 44-54.

Human Computer Interaction Institute

School of Computer Science

Carnegie Mellon University

Pittsburgh, PA 15213-3891

bam@a.gp.cs.cmu.edu

Abstract

This article summarizes the historical development of major advances in
human-computer interaction technology, emphasizing the pivotal role of
university research in the advancement of the field.

A short excerpt from this article appeared as part of "Strategic Directions
in
Human Computer Interaction," edited by Brad Myers, Jim Hollan, Isabel Cruz,
ACM Computing Surveys, 28(4), December 1996

This research was partially sponsored by NCCOSC under Contract No.
N66001-94-C-6037, Arpa Order No. B326 and partially by NSF under grant number
IRI-9319969. The views and conclusions contained in this document are those
of
the authors and should not be interpreted as representing the official
policies, either expressed or implied, of NCCOSC or the U.S. Government.

Keywords: Human Computer Interaction, History, User Interfaces,
Interaction Techniques.

1.
Introduction

Research in Human-Computer Interaction (HCI) has been spectacularly
successful,
and has fundamentally changed computing. Just one example is the ubiquitous
graphical interface used by Microsoft Windows 95, which is based on the
Macintosh, which is based on work at Xerox PARC, which in turn is based on
early research at the Stanford Research Laboratory (now SRI) and at the
Massachusetts Institute of Technology. Another example is that virtually
all
software written today employs user interface toolkits and interface builders,
concepts which were developed first at universities. Even the spectacular
growth of the World-Wide Web is a direct result of HCI research: applying
hypertext technology to browsers allows one to traverse a link across the
world
with a click of the mouse. Interface improvements more than anything else
has
triggered this explosive growth. Furthermore, the research that will lead
to
the user interfaces for the computers of tomorrow is happening at universities
and a few corporate research labs.

This paper tries to briefly summarize many of the important research
developments in Human-Computer Interaction (HCI) technology. By "research,"
I
mean exploratory work at universities and government and corporate research
labs (such as Xerox PARC) that is not directly related to products. By "HCI
technology," I am referring to the computer side of HCI. A companion article
on the history of the "human side," discussing the contributions from
psychology, design, human factors and ergonomics would also be appropriate.

A motivation for this article is to overcome the mistaken impression that
much
of the important work in Human-Computer Interaction occurred in industry,
and
if university research in Human-Computer Interaction is not supported, then
industry will just carry on anyway. This is simply not true. This paper
tries
to show that many of the most famous HCI successes developed by companies
are
deeply rooted in university research. In fact, virtually all of today's
major
interface styles and applications have had significant influence from research
at universities and labs, often with government funding. To illustrate this,
this paper lists the funding sources of some of the major advances. Without
this research, many of the advances in the field of HCI would probably not
have
taken place, and as a consequence, the user interfaces of commercial products
would be far more difficult to use and learn than they are today. As described
by Stu Card:

"Government funding of advanced human-computer interaction technologies built
the intellectual capital and trained the research teams for pioneer systems
that, over a period of 25 years, revolutionized how people interact with
computers. Industrial research laboratories at the corporate level in Xerox,
IBM, AT&T, and others played a strong role in developing this technology
and bringing it into a form suitable for the commercial arena." [6, p.
162]).

Figure 1 shows time lines for some of the technologies discussed in this
article. Of course, a deeper analysis would reveal much interaction between
the university, corporate research and commercial activity streams. It is
important to appreciate that years of research are involved in creating and
making these technologies ready for widespread use. The same will be true
for
the HCI technologies that will provide the interfaces of tomorrow.

It is clearly impossible to list every system and source in a paper of this
scope, but I have tried to represent the earliest and most influential systems.
Although there are a number of other surveys of HCI topics (see, for example
[1] [10] [33] [38]), none cover as many aspects as this one, or try to be
as
comprehensive in finding the original influences. Another useful resource
is
the video "All The Widgets," which shows the historical progression of a
number
of user interface ideas [25].

The technologies covered in this paper include fundamental interaction styles
like direct manipulation, the mouse pointing device, and windows; several
important kinds of application areas, such as drawing, text editing and
spreadsheets; the technologies that will likely have the biggest impact on
interfaces of the future, such as gesture recognition, multimedia, and 3D;
and
the technologies used to create interfaces using the other technologies,
such as user interface management systems, toolkits, and interface builders.

Figure 1: Approximate time lines showing where work was performed
on
some major technologies discussed in this article.

2.
Basic Interactions

Direct Manipulation of graphical objects: The now ubiquitous
direct
manipulation interface, where visible objects on the screen are directly
manipulated with a pointing device, was first demonstrated by Ivan Sutherland
in Sketchpad [44], which was his 1963 MIT PhD thesis. SketchPad supported
the
manipulation of objects using a light-pen, including grabbing objects, moving
them, changing size, and using constraints. It contained the seeds of myriad
important interface ideas. The system was built at Lincoln Labs with support
from the Air Force and NSF. William Newman's Reaction Handler [30], created
at
Imperial College, London (1966-67) provided direct manipulation of graphics,
and introduced "Light Handles," a form of graphical potentiometer, that was
probably the first "widget." Another early system was AMBIT/G (implemented
at
MIT's Lincoln Labs, 1968, ARPA funded). It employed, among other interface
techniques, iconic representations, gesture recognition, dynamic menus with
items selected using a pointing device, selection of icons by pointing, and
moded and mode-free styles of interaction. David Canfield Smith coined the
term "icons" in his 1975 Stanford PhD thesis on Pygmalion [41] (funded by
ARPA
and NIMH) and Smith later popularized icons as one of the chief designers
of
the Xerox Star [42]. Many of the interaction techniques popular in direct
manipulation interfaces, such as how objects and text are selected, opened,
and
manipulated, were researched at Xerox PARC in the 1970's. In particular,
the
idea of "WYSIWYG" (what you see is what you get) originated there with systems
such as the Bravo text editor and the Draw drawing program [10] The concept
of
direct manipulation interfaces for everyone was envisioned by Alan Kay of
Xerox
PARC in a 1977 article about the "Dynabook" [16]. The first commercial systems
to make extensive use of Direct Manipulation were the Xerox Star (1981) [42],
the Apple Lisa (1982) [51] and Macintosh (1984) [52]. Ben Shneiderman at
the
University of Maryland coined the term "Direct Manipulation" in 1982 and
identified the components and gave psychological foundations [40].
The Mouse: The mouse was developed at Stanford Research Laboratory
(now SRI) in 1965 as part of the NLS project (funding from ARPA, NASA, and
Rome
ADC) [9] to be a cheap replacement for light-pens, which had been used at
least
since 1954 [10, p. 68]. Many of the current uses of the mouse were
demonstrated by Doug Engelbart as part of NLS in a movie created in 1968
[8].
The mouse was then made famous as a practical input device by Xerox PARC
in the
1970's. It first appeared commercially as part of the Xerox Star (1981),
the
Three Rivers Computer Company's PERQ (1981) [23], the Apple Lisa (1982),
and
Apple Macintosh (1984).
Windows: Multiple tiled windows were demonstrated in Engelbart's
NLS
in 1968 [8]. Early research at Stanford on systems like COPILOT (1974) [46]
and at MIT with the EMACS text editor (1974) [43] also demonstrated tiled
windows. Alan Kay proposed the idea of overlapping windows in his 1969
University of Utah PhD thesis [15] and they first appeared in 1974 in his
Smalltalk system [11] at Xerox PARC, and soon after in the InterLisp system
[47]. Some of the first commercial uses of windows were on Lisp Machines
Inc.
(LMI) and Symbolics Lisp Machines (1979), which grew out of MIT AI Lab
projects. The Cedar Window Manager from Xerox PARC was the first major tiled
window manager (1981) [45], followed soon by the Andrew window manager [32]
by
Carnegie Mellon University's Information Technology Center (1983, funded
by
IBM). The main commercial systems popularizing windows were the Xerox Star
(1981), the Apple Lisa (1982), and most importantly the Apple Macintosh (1984).
The early versions of the Star and Microsoft Windows were tiled, but eventually
they supported overlapping windows like the Lisa and Macintosh. The X Window
System, a current international standard, was developed at MIT in 1984 [39].
For a survey of window managers, see [24].

3.
Application Types

Drawing programs: Much of the current technology was
demonstrated in
Sutherland's 1963 Sketchpad system. The use of a mouse for graphics was
demonstrated in NLS (1965). In 1968 Ken Pulfer and Grant Bechthold at the
National Research Council of Canada built a mouse out of wood patterned after
Engelbart's and used it with a key-frame animation system to draw all the
frames of a movie. A subsequent movie, "Hunger" in 1971 won a number of
awards, and was drawn using a tablet instead of the mouse (funding by the
National Film Board of Canada) [3]. William Newman's Markup (1975) was the
first drawing program for Xerox PARC's Alto, followed shortly by Patrick
Baudelaire's Draw which added handling of lines and curves [10, p. 326].
The
first computer painting program was probably Dick Shoup's "Superpaint" at
PARC
(1974-75).
Text Editing: In 1962 at the Stanford Research Lab, Engelbart
proposed, and later implemented, a word processor with automatic word wrap,
search and replace, user-definable macros, scrolling text, and commands to
move, copy, and delete characters, words, or blocks of text. Stanford's
TVEdit
(1965) was one of the first CRT-based display editors that was widely used
[48]. The Hypertext Editing System [50, p. 108] from Brown University had
screen editing and formatting of arbitrary-sized strings with a lightpen
in
1967 (funding from IBM). NLS demonstrated mouse-based editing in 1968.
TECO
from MIT was an early screen-editor (1967) and EMACS [43] developed from
it in
1974. Xerox PARC's Bravo [10, p. 284] was the first WYSIWYG editor-formatter
(1974). It was designed by Butler Lampson and Charles Simonyi who had started
working on these concepts around 1970 while at Berkeley. The first commercial
WYSIWYG editors were the Star, LisaWrite and then MacWrite. For a survey
of
text editors, see [22] [50, p. 108].
Spreadsheets: The initial spreadsheet was VisiCalc which was developed
by Frankston and Bricklin (1977-8) for the Apple II while they were students
at
MIT and the Harvard Business School. The solver was based on a
dependency-directed backtracking algorithm by Sussman and Stallman at the
MIT
AI Lab.
HyperText: The idea for hypertext (where documents are linked
to
related documents) is credited to Vannevar Bush's famous MEMEX idea from
1945
[4]. Ted Nelson coined the term "hypertext" in 1965 [29]. Engelbart's NLS
system [8] at the Stanford Research Laboratories in 1965 made extensive use
of
linking (funding from ARPA, NASA, and Rome ADC). The "NLS Journal" [10,
p.
212] was one of the first on-line journals, and it included full linking
of
articles (1970). The Hypertext Editing System, jointly designed by Andy
van
Dam, Ted Nelson, and two students at Brown University (funding from IBM)
was
distributed extensively [49]. The University of Vermont's PROMIS (1976)
was
the first Hypertext system released to the user community. It was used to
link
patient and patient care information at the University of Vermont's medical
center. The ZOG project (1977) from CMU was another early hypertext system,
and was funded by ONR and DARPA [36]. Ben Shneiderman's Hyperties was the
first system where highlighted items in the text could be clicked on to go
to
other pages (1983, Univ. of Maryland) [17]. HyperCard from Apple (1988)
significantly helped to bring the idea to a wide audience. There have been
many other hypertext systems through the years. Tim Berners-Lee used the
hypertext idea to create the World Wide Web in 1990 at the government-funded
European Particle Physics Laboratory (CERN). Mosaic, the first popular
hypertext browser for the World-Wide Web was developed at the Univ. of
Illinois' National Center for Supercomputer Applications (NCSA). For a more
complete history of HyperText, see [31].
Computer Aided Design (CAD): The same 1963 IFIPS conference at
which
Sketchpad was presented also contained a number of CAD systems, including
Doug
Ross's Computer-Aided Design Project at MIT in the Electronic Systems Lab
[37]
and Coons' work at MIT with SketchPad [7]. Timothy Johnson's pioneering
work
on the interactive 3D CAD system Sketchpad 3 [13] was his 1963 MIT MS thesis
(funded by the Air Force). The first CAD/CAM system in industry was probably
General Motor's DAC-1 (about 1963).
Video Games: The first graphical video game was probably SpaceWar
by
Slug Russel of MIT in 1962 for the PDP-1 [19, p. 49] including the first
computer joysticks. The early computer Adventure game was created by Will
Crowther at BBN, and Don Woods developed this into a more sophisticated
Adventure game at Stanford in 1966 [19, p. 132]. Conway's game of LIFE was
implemented on computers at MIT and Stanford in 1970. The first popular
commercial game was Pong (about 1976).

