Get documents about "Feedback Experiment V32
Document Sample
Experimenter: Enter condition below, press Enter,
note instructions, and click RUN
Embedded instructions?
Condition No
6G
Instructions Task
1A UConn
You can double cl
Code RLBHVEFF case presentation
If you choose to Choose option for program.
email results, the decision making
program will open procedure
your default email
program and
generate a New Bonus feedback (New or Old)
message to Tom
Stewart with the No Email data (Yes or No)?
data attached.
Machine number automatically read from
TestPC ComputerID.xls
Run
Delete Output Use this button to delete output after a test
run. Caution: It will delete all output on
the file. It will copy a new
FeedbackExperimentOutput.xls from a
directory named "Backup Files".
Yes Startup value of judgment-before-decision control
For task choice on Intro sheet
Albany
UConn
ESB19
For bonus feedback
New
Old
For Yes/No
Yes
No
For testing
Yes Skip MCPL instructions (disabled)
Yes Skip MCPL cases
No Skip Decision instructions
No Skip Decision cases
No Skip Final questions
You can double click on the word "Set" in
case presentation to restart or end
program.
Conditions, reduced feedback
proportions, instructions are all now
determined by the Condition Table
worksheet
For testing purposes, set number of cases and blocks below
Number of blocks for MCPL (max 3)
Cases presented per block for MCPL (max 25)*
20 Number of blocks for decision task (max 20)
25 Cases presented per block for decision task (max 25)
*Note that you won't get weight feedback for fewer than 25 MCPL cases.
Why is the mean higher for RM, and does it matter? I don't think it matters
Implement embedded instructions for UConn
Randomize case presentation within block, but record output in constant order.
Put all variable instructions in forms so we need Add a button to delete output file when testing
only one set of paper instructions.
Reorganize output file so it produces more statistics and combines output in a
format that is easier to input to SPSS.
Use Wbprotect and Elise
Subjects.
Change start and stop time storage to Output so that block names are not needed
Implement uncertainty conditions
On subjective report form
Add all new forms (to disable X on forms): (Done on all forms I know of) clear the text box if subjec
weight that is out of range
Private Sub UserForm_QueryClose(Cancel As Integer, CloseMode As Integer) resetting is detected as ch
'Stop a user from closing a UserForm via the X: message is displayed.) In
'http://www.ozgrid.com/VBA/MiscVBA.htm button is disabled until da
If CloseMode = 0 Then
Cancel = True I disabled all checking on
MsgBox "The X in the upper right corner has been disabled", vbCritical weights because of a prob
End If locking up due to type mis
End Sub the problem
elete output file when testing
Use Wbprotect and Elise's "Visuals" to hide all worksheet operations from
Subjects.
On subjective report form, text box, it should
clear the text box if subject enters a subjective
weight that is out of range. (I tried, but couldn't
resetting is detected as change and then error
message is displayed.) In version 16, the Next
button is disabled until data are entered.
I disabled all checking on the subjective
weights because of a problem with machine