4.
Up-and-Coming Areas

Gesture Recognition: The first pen-based input device,
the RAND
tablet, was funded by ARPA. Sketchpad used light-pen gestures (1963).
Teitelman in 1964 developed the first trainable gesture recognizer. A very
early demonstration of gesture recognition was Tom Ellis' GRAIL system on
the
RAND tablet (1964, ARPA funded). It was quite common in light-pen-based
systems to include some gesture recognition, for example in the AMBIT/G system
(1968 -- ARPA funded). A gesture-based text editor using proof-reading symbols
was developed at CMU by Michael Coleman in 1969. Bill Buxton at the University
of Toronto has been studying gesture-based interactions since 1980. Gesture
recognition has been used in commercial CAD systems since the 1970s, and
came
to universal notice with the Apple Newton in 1992.
Multi-Media: The FRESS project at Brown used multiple windows
and
integrated text and graphics (1968, funding from industry). The Interactive
Graphical Documents project at Brown was the first hypermedia (as opposed
to
hypertext) system, and used raster graphics and text, but not video (1979-1983,
funded by ONR and NSF). The Diamond project at BBN (starting in 1982, DARPA
funded) explored combining multimedia information (text, spreadsheets,
graphics, speech). The Movie Manual at the Architecture Machine Group (MIT)
was one of the first to demonstrate mixed video and computer graphics in
1983
(DARPA funded).
3-D: The first 3-D system was probably Timothy Johnson's 3-D CAD
system mentioned above (1963, funded by the Air Force). The "Lincoln Wand"
by
Larry Roberts was an ultrasonic 3D location sensing system, developed at
Lincoln Labs (1966, ARPA funded). That system also had the first interactive
3-D hidden line elimination. An early use was for molecular modelling [18].
The late 60's and early 70's saw the flowering of 3D raster graphics research
at the University of Utah with Dave Evans, Ivan Sutherland, Romney, Gouraud,
Phong, and Watkins, much of it government funded. Also, the
military-industrial flight simulation work of the 60's - 70's led the way
to
making 3-D real-time with commercial systems from GE, Evans&Sutherland,
Singer/Link (funded by NASA, Navy, etc.). Another important center of current
research in 3-D is Fred Brooks' lab at UNC (e.g. [2]).
Virtual Reality and "Augmented Reality": The original work on
VR was
performed by Ivan Sutherland when he was at Harvard (1965-1968, funding
by Air
Force, CIA, and Bell Labs). Very important early work was by Tom Furness
when
he was at Wright-Patterson AFB. Myron Krueger's early work at the University
of Connecticut was influential. Fred Brooks' and Henry Fuch's groups at
UNC
did a lot of early research, including the study of force feedback (1971,
funding from US Atomic Energy Commission and NSF). Much of the early research
on head-mounted displays and on the DataGlove was supported by NASA.
Computer Supported Cooperative Work. Doug Engelbart's 1968
demonstration of NLS [8] included the remote participation of multiple people
at various sites (funding from ARPA, NASA, and Rome ADC). Licklider and
Taylor
predicted on-line interactive communities in an 1968 article [20] and
speculated about the problem of access being limited to the privileged.
Electronic mail, still the most widespread multi-user software, was enabled
by
the ARPAnet, which became operational in 1969, and by the Ethernet from Xerox
PARC in 1973. An early computer conferencing system was Turoff's EIES system
at the New Jersey Institute of Technology (1975).
Natural language and speech: The fundamental research for speech
and
natural language understanding and generation has been performed at CMU,
MIT,
SRI, BBN, IBM, AT&T Bell Labs and BellCore, much of it government funded.
See, for example, [34] for a survey of the early work.

5.
Software Tools and Architectures

The area of user interface software tools is quite active now, and
many
companies are selling tools. Most of today's applications are implemented
using various forms of software tools. For a more complete survey and
discussion of UI tools, see [26].

UIMSs and Toolkits: (There are software libraries and tools
that
support creating interfaces by writing code.) The first User Interface
Management System (UIMS) was William Newman's Reaction Handler [30] created
at
Imperial College, London (1966-67 with SRC funding). Most of the early work
was done at universities (Univ. of Toronto with Canadian government funding,
George Washington Univ. with NASA, NSF, DOE, and NBS funding, Brigham Young
University with industrial funding, etc.). The term "UIMS" was coined by
David
Kasik at Boeing (1982) [14]. Early window managers such as Smalltalk (1974)
and InterLisp, both from Xerox PARC, came with a few widgets, such as popup
menus and scrollbars. The Xerox Star (1981) was the first commercial system
to
have a large collection of widgets. The Apple Macintosh (1984) was the first
to actively promote its toolkit for use by other developers to enforce a
consistent interface. An early C++ toolkit was InterViews [21], developed
at
Stanford (1988, industrial funding). Much of the modern research is being
performed at universities, for example the Garnet (1988) [28] and Amulet
(1994) [27] projects at CMU (ARPA funded), and subArctic at Georgia Tech
(1996,
funding by Intel and NSF).
Interface Builders: (These are interactive tools that allow interfaces
composed of widgets such as buttons, menus and scrollbars to be placed using
a
mouse.) The Steamer project at BBN (1979-85; ONR funding) demonstrated many
of
the ideas later incorporated into interface builders and was probably the
first
object-oriented graphics system. Trillium [12] was developed at Xerox PARC
in
1981. Another early interface builder was the MenuLay system [5] developed
by
Bill Buxton at the University of Toronto (1983, funded by the Canadian
Government). The Macintosh (1984) included a "Resource Editor" which allowed
widgets to be placed and edited. Jean-Marie Hullot created "SOS Interface"
in
Lisp for the Macintosh while working at INRIA (1984, funded by the French
government) which was the first modern "interface builder." Hullot built
this
into a commercial product in 1986 and then went to work for NeXT and created
the NeXT Interface Builder (1988), which popularized this type of tool.
Now
there are literally hundreds of commercial interface builders.
Component Architectures: The idea of creating interfaces by connecting
separately written components was first demonstrated in the Andrew project
[32]
by Carnegie Mellon University's Information Technology Center (1983, funded
by
IBM). It is now being widely popularized by Microsoft's OLE and Apple's
OpenDoc architectures.

6.
Discussion

It is clear that all of the most important innovations in Human-Computer
Interaction have benefited from research at both corporate research labs
and
universities, much of it funded by the government. The conventional style
of
graphical user interfaces that use windows, icons, menus and a mouse and
are in
a phase of standardization, where almost everyone is using the same, standard
technology and just making minute, incremental changes. Therefore, it is
important that university, corporate, and government-supported research
continue, so that we can develop the science and technology needed for the
user
interfaces of the future.

Another important argument in favor of HCI research in universities is that
computer science students need to know about user interface issues. User
interfaces are likely to be one of the main value-added competitive advantages
of the future, as both hardware and basic software become commodities. If
students do not know about user interfaces, they will not serve industry
needs.
It seems that only through computer science does HCI research disseminate
out
into products. Furthermore, without appropriate levels of funding of academic
HCI research, there will be fewer PhD graduates in HCI to perform research
in
corporate labs, and fewer top-notch graduates in this area will be interested
in being professors, so the needed user interface courses will not be
offered.

As computers get faster, more of the processing power is being devoted to
the
user interface. The interfaces of the future will use gesture recognition,
speech recognition and generation, "intelligent agents," adaptive interfaces,
video, and many other technologies now being investigated by research groups
at
universities and corporate labs [35]. It is imperative that this research
continue and be well-supported.

ACKNOWLEDGMENTS

I must thank a large number of people who responded to posts of earlier
versions of this article on the announcements.chi mailing list for their
very
generous help, and to Jim Hollan who helped edit the short excerpt of this
article. Much of the information in this article was supplied by (in
alphabetical order): Stacey Ashlund, Meera M. Blattner, Keith Butler, Stuart
K.
Card, Bill Curtis, David E. Damouth, Dan Diaper, Dick Duda, Tim T.K. Dudley,
Steven Feiner, Harry Forsdick, Bjorn Freeman-Benson, John Gould, Wayne Gray,
Mark Green, Fred Hansen, Bill Hefley, D. Austin Henderson, Jim Hollan,
Jean-Marie Hullot, Rob Jacob, Bonnie John, Sandy Kobayashi, T.K. Landauer,
John
Leggett, Roger Lighty, Marilyn Mantei, Jim Miller, William Newman, Jakob
Nielsen, Don Norman, Dan Olsen, Ramesh Patil, Gary Perlman, Dick Pew, Ken
Pier,
Jim Rhyne, Ben Shneiderman, John Sibert, David C. Smith, Elliot Soloway,
Richard Stallman, Ivan Sutherland, Dan Swinehart, John Thomas, Alex Waibel,
Marceli Wein, Mark Weiser, Alan Wexelblat, and Terry Winograd. Editorial
comments were also provided by the above as well as Ellen Borison, Rich
McDaniel, Rob Miller, Bernita Myers, Yoshihiro Tsujino, and the reviewers.

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2008-09-26T06:04:00.002-07:00

---------Making Gabor-----Matlab------

source:http://faculty.washington.edu/ionefine/html/MakingAGabor.html

-------function summary-----------

LINSPACE Linearly spaced vector.
    LINSPACE(X1, X2) generates a row vector of 100 linearly
    equally spaced points between X1 and X2.

    LINSPACE(X1, X2, N) generates N points between X1 and X2.
    For N < 2, LINSPACE returns X2.

Now to create a Gabor (a sinusoid windowed by a Gaussian) becomes a piece of cake. We simply multiply the two-dimensional Gaussian window by the two-dimensional Gabor.

------------------------------------

Making a Gabor

Here we are going to use some of the things we have learned to make a Gabor filter. These are regularly used for image processing. They are also popular as stimuli among vision scientists.

sd=0.3; % standard deviation
x=linspace(-1,1,100);
y=(1/sqrt(2*pi*sd)).*exp(-.5*((x/sd).^2));  % create a normal distribution
y=y./max(y); % scale it so it goes from 0-1

So y is a 1-d Gaussian with a standard deviation of sd=0.3. It has a minimum value of 0 and a maximum value of 1. We can plot this and see what it looks like.

plotting 1D Gaussian

plot(x, y);

create 2D Gaussian

Now we use the outer product to create a two-dimensional Gaussian filter, with a maximum value of 1 and a minimum of 0

filt=(y'*y);
disp(min(filt(:)));
disp(max(filt(:)));

  1.4962e-005

     1

view 2D Gaussian

And then we can look what that two dimensional Gaussian looks like. We are going to allow the colormap to take 256 possible values of gray. That means we want filt to vary between 1 and 256.

colormap(gray(256));
newmax=256;
newmin=1;
delta = (newmax-newmin);
scaled_filt = delta*filt + newmin;
image(scaled_filt);

Using the Gaussian filter to filter other images

We can then use this Gaussian window to filter any image we want. Let’s say we want to filter a sinusoidal grating. We actually use much the same set of tricks.

create 1-D sinusoid

sf=6; % spatial freq in cycles per image
y2=sin(x*pi*sf);
y2=scaleif(y2, 0, 1);
plot(x, y2);

ans =

     2

Create 1-D sinusoidal grating

This now gives us a 1-dimensional sinusoidal grating with a frequency of 6 cycles per image. We will scale it so it has a minimum value of 0 and a maximum value of 1. If we calculate the outer product of this sinusoid with itself we get a weird checkerboard.

img=(y2'*y2);
colormap(gray(256));
scaled_img = delta*img + newmin;
image(scaled_img);

Create 2-D sinusoid

What we want to do is calculate the outer product of the one-dimensional sinusoid with a vector of ones.

y3=ones(size(y2));
img=(y3'*y2);
colormap(gray(256))
scaled_img = delta*img + newmin;
image(scaled_img)
% Once again, img has a minimum of 0 and a maximum of 1
disp(max(img(:)))
disp(min(img(:)))

     1

     0

Create a Gabor

Now to create a Gabor (a sinusoid windowed by a Gaussian) becomes a piece of cake. We simply multiply the two-dimensional Gaussian window by the two-dimensional Gabor.

gabor=img.*filt;
scaled_gabor=delta.*gabor+1;
colormap(gray(256))
image(scaled_gabor);

Note that throughout this process we have worked with two versions of the Gaussian and the Gabor. The original versions (filt, img) were scaled between 0 and 1. We did this to keep the nice property that when the Gaussian filter was at its maximum, the brightness of the grating didn’t change, and when the Gaussian filter was at 0 the Gabor was also at 0. But when we used image to look at these matrices we converted them to range between 1-256 so as to match the colormap.

2008-09-26T06:04:00.001-07:00

Psychtoolbox-3 Tutorial

It would be great if someone could extend this tutorial for new features and standard operating procedures for version 3.

Until then, the following downloadable PDF file may give you a coarse overview over PTB-3's new features:
Talk slides of Psychtoolbox presentation, given at ECVP 2007 Arezzo

Strengths of Matlab & Psychtoolbox

Interpretive general language & a good interface to hardware

Unlike most software packages for experimental psychology and visual psychophysics, the Psychtoolbox is very general. It doesn't contain specific support for generating gratings, running trials, etc. Rather, it provides the user of a high-level interpreted language (Matlab) with a well-defined interface to the graphics hardware (frame buffer and lookup table) that drives the display. In this sense, it is a very generic tool. The power comes from the fact that once you can write arbitrary matrices into the frame buffer and lookup table as fast as the machine can go, everything else is easy to program in Matlab.

Matlab is a high level interpreted language with extensive support for numerical calculation (The MathWorks, 1993). The Psychophysics Toolbox provides a Matlab interface to the computer’s hardware. The core Psychtoolbox routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical blanking, support millisecond timing, and facilitate the collection of observer responses.

The Psychtoolbox doesn't limit the user—if the experiment can be run on the hardware, it can be run with the Psychtoolbox. In comparison, other environments for creating perception experiments provide very evolved support for specific experiments. Our experience with software packages not based on a general-purpose programming language has been that the very first thing we wanted to do turned out to be impossible.

We think the Matlab-Psychtoolbox combination has four winning features that we’d recommend for any experiment-design environment:

A general purpose language (Matlab) allows you to do new things.
For programs that use hardware intensely (e.g. display, keyboard), an interpreted environment (e.g. Matlab) speeds up software development greatly because simple tests can be performed immediately.
The key Psychtoolbox routines are C code, callable as functions from Matlab, that encapsulate the hardware, presenting a simple software interface to the user that provides full control. (In particular, the Psychtoolbox Screen.mex function provides a consistent high-performance user interface to the display, overcoming differences in synchronization behavior among graphics drivers from many manufacturers, within and between Mac and Win platforms.)
The Psychtoolbox Rush function allows you to run an arbitrary bit of code with little or no interruption. We call this "hogging the machine", blocking interrupts for the few seconds of a critical stimulus presentation.

The Psychtoolbox also provides interfaces for timing, sound, keyboard, and the serial port. And it includes many useful Matlab routines, such as color space transformations (Brainard, 1995; Brainard, Pelli, and Robson, 2002) and the QUEST (Watson and Pelli, 1983; Pelli and Farell, 1995) threshold seeking algorithm.

2. Transforming numbers into movies

Using a high-level platform-independent language like Matlab, it's easy to produce a matrix of numbers specifying the desired luminances of all the pixels in the displayed image. Today's off-the-shelf personal computers can copy those numbers from memory to video memory quickly enough to show a new image on every frame of a CRT monitor. However, high-level languages generally provide only rudimentary control of the vital transformations from number to color, and of the rate at which successive images are displayed.

That is where the Psychtoolbox comes in, providing simple but powerful functions to control the pixel transformation and timing synchronization of the computer-display interface.

Here’s a quick sketch of how computers display images. (See Brainard, Pelli, and Robson, 2002, for a fuller treatment.) Once the matrix of numbers has been loaded into frame buffer memory, the subsequent transformation from number to luminance (or color) is complicated, but usefully simplified to three steps. First, at video rates (e.g. 100 million pixels per second), each number passes through a lookup table, typically one 8-bit number in and three 8-to-10-bit numbers out, each driving an 8-to-10-bit digital-to-analog converter. Second, the three analog video signals drive the three guns of a color CRT. The luminance of light emitted by each monitor phosphor is proportional to the corresponding gun's beam current, which is an accelerating function of drive voltage; this is called the monitor's "gamma" function. Third, the luminous image is blurred by the point spread function of the beam.

Most graphics cards have adjustable pixel size, typically 8, 16, or 32 bits per pixel. Furthermore, while most have 8-bit Digital to Analog Converters (DACs), a few have 9- or 10-bit DACs. Many users write to ask what these numbers of bits mean. In the pixmap, each pixel is assigned a certain number of bits, 8, 16 or 32. The number of bits per pixel determines how many different colors you can have in one frame: 256, thousands, or millions. When you actually display an image, the pixel value is used as in index into a lookup table on your graphics card. The values in the lookup table are typically 8 bits per channel, but some cards have 9 or 10 bits per channel. Those values, output from the lookup table then drive digital to analog converters (DACs) with a corresponding precision, 8 to 10 bits. In 8-bit mode you can select any 256 colors. Within the lookup table, each color is specified by three 8-10 bit numbers. If instead you use the 32-bit mode (millions of colors) then the pixel is considered to be made up of three 8 bit values, one per channel (plus 8 bits of padding), each of which goes through a one-channel lookup table, again with 8-10 bit outputs. 16-bit mode is rarely useful. In that mode 5 bits are assigned to each channel (plus 1 bit of padding), allowing only 32 values per gun. Again, we have a longer treatment of this issue in our Display Characterization chapter (Brainard, Pelli, and Robson, 2002).
3. Toolbox overview

The basic idea is that you use Matlab to compute images or movies, and use new Matlab functions provided by the Psychtoolbox for accurate display. The Psychtoolbox routines treat the computer (Mac or Windows) as a display device: a frame buffer with a color lookup table. (To read about how to use frame buffers for visual psychophysics, see our psychophysics bibliography.)

The software has three layers. First, there is Matlab code that you write and some Matlab utilities that we supply, e.g. to compute color lookup tables and implement the QUEST staircase procedure. Second, there are a set of Matlab extensions (MEX or DLL files) that are written in C but callable from within Matlab. Third, the extension files, in turn, use OpenGL for graphics output and operating system facilities for other input and output.

The Screen mex file is the heart of the Psychophysics Toolbox, providing many subfunctions (selected by a text argument) that control the display screen(s). Experiments typically begin with a call to Screen('OpenWindow') and end with a call to Screen('CloseAll'). Anywhere in between, you may copy an image from a Matlab matrix onto the screen using Screen('PutImage') and change the lookup table using Screen('LoadClut') or (even better) Screen('LoadNormalizedGammaTable'). Typically you'll create a window on each screen that you're using in your experiment. Copying within or between windows is very fast. And you can create an unlimited number of offscreen windows (in memory, not visible) that can then be shown, one after another, as a movie, by copying to an onscreen window. Other Screen functions display text and dialogs and provide frame-accurate timing.

You can use the Screen function to write Matlab scripts that intermix graphics operations, calculations, and wait for observer responses. If you run the routines interactively from the command window, there will be a certain level of chaos as Matlab's windows overwrite parts of the experimental window. Still, this mode can be useful for debugging, especially if you restrict the window sizes to avoid overlap, or you have a second monitor.

Operations such as synching to vertical blanking and writing color lookup tables depend on the kind of video card(s) you have, and their video drivers. New versions of the computer operating system often include new video drivers. The Psychtoolbox provides a uniform interface, but you should check the timing on your computer, by running ScreenTest.m.

Note that Matlab has a number of built-in graphics commands, like BAR, that can draw into Matlab "figure" windows. Those commands won't draw into a Screen window. Use Matlab commands to draw into Matlab figures; use Screen to draw into Screen windows. For example, if you have an open Screen window, you can draw a black filled rectangle (10x25) in it by saying: Screen(window,'FillRect',BlackIndex(window),[0,0,25,10]). You can erase the whole window by overwriting with white: Screen(window,'FillRect').

Priority

A major challenge in doing psychophysics on modern personal computers is that operating systems are becoming more and more aggressive about stealing time away from your display code to do other things. Priority is a function that allows to protect your Matlab code (to some degree) from interruption. This allows you to keep your computer running more or less normally, with lots of background processes, yet grab complete control for the periods of time that it takes to present your stimuli.

Other Psychophysics routines

In addition to Screen and Priority, there are routines to satisfy all the needs of psychophysical experiments: Unbuffered keyboard i/o via KbCheck, KbWait, KbStrokeWait, KbName etc., mouse i/o via SetMouse, GetMouse and GetClicks, serial i/o via IOPort, timing via GetSecs and WaitSecs, sounds via PsychPortAudio and Snd, and threshold-estimation via the Quest staircase procedure. Other routines interface to the PhotoResearch PR-650 color meter, save images as EPS files, interface with Eyetrackers or EEG systems. An overview of the basic routines can be found by typing help PsychBasic. A high level overview over all categories of functions can be found by typing help PsychToolbox.

4. Displaying a grating with Screen.mex

Let's write a program to display a grating. We’ll open up a window on the screen, write a Matlab matrix into our window (i.e. into the frame buffer), and then close the window. These functions, Screen('OpenWindow'), Screen('PutImage'), and Screen('Close'), along with functions to load the lookup table and sync to the vertical blanking, are the heart of the Psychtoolbox.

First we open a full-screen window on screen 0 (the main monitor):

whichScreen = 0;
window = Screen(whichScreen, 'OpenWindow');

This full-screen window contains the entire screen, every pixel. There is no menu bar, title bar or border. You can ask Screen to open as many windows as you like, on as many monitors as you have, but usually you’ll want just a full-screen window on one monitor.

Next we figure out what numbers will produce white, gray, and black, and fill the whole window with gray.

white = WhiteIndex(window); % pixel value for white
black = BlackIndex(window); % pixel value for black
gray = (white+black)/2;
inc = white-gray;
Screen(window, 'FillRect', gray);

Now we use Matlab functions to compute a gabor patch (a grating vignetted by a gaussian envelope), and put that image into our window.

[x,y] = meshgrid(-100:100, -100:100);
m = exp(-((x/50).^2)-((y/50).^2)) .* sin(0.03*2*pi*x);
Screen(window, 'PutImage', gray+inc*m);

Now we ask the system to make our stimulus image visible at the beginning of the next video refresh interval, by "Flipping" it onto the visible display:

Screen(window, 'Flip');

Now we pause, displaying the grating until the observer presses any key, and finally close the window.

KbWait;
Screen('CloseAll');

That’s a complete program that will run on any computer with Matlab and the Psychtoolbox. It’s a slightly abbreviated version of GratingDemo.m, which is included in the Psychtoolbox.

In the above, the call to Screen('PutImage') slowly translates the Matlab double precision matrix into the pixmap format of the frame buffer. You won't want to do that while showing a movie. In that case you’d create a texture, which is allocated in your computer’s memory, and store the Matlab image matrix into it:

w = Screen(window, 'MakeTexture', gray+inc*m);

Now, whenever you like, you can blit (i.e. copy) very quickly (up to 80 GB/s depending on your graphics hardware—run ScreenTest to time your hardware) from the texture to onscreen graphics memory:

Screen('DrawTexture', window, w);

If you create multiple textures in advance, then you can show them, one after another, one per frame, to create a movie. The 'Flip' command will automatically synchronize to the vertical blanking of your display device:

for i = 1:100
    Screen('DrawTexture', window, w(i)); 
    Screen(window,'Flip'); 
end

The Psychtoolbox program MovieDemo.m illustrates this.

Incidentally, if you display stimuli on the main screen, as we often do, then the Screen window will hide the main menu bar and obscure Matlab’s command window. That can be a problem if your program stops (perhaps due to an error) before closing the window. The keyboard will seem to be dead because its output is directed to the front most window, which belongs to Screen not Matlab, so Matlab won’t be aware of your typing. It’s ok. Remain calm. Typing Ctrl-C will stop your program if hasn't stopped already. Typing command-zero (on the Mac) or Alt-Tab (on Windows) will bring Matlab’s command window forward. That will restore keyboard input. The screen might still be hard to make out, if you’ve been playing with the lookup table. Typing

clear Screen

will cause Matlab to flush Screen.mex. Screen.mex, as part of its exit procedure, cleans up everything it did, closing all its windows and restoring the lookup table of all its displays. And everything will be hunky dory again. Remember the magic incantations: command-zero (Mac) or ALT-tab (Win) to bring the command window forward, and "clear screen" to restore the displays to normal.

5. Examples

You got yourself a computer, bought Matlab, and installed the Psychtoolbox. Now you want to get your experiment running. Where to begin?

George Sperling pointed out to us recently that writing software from scratch is hard. It's much easier to edit an already working program that does something similar. The PsychDemos folder includes a variety of short programs that show how to do various specific things, including synthesizing and displaying a movie. Type

help PsychDemos

at the Matlab prompt for a list.

The Psychtoolbox website includes a Library page with links to programs written by other users. We invite everyone to send software to the Psychtoolbox forum, which automatically archives your message and enclosure. (Please include the keyword DONATE in the subject, so we can all search the forum for software.) We add links on the Library page to programs in the forum that appear to have enduring value.

By the way, don’t shortchange yourself. Buy enough memory ($0.4/MB) and disk space ($3/GB) as well as a recent graphics card to work comfortably.

6. Online help

The Psychtoolbox has no manual. Matlab has manuals, but we hardly ever use them. Instead we use the HELP command. Typing

help

will list a variety of broad topics on which Matlab offers help. You can ask for help on any function, including Matlab’s functions and any function in the Psychtoolbox. For example,

help help/techdoc/ref/meshgrid.html">meshgrid

will explain how the Matlab function meshgrid works. Similarly,

help Screen

will give a brief synopsis of Screen. If you type

help Psychtoolbox

you will get an overview of the hierarchical organization of the Psychtoolbox. For any of the subdirectories listed, you can get a synopsis of the functions in that subdirectory. So

help PsychBasic

will give you a synopsis of the core toolbox routines. The HELP facility is a fast way to explore Matlab and the Psychtoolbox, and we use it all the time.

Some of the Psychtoolbox functions, like Screen, have a large number of subfunctions, making it impractical to include all the information in the HELP display. Simply typing

Screen

will give you a synopsis of all the Screen subfunctions. For more detail on a specific subfunction, call Screen itself, adding a question mark to the sub function name.

Screen('CopyWindow?')

will type out helpful text for 'CopyWindow'. You can omit the parentheses and quote marks, because Matlab considers this

Screen CopyWindow?

equivalent to the above.

In our help text for specific functions, we've mostly followed Mathworks's help-text conventions. But note that we designate optional arguments to function calls by embracing them with square brackets. You're not meant to include these brackets when you actually call the function. For example, "help Snd" will tell you this:err = Snd(command,[sig],[rate]). What this means is that the "command" argument is required and the "sig" and "rate" arguments are optional. Thus, a typical call to Snd looks like this, and has no brackets: Snd('Play','Quack'). If you would like to force an optional argument explicitly to its default, you can typically pass the empty matrix. This is useful for functions with more than one optional argument where you'd like to (e.g.) accept the default on the first but explicitly pass the second.

Matlab usually ignores case (at least on Mac and Win platforms), except in variable names and built-in functions. The Psychtoolbox, by default, ignores case, but this is a user-settable preference. Although lazy typists can type everything in lower case, keep in mind that this practice may lead to portability problems somewhere down the line.

Another helpful tool is lookfor. Suppose you want to convert a variable of cell type to something else, such as a matrix. However, you have no idea what the function might be called.

lookfor cell

generates a list of all the functions with cell in the name. cell2mat is an obvious choice, and inspecting the list quickly teaches you about various cell related functions while you are working with the cell type.

7. Parallelize

Matlab is quick. Running on a 250 MHz PowerBook G3, the loop overhead is only 1 microsecond per iteration (after the first). Because it’s an interpreted language, it takes time (7 microseconds) to process each statement. However, one statement can perform a large number of elementary operations, e.g. adding two 100 element matrices requires 100 adds. Matlab does the elementary operations very efficiently. The large 76:1 ratio of the 7 microsecond statement overhead to the 0.09 microseconds per elementary operation (~ + - * / = = & | sin sign) is a defining characteristic of the language. You can run the Psychtoolbox program SpeedTest to assess these parameters for your own computer.

The implication, worth remembering, is that the run time of statements that operate on fewer than 76 elements is mostly spent processing the statement, not the elements. An example may help.

x = ones(10); 
y = ones(10);

This creates two 100 element arrays. (They’re 10x10 square matrices.) In languages, like C or BASIC, that lack matrix operations, one would add x and y by writing a loop.

for i = 1:100
    z(i) = x(i) + y(i); 
end

That works in Matlab too, but it runs very slowly, taking 800 microseconds (i.e. 8 microseconds per iteration, for 100 iterations). The right way to do this in Matlab is to operate on the whole matrix at once,

z = x + y;

which takes just 16 microseconds. That’s 50 times faster!

Again, the thing to remember is that the run time of statements that operate on fewer than 76 elements is mostly spent processing the statement, not the elements. An important part of learning Matlab is learning how to operate on lots of elements at once, as in the above example.

All of the timing above is for compiled code. Matlab compiles functions and loops before executing them, so you’ll usually benefit from the compilation without having to think about it. The one case you should avoid is calling a script (i.e. not a function) repeatedly; you should convert that script into a function.

8. Use the debugger

Matlab has a great built-in debugger, allowing you to step through your program, examine and modify variables, and set breakpoints. However, in the Mac version, the way you start it up is confusing, at least the first time you do it, which discourages many people enough that they never discover how useful the debugger is.

Be warned that, on the Mac, the debugger has slight difficulties with files that are in the Matlab toolbox folder (which includes the Psychtoolbox) and that the debugger may give a spurious error beep if you choose to debug a file whose name has any uppercase characters. For best results, debug a file outside the toolbox folder with a filename that’s entirely lowercase. Later on, once you've got the hang of using the debugger, you can ignore this restriction, but, as a beginner, it'll be less confusing to respect it.

Suppose you just wrote a function called foo.m, and you’ve got the file open, in a window called "foo.m". Click on the debugger icon (a green bug) in the window’s title bar. This will open the debugger window, which (confusingly) is also called "foo.m". Note the debugger's flow control icons at the left end of the title bar. Now set a breakpoint somewhere in your program by clicking one of the dashes "—" that appear in the left margin of the window, next to each statement. Clicking the dash turns it into a red dot , a breakpoint. (You can set multiple breakpoints, if you like.)

Sometimes when you try to set a breakpoint, you'll get a beep and no red dot. This usually means that Matlab is having trouble finding your file. (Which is sad, considering that it's got the file open.) Setting a breakpoint seems to be implemented in effect by issuing a command like "dbstop foo 17". This will fail if foo is neither in Matlab's path (a list of folders of likely places to find stuff) nor the current directory. You fix this by using the Matlab CD command to set the current directory to be the folder that contains the file your debugging, foo.m. If that succeeded, you should be able to open foo by typing "edit foo" in the command window. Now you should be able to set breakpoints without difficulty.

Now you’re ready to run your program. You’d have thought that you could just click something to say "Go!". No such luck. You must now go back to the Matlab command window. Using the keyboard shortcut, type command-zero (on a Mac) or xxx (on Win) to bring the command window forward. Now run foo, by typing its name:

foo

The program will begin execution and halt when it gets to your breakpoint. The command window will display a special prompt

K>>

indicating that you’re in the debugger. You can issue any Matlab command you like. Mostly you’ll simply type variable names to see what values they have. You can resume execution by typing

dbcont

but, instead, you’ll probably find it more convenient to go back to the debugger window by typing the shortcut command-4 (Mac) or xxx (Win), and use the flow-control icons in the title bar. You can single step , descend into a subroutine , ascend to the calling program , continue , or stop . You can also add or remove breakpoints . When you’re done, you should remove all the breakpoints.

9. Measuring threshold

You can measure whatever you like, but it is often useful to measure the stimulus intensity that yields a criterion level of observer performance (Pelli and Farell, 1995). The Psychtoolbox includes Matlab code implementing the QUEST procedure for estimating threshold.

Experiments are usually organized as a run (e.g. 40 to 100) of trials. Each trial presents stimuli to the observer and waits for a response. Each trial takes several seconds. To measure threshold you’ll write a loop, with one iteration per trial.

Before starting the loop, you’ll initialize QUEST, giving it a rough guess for the value of threshold. You may also want to ask for the observer’s name and so on.

Within that loop are the guts of your experiment. Typically you might call QUEST to ask it to suggest a good contrast to test at, based on the initial guess and all the observer’s responses so far. Then you’d compute an appropriate stimulus and display it briefly in a window. If you’re using a two-interval forced choice paradigm you’ll have two intervals, announce by beeps, and display the signal in only one of them. Then you’ll wait for the observer’s response, typically a keypress or mouse click. Finally, tell QUEST what contrast you actually tested at and whether the observer’s response was right or wrong. The Psychtoolbox demo program ContrastThreshDemo illustrates how QUEST is used in the toolbox environment. We recommend discarding the observer’s first response, just in case he or she wasn’t quite ready.

Finally, after the last trial, you’ll report QUEST’s threshold estimate and confidence interval.

Judging from email queries we’ve received from users, the most common beginner’s mistake is to forget to leave things in the same state at the end of the trial as they were at the beginning. If you open a window at the beginning of the trial (on- or off-screen) then close it at the end. Otherwise you’ll eat up memory fast, adding yet another window on each trial. The symptom of this programming error is that the experiment works perfectly for a few trials but eventually fails, when it runs out of memory.

We suggest that you avoid opening and closing windows (whether on- or off-screen) within a trial because it’s slow. It’s better to open all the windows you’ll need ahead of time and then just use them on each trial. Finally, after the last trial, you should close them all.

10. Calibration

Everyone says that you should calibrate your monitor so that you’ll know what you’re displaying, but rarely is software and a photometric instrument provided to help you do it. The Psychtoolbox, being free software, doesn’t include the instrument, but it does include software, in PsychCal, which should help, though it still isn’t as well documented as we’d like. Our measure page has some suggestions on what to buy. You may wish to read our chapter on display calibration (Brainard, Pelli, and Robson, 2002).

11. use psychotoolbox and fMRI

There are many ways you can interface matlab with your EEG or MRI system. Here is an example on how to make it work in fMRI.

A - How the MRI is set

The MRI trigger is converted via a ForbInterface unit (Current Designs) into a TTL send to the mouse port.
The subject response is received via the same Forbinterface and plugged into your computer. Responses are perceived as keyboard keys.

B - Basis of the program

Make sure responses, MRI trigger (mousse) and timing are correct using the priorityLevel function
e.g. priorityLevel=MaxPriority(['GetSecs'],['KbCheck'],['KbWait'],['GetClicks']);

Get the starting point of the MRI with GetSecs, load your stimuli after each MRI pulse (mouse click) and record the timing
e.g.

MRIstart = GetSecs;
WaitTTL = GetClicks;
if WaitTTL == 1
    t = GetSecs; t = t-MRIstart;
    % .. do something here ..
end

in the core of the experiment one can collect responses with KbCheck
e.g.

start = GetSecs;
timeSecs = KbWait;
[keyDown, secs, keyCode] = KbCheck;
stop = GetSecs;
rt_catch(nbtrial_catch) = stop - start;
         
success = 0;
while success == 0
    pressed = 0;
    while pressed == 0
        [pressed, secs, kbData] = KbCheck;
    end
    for i = 1:length(keysWanted)
        if kbData(keysWanted(i)) == 1
            success = 1;
            keyPressed = keysWanted(i);
            break;
        end
    end
end

Finally, depending on your TR, the PPT will return an error message related to timing issues (too long delays)
One can use WaitSecs between events (and after you collect the subject response) to make sure everything is ok
e.g. WaitSecs(TR-TA); % given that you set the acquisition time (time to acquire a volume - TA) and repetition time (time between volumes - TR)

ATTENTION: for an unknown reason FlushEvents overloads so it is not used here.

12. Good luck!

References

Brainard, D. H. (1995) Colorimetry. In Handbook of Optics: Volume 1. Fundamentals, Techniques, and Design. M. Bass (ed.). McGraw-Hill, New York, 26.1-54.

Brainard, D. H. (1997) The Psychophysics Toolbox. Spatial Vision 10:433-436 (PDF)

Brainard, D. H., Pelli, D.G., and Robson, T. (2002). Display characterization. In the Encyclopedia of Imaging Science and Technology. J. Hornak (ed.), Wiley. 172-188. (PDF)

The MathWorks (1993) Matlab User's Guide. The MathWorks, Inc., Natick, MA.

Pelli, D.G. (1997) The VideoToolbox software for visual psychophysics: Transforming numbers into movies. Spatial Vision 10:437-442. (HTML)

Pelli, D. G. and Farell, B. (1995) Psychophysical methods. In Handbook of Optics: Volume 1. Fundamentals, Techniques, and Design. M. Bass (ed.). McGraw-Hill, New York, 29.1-13.

Pelli, D. G. and Zhang, L. (1991) Accurate control of contrast on microcomputer displays. Vision Research 31, 1337-1350. [pdf]

Watson, A. B. and Pelli, D. G. (1983) QUEST: a Bayesian adaptive psychometric method. Perception and Psychophysics 33, 113-120

To read PDF files you may want to download the free Acrobat reader from Adobe.

Thanks to George Sperling for suggesting that a tutorial and examples would be useful.

David Brainard, Denis Pelli & Allen Ingling.
psychtoolbox@yahoogroups.com

19 September 2000

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2008-09-26T06:04:00.000-07:00

Psychtoolbox-3 Tutorial

Strengths of Matlab & Psychtoolbox

A general purpose language (Matlab) allows you to do new things.
For programs that use hardware intensely (e.g. display, keyboard), an interpreted environment (e.g. Matlab) speeds up software development greatly because simple tests can be performed immediately.
The key Psychtoolbox routines are C code, callable as functions from Matlab, that encapsulate the hardware, presenting a simple software interface to the user that provides full control. (In particular, the Psychtoolbox Screen.mex function provides a consistent high-performance user interface to the display, overcoming differences in synchronization behavior among graphics drivers from many manufacturers, within and between Mac and Win platforms.)
The Psychtoolbox Rush function allows you to run an arbitrary bit of code with little or no interruption. We call this "hogging the machine", blocking interrupts for the few seconds of a critical stimulus presentation.

whichScreen = 0;
window = Screen(whichScreen, 'OpenWindow');

white = WhiteIndex(window); % pixel value for white
black = BlackIndex(window); % pixel value for black
gray = (white+black)/2;
inc = white-gray;
Screen(window, 'FillRect', gray);

Now we use Matlab functions to compute a gabor patch (a grating vignetted by a gaussian envelope), and put that image into our window.

[x,y] = meshgrid(-100:100, -100:100);
m = exp(-((x/50).^2)-((y/50).^2)) .* sin(0.03*2*pi*x);
Screen(window, 'PutImage', gray+inc*m);

Now we ask the system to make our stimulus image visible at the beginning of the next video refresh interval, by "Flipping" it onto the visible display:

Screen(window, 'Flip');

Now we pause, displaying the grating until the observer presses any key, and finally close the window.

KbWait;
Screen('CloseAll');

w = Screen(window, 'MakeTexture', gray+inc*m);

Now, whenever you like, you can blit (i.e. copy) very quickly (up to 80 GB/s depending on your graphics hardware—run ScreenTest to time your hardware) from the texture to onscreen graphics memory:

Screen('DrawTexture', window, w);

for i = 1:100
    Screen('DrawTexture', window, w(i)); 
    Screen(window,'Flip'); 
end

clear Screen

help PsychDemos

help

will list a variety of broad topics on which Matlab offers help. You can ask for help on any function, including Matlab’s functions and any function in the Psychtoolbox. For example,

help help/techdoc/ref/meshgrid.html">meshgrid

will explain how the Matlab function meshgrid works. Similarly,

help Screen

will give a brief synopsis of Screen. If you type

help Psychtoolbox

you will get an overview of the hierarchical organization of the Psychtoolbox. For any of the subdirectories listed, you can get a synopsis of the functions in that subdirectory. So

help PsychBasic

Screen

will give you a synopsis of all the Screen subfunctions. For more detail on a specific subfunction, call Screen itself, adding a question mark to the sub function name.

Screen('CopyWindow?')

will type out helpful text for 'CopyWindow'. You can omit the parentheses and quote marks, because Matlab considers this

Screen CopyWindow?

lookfor cell

x = ones(10); 
y = ones(10);

This creates two 100 element arrays. (They’re 10x10 square matrices.) In languages, like C or BASIC, that lack matrix operations, one would add x and y by writing a loop.

for i = 1:100
    z(i) = x(i) + y(i); 
end

z = x + y;

foo

The program will begin execution and halt when it gets to your breakpoint. The command window will display a special prompt

K>>

indicating that you’re in the debugger. You can issue any Matlab command you like. Mostly you’ll simply type variable names to see what values they have. You can resume execution by typing

dbcont

MRIstart = GetSecs;
WaitTTL = GetClicks;
if WaitTTL == 1
    t = GetSecs; t = t-MRIstart;
    % .. do something here ..
end

in the core of the experiment one can collect responses with KbCheck
e.g.

start = GetSecs;
timeSecs = KbWait;
[keyDown, secs, keyCode] = KbCheck;
stop = GetSecs;
rt_catch(nbtrial_catch) = stop - start;
         
success = 0;
while success == 0
    pressed = 0;
    while pressed == 0
        [pressed, secs, kbData] = KbCheck;
    end
    for i = 1:length(keysWanted)
        if kbData(keysWanted(i)) == 1
            success = 1;
            keyPressed = keysWanted(i);
            break;
        end
    end
end

19 September 2000

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Behavioral Experiment Software Survey Results

2008-07-08T13:33:00.000-07:00

Behavioral Experiment Software Survey Results

Hi everyone,

The results of the software survey are in. We had 187 responses, but one was unanalyzable (the respondent did not specify a software package). Thanks everyone for your responses; I hope these results prove useful.

Here's a one-paragraph summary of the survey results; details below: E-Prime is the most popular package of those surveyed, but the majority of folks are using either E-Prime, DMDX, or some flavor of PsyScope. E-Prime, PsyScope, SuperLab, and ERTS are all rated as easy to build experiments with, and about equally so. DMDX and NESU are seen as slightly harder. Presentation and MatLab are notably the hardest of the commonly used packages. SuperLab was seen as easiest for novices. E-Prime and PsyScope were rated a shade harder for novices, and then from ERTS to SuperLab Pro to DMDX to NESU, novice-ease ratings dropped. Presentation and MatLab were both seen as notably difficult for novices. DMDX nets the highest satisfaction rating, with PsyScope X and E-Prime a smidge lower. MatLab, SuperLab, and Presentation rank below that, ending with NESU, PsyScope classic, and SuperLab Pro. PsyScope X has the highest "sticking with" rate, and E-Prime, SuperLab, MatLab, and DMDX all have 50%+ "sticking with" ratings. People are running away from PsyScope classic in droves, probably because the classic Mac OS is very quickly approaching end-of-life. E-Prime, both flavors of PsyScope, and DMDX are highly recommended. MatLab is also well recommended. Then it drops noticeably to Presentation, SuperLab, ERTS, and finally SuperLab Pro and NESU. A summary of two open-ended questions is also included below.

Details:

Of the 186 valid responses, the most commonly used software package ended up being E-Prime (57 responses, 30.6%). Here's the ranking of software packages, listing number of respondents and percentage for each:

1. E-Prime: 57, 30.6%

2. DMDX: 32, 17.2%

3. PsyScope Classic: 18, 9.7%

4. Presentation: 12, 6.5%

5. PsyScope X: 11, 5.9%

6. NESU: 8, 4.3%

7. ERTS: 6, 3.2%

8. SuperLab: 5, 2.7%

9. MatLab: 5, 2.7%

10. SuperLab Pro: 4, 2.2%

11. Linger: 4, 2.2%

12. MEL: 3, 1.6%

13. Experiment Builder: 3, 1.6%

14. EyeTrack: 2, 1.1%

The following packages had 1 response each: Authorware, C programming, Delphi Borland, DirectRT, ExBuilder, Habit, Inquisit, MacroMedia Director, PHPsurveyor, PCexpt, RSVP, WWStim, WebExp, iMovie, tscope, vision analyzer/recorder.

Bob Slevc worked to dig up links for many of these software packages. I'll put the links below the signature line.

Note that PsyScope classic and PsyScope X could be combined to have a total response count of 29 or 15.6%, which would keep it in third place behind E-Prime and DMDX. SuperLab and SuperLab Pro (which I assume are distinct) could similarly be combined for 9 responses (4.8%), which would put it right behind Presentation.

We asked, "How easy/hard is it for you to build an experiment with your software?" with a 7-point response scale (1 = "very easy" and 7 = "very hard"). Overall, the mean difficulty rating was 3.09 with a standard deviation of 1.43, and a median of 3. Here are the mean and median build-difficulty ratings for the packages that received at least five responses (combining the above noted PsyScope and SuperLab):

ERTS (6): 2.5, 2

E-Prime (57): 2.68, 2

PsyScope (29): 2.86, 3

SuperLab (9): 3.0, 3

NESU (8): 3.25, 3

DMDX (32): 3.38, 3.5

MatLab (5): 4.2, 4

Presentation (12): 4.54, 5

We also asked, "How easy/hard is it for a novice to learn how to build experiments with your software?" The mean difficulty rating was 4.12 with a standard deviation of 1.63, and a median of 4. Here are the mean and median build-difficulty ratings for the primarily used packages (for this analysis, PsyScope classic and PsyScope X were combined, because their response profiles were similar; SuperLab and SuperLab pro were different, so are reported separately):

SuperLab (5): 2.6, 3

PsyScope (29): 3.5, 3.5

E-Prime (57): 3.54, 3

ERTS (6): 4.17, 4.5

SuperLab Pro (4): 4.25, 4.5

DMDX (32): 4.88, 5

NESU (8): 5, 5.5

Presentation (12): 5.64, 6

MatLab (5): 6, 6

We then asked, "How satisfied are you with your current software?" with "1" meaning "Completely dissatisfied" and "7" meaning "Completely Satisfied." The mean satisfaction rating was 4.59 with a standard deviation of 1.43, and a median of 5. Here are the mean and median ratings by package:

DMDX (32): 5.09, 5

PsyScope X (11): 4.91, 5

E-Prime (57): 4.77, 5

MatLab (5): 4.4, 4

SuperLab (5): 4.2, 4

Presentation (12): 4.09, 4

NESU (8): 3.88, 4

PsyScope classic (18): 3.82, 4

SuperLab Pro (4): 3, 3

Two more quantitative questions. First, we asked "Are you sticking with your current software for the foreseeable future, or are you looking to change setups?" 25 respondents responded with "Don't Know," 103 with "Sticking with my current software for the foreseeable future," and 44 with "looking to change." Here's the breakdown by package, reporting percentages of those sticking with their package and those looking to change (sorted by sticking percentage):

PsyScope X (10): 90%, 10%

E-Prime (52): 71.1%, 9.6%

SuperLab (5): 60%, 40%

MatLab (5): 60%, 40%

DMDX (30): 53%, 20%

Presentation (11): 45.4%, 36.4%

ERTS (5): 40.0%, 60%

PsyScope classic (16): 37.5%, 62.5%

NESU (8): 12.5%, 62.5%

SuperLab Pro (4): 0%, 50%

Finally, we asked "Would you recommend your current software?" 138 people said "yes" and 32 said "no." Here's the breakdown of percent "yes" responses by package:

E-Prime (50): 92%

PsyScope X (10): 90%

PsyScope classic (16): 87.5%

DMDX (30): 86.7%

MatLab (5): 80%

Presentation (12): 63.6%

SuperLab (5): 60%

ERTS (5): 60%

SuperLab Pro (4): 50%

NESU (8): 50%

We also asked two complementary open-ended questions that aren't easy to summarize. One was, "What do you like about your current software? What are its strengths? What does it do well?" and the other was, "What do you not like about your current software? What are its weaknesses? What does it not do well (or at all)?" Considering the big hitters (E-Prime, DMDX, and PsyScope), my general impression of the flavor of the comments were:

E-Prime: Easy to learn, good support, user friendly, etc. But, some consider it expensive, thought it inflexible, and are worried about precision of timing.

DMDX: It's free, powerful, good timing, good user-support group, and good author support. Weaknesses were mostly regarding lack of intuitiveness and steep learning curve.

PsyScope: It's free, user-friendly, timing is accurate. But it can be buggy. PsyScope classic users are worried about using a legacy system. PsyScope X users worry about the transition to Intel-based Macs, but with some optimism.

The Excel file with everyone's responses is available on this page: .

Again, thanks for participating. We were thrilled to see that we actually had 187 people respond!

Best,

Vic Ferreira

Jeremy Boyd

Jeff Elman

Robert Buffington

Bob Slevc

DMDX: http://www.u.arizona.edu/~kforster/dmdx/dmdx.htm

E-Prime: http://www.pstnet.com/products/e-prime/

ERTS: http://www.erts.de/

Experiment Builder: http://www.eyelinkinfo.com/optns_eb.php

Linger: http://tedlab.mit.edu/~dr/Linger/

MatLab: http://www.mathworks.com/

MEL: (Note that MEL is the predecessor to E-Prime)

NESU: http://www.mpi.nl/world/tg/experiments/nesu.html

Presentation: http://www.neuro-bs.com/

PsyScope Classic: http://psyscope.psy.cmu.edu/

PsyScope X: http://psy.ck.sissa.it/

SuperLab (Pro): http://www.superlab.com/

UPDATE: Other relevant links (Thanks to Roberto Heredia):

http://www.tamiu.edu/~rheredia/materials.html

http://www.tamiu.edu/~rheredia/softwareresources.html

UPDATE 2: Alan Garnham suggested to me that SuperLab and SuperLab Pro are not distinct products (though I swear I have a memory of their being something called ‘SuperLab’ without ‘SuperLab Pro’!). If I get a chance, I’ll combine the two in the above analyses.

source:
http://lpl.ucsd.edu/LabPage/Lab_Blog/B1A6A7D2-0069-41E3-89E9-B3683FEEC758.html

To Err Is Human

2008-07-04T11:24:00.000-07:00

To Err Is Human

By He, Jibo

Everyone knows the old saying, “To err is human”. It is unavoidable for human being to make errors in decision making, because of our limitation of time, physical, cognitive, emotional and other resources. These limitations fail all our effortful endeavors to make perfect rational decision, and maximize our expected value. The same limitations harass the stock brokers in the Wall Street (Blodget, 2004), and doctors (Groopman, 20007). No matter how smart they are and how many tools they are armed with, they cannot escape from making bad decisions.

Everyone knows! Wait! But maybe not for classical economists. Unlike ordinary human, such as us, the economists are equipped with the most advanced mathematical tools and logic ability. They assume that human are Homo economicus, with unlimited resources in time, physical and cognitive ability, as well as stable preferences (Cassidy, 2006). The human in economists’ eyes makes rational decisions, and never err.

Economists are so smart and almighty that they have successfully attracted a great amount of followers, amongst of which include the stock brokers at Wall Street, and the educators of the doctors. They all pursue the dreams of rational decisions, which the economists depict for them. So the brokers developed all kinds of complicated tools and mathematics index, for example, NASDAQ index, K-line etc. But none of them successfully predicted the disastrous depression in 1929. And it is worthy to note that the economists, the dream designers of perfect decision, have successfully predicted the 10 economic depressions for the only 5 actual depressions in the past 30 years. Under the illusions of perfect decision makers, the educators of doctors are also temped to believe that the candidate doctors are born to know how to apply their knowledge, and they would be perfect decision makers as long as they are taught the practical aspects of patient care. Techniques on how to make decisions are ignored and seldom taught in medical schools (Groopman, 2007). However, according to the survey of Croskerry, a physician at Dartmouth General Hospital, the perfect decision makers assumed by the economists actually misdiagnose fifteen percent, or even more, of the patients. And most of the misdiagnoses are the results of errors in thinking.

The consistent failures of human in decision making call doubt about the rational approach to decision making by economists. Kahneman and Tversky developed the Prospect Theory in 1979, which marked the trend of naturalistic approaches to decision making. And some other behavioral economists, neuroeconomist adopted empirical research methods to investigate into human decision making process. These well-grounded researches call on our attentions to the limitations of human, and doubted on the unrealistic assumptions of Homo economicus with unlimited abilities and resources by classical economists. Human, as decision makers, are constraint by their limited physical, cognitive resources, unstable preferences and emotions, and the impossibility to make concise prediction of risk. Rather than perfect rationality in economics, human decision making is bounded rationality as a result of all kinds of limitations. We regret loss, so we are loss aversion; we seeks to build our self-esteem by all means, so we shows confirmatory bias in decision (Blodget, 2004); we have limited memory ability, so we rely on representative and availability heuristics in decision making. Etc.

To err is human. Economists’ escape from accepting the facts that we are limited in resources does no good to help reduce human error in decision making. We have to face up to our constraints in decision making, and get aware of and trained to avoid these limitations.

REFERENCES

Cassidy, J. (2006). Mind games: what neuroeconomics tells us about money and the brain. The New Yorker. Source:http://www.newyorker.com/archive/2006/09/18/060918fa_fact

Groopman, J. (2007). What’s the trouble? How doctors think. The New Yorker. Source: http://www.newyorker.com/reporting/2007/01/29/070129fa_fact_groopman

Blodget H. (2004). The greatest Wall Street danger of all: you. Slate.

Source: http://slate.msn.com/id/2110977/

Kahneman, D. and Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2):263-292.

Does Short-Term Memory Load Influence Visual Search? An Oculomotor Study

2008-07-02T15:09:00.000-07:00

ABSTRACT SUBMISSION DETAILS
Type of Submission:	Poster
Submission Date:	January 31, 2008
Review Status:	PENDING

Title:	Does Short-Term Memory Load Influence Visual Search? An Oculomotor Study
Subject Area:	Cognitive
Keyword:	Attention
Presenters:	Jibo He, University of Illinois, Urbana/Champaign Jason S McCarley, University of Illinois at Urbana-Champaign
Abstract:
A dual-task experiment examined the influence of STM load on visual processing and saccade targeting in visual search. Increased load altered saccade amplitudes, but did not appear to slow visual search nor to compromise foveal stimulus analysis. Results imply independence between STM maintenance and visual search.
Supporting Summary:
INTRODUCTION: A dual-task experiment examined the influence of short-term memory (STM) load on visual processing and saccade targeting in visual search. Performance of visual search under high and low memory load was compared through analysis of oculomotor data. The search task was designed to allow insight into the quality of the participants’ foveal analysis and saccade control. METHOD: Participants performed a visual search task while concurrently maintaining either a low or high memory load. The search task required participants to locate a circle (O) among a set of 35 gapped-circle (C) distractors. The memory task required participants to hold either a single alphanumeric character or six characters in STM. Each trial began with a fixation cross, followed by a memory set of either one character (low memory load) or six characters (high memory load). The visual search task was classified as coded or uncoded. In the coded condition, the Cs were oriented to face in the direction of the target, such that the participant could use distractors to guide search toward the target’s location. In the uncoded condition, distractors were oriented randomly. Comparison of performance in the coded and uncoded conditions thus provides a measure of the participants’ ability to utilize foveal analysis of distractor orientation to facilitate saccade targeting during search (Hooge & Erkelens, 1998). Memory performance was measured with a recognition test after the visual search each trial. RESULTS: Visual search was significantly more efficient under coded than uncoded conditions, as evident in changes in response times and saccade frequencies. Mathematical analysis (Wagenmakers et al., 2007) of saccade latency and accuracy data also revealed an increase in information accumulation rate for saccade target selection under coded search conditions. High memory load increased first-saccade latency each trial and led to larger saccade amplitudes, but otherwise did not hinder performance. No significant differences in memory recognition or saccade targeting accuracy across the four conditions were found. DISCUSSION: For coded search task, the benefits of cues outweighed the cost of additional cognitive processing of direction of the cues than uncoded search task. Higher STM load did not appear to slow down visual search, nor to compromise participants’ ability to guide search on the basis of distractor analysis. Results imply independence between STM maintenance and visual search.

'Lazy eye' treatment shows promise in adults

2008-07-01T12:38:00.000-07:00

'Lazy eye' treatment shows promise in adults

New data, based on a finding first reported in 2006, suggest a simple and effective therapy for amblyopia. Clinical use will depend on optometry community

source: http://www.eurekalert.org/pub_releases/2008-03/uosc-et022708.php

New evidence from a laboratory study and a pilot clinical trial confirms the promise of a simple treatment for amblyopia, or “lazy eye,” according to researchers from the U.S. and China.

The treatment was effective on 20-year-old subjects. Amblyopia was considered mostly irreversible after age eight.

Many amblyopes, especially in developing countries, are diagnosed too late for conventional treatment with an eye patch. The disorder affects about nine million people in the U.S. alone.

Results from the laboratory study will be published online the week of Mar. 3 in PNAS Early Edition.

Patients seeking treatment will need to wait for eye doctors to adopt the non-surgical procedure in their clinics, said Zhong-Lin Lu, the University of Southern California neuroscientist who led the research group.

“I would be very happy to have some clinicians use the procedure to treat patients. It will take some time for them to be convinced,” Lu said.

“We also have a lot of research to do to make the procedure better.”

In a pilot clinical trial at a Beijing hospital in 2007, 28 out of 30 patients showed dramatic gains after a 10-day course of treatment, Lu said.

“After training, they start to use both eyes. Some people got to 20/20. By clinical standards, they’re completely normal. They’re not amblyopes anymore.”

The gains averaged two to three lines on a standard eye chart. Previous studies by Lu’s group found that the improvement is long-lasting, with 90 percent of vision gain retained after at least a year.

“This is a brilliant study that addresses a very important issue,” said Dennis Levi, dean of optometry at the University of California, Berkeley. Levi was not involved in the study.

“The results have important implications for the treatment of amblyopia and possibly other clinical conditions.”

The PNAS study shows that the benefit of the training protocol – which involves a very simple visual task – goes far beyond the task itself. Amblyopes trained on just one task improved their overall vision, Lu said.

The improvement was much greater for amblyopes than for normal subjects, Lu added.

“For amblyopes, the neural wiring is messed up. Any improvement you can give to the system may have much larger impacts on the system than for normals,” he said.

The Lu group’s findings also have major theoretical implications. The assumption of incurability for amblyopia rested on the notion of “critical period”: that the visual system loses its plasticity and ability to change after a certain age.

The theory of critical period arose in part from experiments on the visual system of animals by David Hubel and Torsten Wiesel of Harvard Medical School, who shared the 1981 Nobel Prize in Medicine with Roger Sperry of Caltech.

“This is a challenge to the idea of critical period,” Lu said. “The system is much more plastic than the idea of critical period implies. The fact that we can drastically change people’s vision at age 20 says something.”

A critical period still exists for certain functions, Lu added, but it might be more limited than previously thought.

“Amblyopia is a great model to re-examine the notion of critical period,” Lu said.

The first study by Lu’s group on the plasticity of amblyopic brains was published in the journal Vision Research in 2006 and attracted wide media attention.

Since then, Lu has received hundreds of emails from adult amblyopes who had assumed they were beyond help.

Berkeley’s Levi cautioned that the clinical usefulness of perceptual learning, as Lu calls his treatment, remains a “sixty-four thousand dollar question.”

“It's clear that perceptual learning in a lab setting is effective,” Levi said. “However, ultimately it needs to be adopted by clinicians and that will probably require multi-center clinical trials.”

###

Lu is collecting patients’ names for possible future clinical trials. He can be contacted at zhonglin@usc.edu.

The researchers are also working to develop a home-based treatment program.

For patients who can travel, the Chinese hospital that hosted the pilot trial may be able to provide treatment. Contact Dr. Lijuan Liu, Beijing Xiehe Hospital, at lijuan_l@yahoo.com.cn.

The other members of Lu’s group are Chang-Bing Huang and Yifeng Zhou of the Vision Research Lab at the University of Science and Technology of China, in Hefei, Anhui province (Huang is currently a postdoc in Lu’s lab at USC).

Funding for the research came from the Chinese National Natural Science Foundation and the U.S. National Eye Institute.

ABOUT AMBLYOPIA (from PNAS)

Amblyopia affects about 3 percent of the population and cannot be rectified with glasses. People with the disorder suffer a range of symptoms: poor vision in one eye, poor depth perception, difficulty seeing three-dimensional objects, and poor motion sensitivity.

Also known as lazy eye, the disorder is caused by poor transmission of images from the eye to the brain during early childhood, leading to abnormal brain development. Lazy eye is actually a misnomer because in many cases the structure of the eye is normal.

Rapid visual memory decay in mild cognitive impairment

2008-07-01T12:35:00.001-07:00

Rapid visual memory decay in mild cognitive impairment

Source: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=547895

Zhong-Lin Lu et al. report that the rapid decay of iconic (visual short-term) memory appears to be a general characteristic of mild cognitive impairment, which often precedes the onset of Alzheimer's disease. Iconic memory typically lasts only a fraction of a second before it is either lost or stored in short-term memory, and a link between iconic memory impairment and Alzheimer's disease has been suggested. The authors tested the iconic memory of 23 young adults (average age, 20 years), 11 older adults with mild cognitive impairment (average age, 85 years), and 16 older controls (average age, 82 years). Iconic memory was characterized by using the partial report paradigm with the same visual stimuli parameters in each observation group. The researchers found that mean iconic memory duration was significantly shorter in subjects with mild cognitive impairment (0.07 s), compared with both older (0.30 s) and younger (0.34 s) controls. In a series of conventional neuropsychological tests used to assess cognitive function, the mild cognitive impairment group performed significantly worse than the older control group. In both of these groups, no significant performance differences were observed in visual and task abilities or the ability to transfer items to short-term memory. The authors suggest that testing iconic memory could be used with other measures to aid the diagnosis of Alzheimer's disease.

“Fast decay of iconic memory in observers with mild cognitive impairments” by Zhong-Lin Lu, James Neuse, Stephen Madigan, and Barbara Anne Dosher (see pages 1797-1802)

Figure 1

Native-state conformations of α-synuclein.

Figure 2

VP branching morphology of ERα^-/- mice.

Figure 3

Mast cell degranulation induced by light chains.

Figure 4

Cholera phage types.

Mental Chronometry: Subtractive and Additive Factors Method

2008-07-01T08:14:00.000-07:00

Mental Chronometry: Subtractive and Additive Factors Method

He, Jibo

(Department of Psychology, University of Illinois, Urbana/Champaign, IL, 61801, US)

Reaction time (RT) is one of the best quantitative measures in psychology, which is objective and could be compared easily among diversified tasks. RT is not only a good measure of task performance, but also can be used to probe into the mental process. Mental chronometry uses RT to confirm the existence and quantify the duration of a specific mental process. The widely use of mental chronometry benefits from the methodological breakthroughs, that is, the invention of subtractive and additive factors methods. In this reaction paper to the two materials (Johnson & Proctor, 2004; Wichens, &Holland, 1992), I will briefly summarize the two methods and comment on their usage and relative merits.

Subtractive method measures the duration of mental process by deleting a mental operation entirely from the RT task (Wichens, &Holland, 1992). Subtractive uses go-no-go task paradigm. The duration of a mental operation is the time difference of a go-response and no-go response. However, subtractive method cannot be widely used because of the dependencies of mental process with its proceeding procedures. We can use go-no-go paradigm to measure the duration of a response execution, because the deletion of response execution does not hinder other mental process as it is the end of a RT task. We cannot delete other mental process without disturbing following processes, such as perceptual encoding and response selection. The difficulty in deleting mental process should be one of the reasons why subtractive method is not widely used as the additive factors method.

Additive factors method is used to identify the existence of a proposed mental process by manipulating variables only influencing on a specific mental process. Additive factors method assumes that mental processes are executed in serial without dependencies and parallel processing. By adding variables to influencing on a target mental process, the experiment manipulation will only change this mental process without disturbing other mental process. If two manipulations influence two different mental processes, the additive effect on RT should be larger than the condition that the two manipulations operate on the same mental processes. If two operations operate on the same mental process, it will cause interactive effect on the total RT. If two operations operate on different mental processes, it will cause additive effect on the total RT. Therefore, if the time of the interactive RT task and the additive RT task differs, a specific mental process should exist, and the difference of the time for interactive RT and additive RT is the duration of this mental process.

However, despite of the success of the subtractive and additive factors methods, both of the two methods can not estimate the duration of mental process accurately enough, because the basic assumptions are not held for every RT tasks. Both of the two methods assume that mental processes are executed in serial. We can measure the time for a specific mental process by subtracting or adding/changing a mental process. However, the assumption of serial processing is not always true. Many of our mental operations are processed in parallel benefiting from the functions are allocated to independent parts of the brain or modalities of our organs. For example, we could speak while listen, smile while think etc. The estimation of RT based on the serial assumption of mental processes should over estimate the total RT for a task since some of the mental processes are executed in parallel. Another limitation of the subtractive and additive factors methods is that they assume the independence of different mental processes. That is, we can influence on only a specific mental process without disturbing other mental processes. However, actually, this assumption does not hold. The firing of a mental process usually depends on its proceeding process. For instance, we cannot carry out the process of response selection without perceptual encoding first. Therefore, if we manipulate on the stage of perceptual encoding, we do not change the duration of perceptual encoding only, we are likely to change the following response selection too. The failure of perceptual encoding will cause us fail to come up with relevant response too, which will definitely lengthen the duration of response selection.

To sum up, the invention of subtractive and additive factors methods contribute to mental chronometry, which can identify a mental process and measure its duration. But we should also be aware of its limitation in assuming serial processing and independent of different mental processes.

REFERENCES

Johnson, Addie & Robert W. Proctor. "Information Processing and the Study of Attention (excerpt)." Attention: theory and practice. Sage, 2004. 32-37.

Wickens, C.D., Hollands, J. G. (1992). Engineering Psychology and Human Performance (2^nd Edition). Published by Harper Collins. Pages: 335-339.

Human Factors/Ergonomics: Using Psychology to Make a Better and Safer World

2008-07-01T08:12:00.000-07:00

Human Factors/Ergonomics: Using Psychology to Make a Better and Safer World

by Michael S. Wogalter and Wendy A. Rogers - North Carolina State University, Georgia Institute of Technology

Fields of Psychology

What is Human Factors/Ergonomics (HF/E), and why does the field have two names? The field of HF/E is the scientific discipline that attempts to find the best ways to design products, equipment, and systems so that people are maximally productive, satisfied, and safe. Historically, the term human factors has been used in the United States, and the term ergonomics has been used in Europe. Other terms used to describe the field are engineering psychology and applied experimental psychology. Whatever the name, HF/E is the science that brings together psychology and engineering design.
The field of HF/E is multidisciplinary and benefits from the input of experts from domains such as psychology, engineering, computer science, biomechanics, medicine, and others. Frequently, the HF/E professional plays the role of mediator between divergent interests advocating for the human point of view in the design of products, equipment, and systems by championing designs that make maximal use of the magnificent abilities that people possess and limiting the use of tasks where people could make errors.
Early contributions to the establishment of HF/E included the analysis of time and motion of people doing work, and determining human capabilities and limitations in relation to job demands. Most people credit the beginning of the field with the military during World War II. Pilots were flying their airplanes into the ground, and eventually psychologists were called in to find out why. We'd call it "human error" today, and part of the reason for the aircraft crashes was the lack of standardization between different aircraft models. The growing complexity of military hardware during this time period was revealing for the first time in history that even highly selected individuals who were given extensive training could not do the tasks that they needed to do. Pilots were not able to control their aircraft under stressful emergencies. The machines outstripped people's capabilities to use them. Investigations revealed that pilots had certain expectations of how things should work (for example, the location of the landing gear control and how to activate it), and these were frequently violated by aircraft designers (who frequently knew very little about people's abilities and limitations). Before WWII, it was assumed that people could eventually learn whatever they were given if they were trained properly. Since WWII, the field has blossomed as is evident from the examples provided in the next section.

Examples of Human Factors/Ergonomics Applications

Most people, if they even know the term ergonomics, might recognize it as dealing with chairs or possibly automotive displays. While the design of chairs and automobiles is within the purview of HF/E, the field is much broader than that. In fact, many HF/E professionals believe that nearly all aspects of daily activities are within the domain of HF/E. The field deals with the interface between people and things, whether it be a dial on a car dashboard or a control on a stove top. The fundamental philosophy of HF/E is that all products, equipment, and systems are ultimately made for people and should reflect the goals of user satisfaction, safety, and usability. Table 1 lists some examples of the type of issues on which HF/E specialists focus.
Two specific examples might serve to illustrate HF/E considerations. The first is that as commonplace as automated teller machines (ATM) have become, many older adults do not use them even though they could benefit from their convenience (see Figure 1). The goal of an HF/E specialist would be to ensure that the design of the machine was easy to use (including the design of the buttons, the wording, the spatial layout and the sequencing of the displays, etc.). Moreover, an HF/E person might suggest employing an outreach training program to assist first-time users. The ultimate HF/E solution would be, however, to make the technology so obvious that training is not necessary. Many people can't program a VCR. You might know a statistics program that could be made easier to use and understand. These are the sort of systems that could benefit from HF/E considerations.
The second example concerns pictorial symbols. Increasingly, symbols are being used to convey concepts to people who do not understand the primary language of the locale, and this is becoming increasingly important with people and companies involved with international travel and trade. In Figure 2, the pictorial on the left is from an actual sign on automatic doors like you might see at hospitals and airports. What does it mean? The slash obscures a critical feature of the underlying symbol. The pictorial could be interpreted as "Do not stand" or the opposite, "Do not walk." The interpretation the door manufacturer wanted to convey is the first one, because the doors sometimes close unexpectedly. Can you see that the pictorial symbol could be interpreted as the opposite of its intended meaning? The alternative interpretation was apparently missed by the designer. This is called a critical confusion because the meaning can create a hazard. Fortunately, most people probably do not have the chance to misinterpret this symbol. This is because whenever a person walks up to the door, the doors slide to the side, out of the way. The problem is (a) that the sensors sometimes do not pick up people standing at the threshold, and (b) that these people haven't seen the sign. People have been knocked to the ground by automatic doors that have closed unexpectedly, and for some fragile individuals that event has produced injury. An HF/E analyst would first want to "design out" the hazard (i.e., so it can't close on anyone) using, for example, better sensors and more reliable and better designed components and systems. If you can't design out the hazard, then at least you ought to guard against the hazard contacting and injuring people. When warnings are used, they ought to be designed so target audiences grasp the intended message quickly and readily with little time and effort.

Careers in HF/E

There are a wide range of opportunities in the field of HF/E:

-- aerospace systems
-- accident analysis
-- computer software and hardware design
-- communications technology
-- educational technology
-- forensic psychology
-- government research laboratories
(Air Force, Army, Navy, NASA)
-- graphics and information design
-- health and medical technology design
-- systems management
-- training development
-- university faculty
-- usability analysis
-- virtual reality
-- workplace design

HF/E is an area in which one does not necessarily need a PhD or even a master's degree to work in the field (although most human factors psychologists with bachelor's degrees have had some relevant graduate school experience). A recent salary survey of HFES members (Lovvoll, 1997)--using data from only those people reporting that their last degree was from a psychology department--shows that a decent living may be earned at all education levels, although it must be noted that the totals cut across all years of experience (see Table 2).
Another method of assessing salaries in the field is to group the job categories as shown in Table 3.

Learning More About Human Factors/Ergonomics

Many students have not heard of the field of HF/E, in part because there is often not a course in the curriculum, it is usually not covered in other courses, and many psychology professors do not know enough about it to inform their students (Martin & Wogalter, 1997). However, there are several organizations that encourage student participation and membership.
American Psychological Association (APA). Division 21 of APA is the Division of Applied Experimental and Engineering Psychology. For more information about becoming a student member of the division, contact Cathy Gaddy at cgaddy@aaas.org. You may also access information about APA's Division 21 (Applied Experimental and Engineering Psychology) on the Internet: http://www.apa.org/about/division.html#d21.
Human Factors and Ergonomics Society (HFES). HFES is the largest U.S. organization in the field with approximately 5,000 members. Nearly half of the members are psychologists, with the other members coming from fields such as engineering, computer science, system design, and others. For more information about becoming a student member of HFES, contact Diane de Mailly at hfesdm@aol.com or call (310) 394-1811. The HFES home page may be found at http://www.hfes.org. From this site you can download the complete listing of HF/E graduate programs in the U.S. and Canada. They also have a year-round job placement service.
Another way to learn more about the field of HF/E is to head for the library and browse through a textbook on the topic. You will surely be amazed by the range of topics covered. Some of the standard textbooks in the field are:

Proctor, R. W., & Van Zandt, T. (1994). Human factors in simple and complex systems. Boston: Allyn and Bacon.
Salvendy, G. (1997). Handbook of human factors and ergonomics. New York: Wiley and Sons.
Sanders, M. S., & McCormick, E. J. (1993). Human factors in engineering and design (7th ed.). New York: McGraw-Hill.
Wickens, C. D. (1992). Engineering psychology and human performance. New York: HarperCollins.

The field of HF/E is exciting, challenging, and important. Specializing in this field will enable you to get involved in the development of the future as well as to help individuals interact safely and effectively with today's technology. Although things will, undoubtedly, get more complex, potentially they can be made easier to use, helping to benefit our lives.

References

Lovvoll, D. (1997). Salary survey. HFES Bulletin, 40(5), 1-3.

Martin, D. W., & Wogalter, M. S. (1997). The exposure of undergraduate students to human factors/ergonomics instruction. Proceedings of the 41st Annual Meeting of the Human Factors and Ergonomics Society (pp. 470-473). Santa Monica, CA: HFES.

This article is part of a continuing series on the various fields of psychology and the careers available within those fields.
Correspondence concerning this article should be addressed to Michael S. Wogalter, Department of Psychology, North Carolina State University, 640 Poe Hall, Campus Box 7801, Raleigh, NC 27695-7801. Electronic mail be sent to wogalter@ncsu.edu.

ABOUT THE AUTHORS: Michael S. Wogalter, PhD, is an associate professor of psychology at North Carolina State University, Raleigh, NC. Before coming to NCSU, he held faculty appointments at Rensselaer Polytechnic Institute and the University of Richmond. He received his PhD in human factors psychology from Rice University, an MA in human experimental psychology from the University of South Florida, and a BA in psychology from the University of Virginia.
He teaches graduate-level courses in human-computer interaction and risk communication, and undergraduate courses in ergonomics. Most of his research focuses on hazard perception, warnings, complex visual and auditory displays, and human information processing. An active member of the Human Factors and Ergonomics Society, he is currently secretary-treasurer and is a member of the Executive Council. He holds membership in several other professional organizations including APA, APS, the Ergonomics Society, and Sigma Xi. He is also on the editorial boards of the journals Human Factors, Ergonomics, Psychology & Marketing, and Occupational Ergonomics.
Wendy Rogers, PhD, is currently an associate professor in the School of Psychology at the Georgia Institute of Technology, where she is a member of the engineering psychology program. She received her BA from Southeastern Massachusetts University and her MS (1989) and PhD (1991) from Georgia Institute of Technology.
Her research interests include skill acquisition, human factors, training, attention, automaticity, individual differences, and cognitive aging. Her research is currently funded by the National Institutes of Health (National Institute on Aging) as part of the Center for Applied Cognitive Research on Aging. She serves on the editorial boards of Psychology and Aging, Experimental Aging Research, Ergonomics in Design, and Human Factors. She is immediate past president fo the Division of Appllied Experimental and Engineering Psychology (APA's Division 21). She is also the chair of Student Affairs and member of the Executive Council of the Human Factors and Ergonomics Society.

____________________________________________

Fall 1998 issue of Eye on Psi Chi (Vol. 3, No. 1, pp. 23-26), published by Psi Chi, The National Honor Society in Psychology (Chattanooga, TN). Copyright, 1998, Psi Chi, The National Honor Society in Psychology. All rights reserved.

Reading Response to Giudice (2008)

2008-06-27T05:01:00.000-07:00

Reading Response to Giudice (2008)

Abstract

Our lab’s research in China does not show gender differences in insecure attachment patterns. We believe that cultural differences between Chinese and Western societies may help to explain this phenomenon. Mating and parenting circumstances in China do not allow males to adopt a zero-investment strategy. In addition, attachment styles are transmitted across generations and last for the whole lifespan. Here, we argue that the influence of mating and parenting on the well-developed attachment patterns in childhood is relatively small.

Full Text

In section 6 of the target article, Giudice (2008) reported a significant gender difference in insecure attachment: Whereas females were more likely to be ambivalent, males were more likely to be avoidant. However, gender differences have rarely been reported in prior studies (Crittenden 2000; Schmitt 2003). We believe a cross-cultural perspective may help to reconcile this apparent contradiction. In particular, attachment studies in Asian cultural samples, such as China, should be taken into account for a more comprehensive analysis.

Our recent studies in China suggest that there are no gender difference in insecure attachment styles (Li 2005; Li & Du 2005; Li & Kato 2006; Li et al 2006a; b; c; 2007; in press; Wang & Li in press). Table 1 summarizes our results across a variety of demographic groups (middle school students, undergraduates, company employees, inpatients and mothers). Pearson Chi-square tests showed that neither sample had significantly different attachment patterns between males and females. We also note that in the mother sample, there were more avoidant females than anxious/ambivalent ones.

Giudice argues that males and females strive to maximize their reproduction of genes. Gender differences in mating, reproduction and parenting efforts lead to diverse attachment styles: insecure females tend to be anxious/ ambivalent, while insecure males tend to be avoidant (sect 6.3.1, para 5). However, reproductive investment alone does not account for the total cost of reproduction and parenting. Females have the privilege to select the most suitable male who help child-rearing (Clutton-Brock 1991). Transitional China since the 1980’s is one such example where parental investment is significantly higher than that in Western nations (Wang & Ollendick 2001). During the 1980’s, the Chinese government began to implement family planning (“one child”) policy to control population growth, which profoundly changed the demographic as well as cultural values in Chinese society (Arnold & Liu 1986; Xu et al. 2007). First, this policy does not allow males to have multiple children, which requires males to invest in the quality of offspring, rather than quantity (Wang & Ollendick 2001). This greatly reduces the likelihood of males taking a zero-parenting strategy. Secondly, the traditional son preference was even exaggerated and the “one child” policy often became a “one son” policy, creating an unbalanced gender ratio (Chan et al 2006). In this case, males have to compete for limited number of females. Finally, the woman’s rights movement has been widespread since the communist liberation in the early 1950s when the socio-economic status of women improved considerably. Recent studies have shown that during family purchase decisions, females now play an equal- status role as males (Dong & Li 2007). Thus, for contemporary Chinese females, although they cannot shift the balance between parenting and mating effort as easily as men, they do not need to develop an anxious/ambivalent attachment strategy to invite paternal investment (Archer & Mehdikhani, 2000).

A gender difference in insecure attachment could also be explained from the perspective of intergeneration transmission. According to Bowlby (1980), people develop their mental representations of the environment and significant others based on their experience with parents or other caregivers. Bowlby labeled this mental representation as an Internal Working Model (IWM). Once formed, IWMs tend to remain stable for the whole lifespan (Hu & Meng 2003). The stability of IWM produces similar attachment patterns from childhood to adulthood. This argument is supported by cross-sectional and longitudinal studies (Durrett et al. 1984; Brennan et al. 1998; Fraley & Spieker 2003; Hu & Meng 2003; Nakao & Kato 2003; Li & Kato 2006). Li (2006) summarized the distribution of attachment styles in infants and adults in Chinese and American samples. He found that the proportion of each attachment style was similar for both infants and adults. This result suggests that the attachment style may remain relatively stable across the lifespan. Longitudinal studies on attachment development also support the stability of attachment styles within generations (Shemmings 2006; Emery et al. 2008). The stability of attachment from infancy to adulthood suggests that the influence of mate selection and sex competition in early adulthood on attachment patterns is trivial. This may well explain the lack of gender difference in insecure attachment in Chinese samples.

Acknowledgement

This study is supported by National Natural Science Foundation of China (No. ). We thank Adam Pearson and Mark Sheskin for useful comments and suggestions.

References

Archer, J. & Mehdikhani, M. (2000) Strategic pluralism: men and women start from a different point. Behavioural and Brain Sciences 23: 620–621.

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Li, T. & Kato, K. (2006) Measuring adult attachment: validation of ECR in Chinese sample. Acta Psychologica Sinica 38: 399-406.

Li, T., He, J., Guo, X. & Lu, X. (2006) Adult attachment and social support of self-learning students. Chinese Journal of Behavioral Medical Science 15: 1019-1020.

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rfLogger: A Logging Browser and Data Processing Method SurLogger: A Logging Browser and Data Processing Method In Web-based Studies

2008-06-26T06:25:00.000-07:00

SurfLogger: A Logging Browser and Data Processing Method

In Web-based Studies

Jibo, He

(Department of Psychology, University of Illinois, Urbana/Champaign, IL, 61801, USA)

Jibohe2@uiuc.edu

ABSTRACT

Despite of the increasing interest in web-based studies, researchers lack a convenient tool for data collection. The existing tools have constraints in the data they could collect or in the availabilities to the study environment. SurfLogger, described in this paper, is an automated data logging tool, free, open-source, cross-platform, and easy to modify. SurfLogger is expected to meet the increasing needs of web-based studies.

Keywords

SurfLogger, browser, instrumentation, Web, WWW, Python

INTRUDCTION

In this information age, the World Wide Web (WWW) is the most fast developing information resources (Eighmey, & McCord, 1998). The booming and infinite opportunities accompanying WWW win interests from vast of communities, including web site designers, user interface researcher, cognitive psychologists, E-commence businessman, as well as many others who are interested in characterizing how users interact with web browser and gain information from varied designs of web pages (Eighmey, & McCord, 1998; Wang, Jing, He, and Yang, 2007; Reeder, Pirolli, and Card, 2000).

Despite of the wide interest in web-based researches, there are still no full-fledged and easily accessible tools to collect user log and browser interactive data. Current data collection methods are far from convenient. Some studies collected data from servers or proxies, which is not only bothersome and expensive to configure the servers or proxies, but also cannot capture users’ interaction with the browser and users experience (Pitkow, 1998). Another substitutive solution is to use videotaped data, usually providing more comprehensive users information (Byrne, John, Wehrle, and Crow, 1999). But coding videotaped data is too consuming in time and labor, and accuracies of coding cannot be guaranteed as perfect. Reeder, Pirolli and Card (2000) did a wonderful job to create WebLogger for data collection in web-based studies. Sadly, WebLogger was written in Visual Basics and depends on Microsoft’s Internet Explorer 6.0 (IE) (Reeder, Pirolli and Card, 2000, 2001). WebLogger cannot be used after IE updating to the latest version of IE 7.0, and neither in Linux and Mac Operating System.

To meet the needs for such a tool in web-based research, I have developed SurfLogger, which collects users’ interaction data with both the web and the browser. SurfLogger is an automated data logging tool, free, open-source, and cross-platform (can be used in Windows, Linux, Mac and many other operating systems), and easy to modify. SurfLogger does not depend on other software, such as Internet Explorer, and does not need installation.

SURFLOGGER

Description

SurfLogger is written in Python, a scripting language, and the GUI (Graphical User Interface) is created with wxPython, which is a Python bundle of wxWidget. SurfLogger can record a variety of user actions with the web pages and the browsers. SurfLogger produces two files, logfile.txt and urlfile.txt. Logfile.txt stores action IDs (natural numbers assigned to each action, used to track the record to the responding actions), the time for each actions, interaction with the browsers (such as, clicking on the Back, Forward, Home, etc. buttons), and mouse coordination when clicking. The time record could be used to compute the time of completion for each task. The number of button press on the browsers could be used as a measure of effort in carrying out the task. SurfLogger also captures the images of each screen when the web page refreshes. Marking the mouse coordination on the screen captures could tell us what links the users clicked at. Urlfile.txt stores action IDs and URLs (Uniform Resource Locator). Action IDs are used to synchronize the record in logfile.txt and urlfile.txt. URL record is stored in a separate file because the abundant information it can provides. I will give an example about how to extract information from urlfile.txt in case study section of this paper.

Log File Format

The records are stored in two files, the logfile.txt and urlfile.txt. Every variable takes up one line, which begins with variable name, followed by variable value. The variable name is self-explanatory. In the logfile.txt (see Figure 1), a record set for one action includes the mouse coordination, browser action (clicking on Back, Forward Home or other button on the browser), time for the action, and action ID. In the urlfile.txt (see Figure 2), each set of record contains action ID and URL. Adjacent record sets are separated by a blank line. The format of log file is designed to be human-readable and easily read for analysis software.

ID: 3

TIME: 04 Apr 2008 11:50:04

Mouse Coordination: 125 52

Browser Action: Back

Figure 1. Records in logfile.txt

ID： 3

URL：http://www.citeulike.org/user/testMaterial/article/2624476

Figure 2. Records in urlfile.txt

Case study

Wednesday, August 30, 2006 3:06:54 PM

http://msra-vss50-b/igroup2/search.aspx?q=Disney#g,14,1,-1

RELATED WORK

Although a large number of researchers are interested in web-based study, there are not many well-developed tools. Reeder, Pirolli and Card (2000) developed a great tool called WebLogger, which can collect extensive data, including user input from keyboard and mouse, user actions on the interface elements of IE, and URLs. Choo, Detlor and Turnbull (1999) also developed a similar tool named WebTracker. But both WebLogger and WebTracker can be used only in Windows platform, and relied on the explorer software of IE or Netscape’s Navigator. After the explorers upgraded, the codes of WebLogger and WebTracker have to update too, in order to function normally.

The LogSquare, sold by ManGold Inc., can record keyboard entries, web page actions, mouse clicks, user comments and coding etc. However, despite the price of LogSquare, it can not offer researchers the flexibilities in data collecting and analysis. IT companies also wrote some tools for their usability test. But these tools are usually not full-fledged, and not available to the common researchers (Wang, Jing, He, and Yang, 2007).

Besides the above mentioned automated logging tools, researchers also used some compensatory recording methods. Catledge and Pitkow (1995) studies user interface by capturing client-side browsing event with NCSA’s XMosaic. Byrne and his colleague (1999) used videotape recording to study web-browsing behaviors. However, these methods are not only time-consuming, but also provided limited data about users’ behaviors.

CONCLUSION

ACKNOWLEDGEMENTS

I appreciate Dr. Wai-Tat Fu of University of Illinois, Urbana/Champaign for suggestion and revision. I would also like to thank Jeff Grimmett and Michael Urman for sharing their work, thank Robin Dunn and other members of the Python/wxPython communities for information and help.

REFERENCES

Byrne, M.D., John, B.E., Wehrle, N.S., and Crow, D.C. (1999). The Tangled Web We Wove: A Taskonomy of WWW Use. In Proceedings of CHI '99 (Pittsburgh PA, May, 1999), ACM Press, 544-551.

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Catledge, L.D. and Pitkow, J.E. (1995). Characterizing browsing strategies in the World Wide Web. In Computer Networks and ISDN Systems 27: 1065-1073.

Choo, C.W., Detlor, B., and Turnbull, D. Working. (1999). The web: An Empirical Model of Web Use. HICSS’33 (Hawaii International Conference on Systems Science). Available at http://choo.fis.utoronto.ca/FIS/ResPub/HICSS/

Eighmey, J., & McCord, L. (1998). Adding value in the information age: Uses and gratifications of sites on the World Wide Web, Journal of Business Research
41(3):187-194.

Igroup: http://igroup.msra.cn/

Jing, F. Wang, C., Yao, Y. , Deng, K. , Zhang, L., & Ma, W.Y. (2006). IGroup: A web image search engine with semantic clustering of search results. International Multimedia Conference: Proceedings of the 14th annual ACM international conference on Multimedia, Santa Barbara, CA, USA.

LogSquare. http://www.mangold.de/LogSquare.16.0.html.

Pitkow, J.E. (1998). Summary of WWW Characterizations. In Proceedings of the Seventh International WWW Conference, Brisbane, Australia. Also available at http://www7.scu.edu.au/programme/fullpapers/1877/com1877.htm.

Reeder, R., Pirolli, P., and Card, S. (2001). WebEyeMapper and Weblogger: Tools for analyzing eye tracking data collected in web-use studies.

Reeder, R., Pirolli, P., and Card, S. (2000). WebLogger: A data collection tool for web-use studies. Technical report number UIR-R-2000-06 online at http://www.parc.xerox.com/istl/projects/uir/pubs/default.html.

Wang, S., Jing, F., He, J., Yang, J. (2007), IGroup: Presenting Web Image Search Results in Semantic Clusters. Proc. SIG CHI’2007, ACM Press.

Python. http://www.python.org/

wxPython. http://www.wxpython.org/

use of this blog

2008-06-26T06:16:00.000-07:00

This blog will be used to communicate with my colleague and spread the knowledge of Human Factors and Engineering Psychology. I will publish my researches and writings in HF and Engineering Psychology at this blog. Some of my other training and interest, such as, developmental psychology, attachment, computer science, artificial intelligence, driving, economics and music, may also appear in this site.
I am very happy to discuss with you if you are interested in my writings. Please correspond to hejibo@gmail.com. Thank you for your interest!

about this blogger: He, Jibo

2008-06-26T05:44:00.000-07:00

Mr. Jibo He
Graduate Research Assistant

jibohe2@illinois.edu
217-244-4461 (phone)
217-244-8647 (fax)

Address
University of Illinois
3414 Beckman Institute
405 N. Mathews
Urbana, IL 61801

Education
B.S., Department of Psychology, Peking University, 2007
B.S., China Center for Economic Research, Peking University, 2007

Special Interests
Visual cognition, attention, usability, user centered design, human-machine interaction

Selected Articles

Jing, F., Wang, S., He, J., Du, Q., Zhang, L. (2007), Long Query Suggestion List: Prioritized or Organized. Proc. 12th HCI International.

Wang, S., Jing, F., He, J., Yang, J. (2007), IGroup: Presenting Web Image Search Results in Semantic Clusters. Proc. SIG CHI’2007, ACM Press.

Human Factors and Engineering Psychology

Cognitive load modulates attentional capture by color singletons during effortful visual search☆

import .edf files in batch into DataViewer

Eprime Learning materials

SurfLogger: A Logging Browser and Data Processing Method

SurfLogger

####Matlab Programming Notes####

Help for Psychtoolbox

Help for MATLAB

A Brief History ofHuman Computer Interaction Technology

---------Making Gabor-----Matlab------

Contents

Making a Gabor

plotting 1D Gaussian

create 2D Gaussian

view 2D Gaussian

Using the Gaussian filter to filter other images

create 1-D sinusoid

Create 1-D sinusoidal grating

Create 2-D sinusoid

Create a Gabor

Psychtoolbox Wiki : PsychtoolboxTutorial

Psychtoolbox-3 Tutorial

Strengths of Matlab & Psychtoolbox

Psychtoolbox Wiki : PsychtoolboxTutorial

Psychtoolbox-3 Tutorial

Strengths of Matlab & Psychtoolbox

Behavioral Experiment Software Survey Results

To Err Is Human

Does Short-Term Memory Load Influence Visual Search? An Oculomotor Study

'Lazy eye' treatment shows promise in adults

'Lazy eye' treatment shows promise in adults

New data, based on a finding first reported in 2006, suggest a simple and effective therapy for amblyopia. Clinical use will depend on optometry community

Rapid visual memory decay in mild cognitive impairment

Mental Chronometry: Subtractive and Additive Factors Method

Human Factors/Ergonomics: Using Psychology to Make a Better and Safer World

Reading Response to Giudice (2008)

rfLogger: A Logging Browser and Data Processing Method SurLogger: A Logging Browser and Data Processing Method In Web-based Studies

use of this blog

about this blogger: He, Jibo

A Brief History of

Human Computer Interaction Technology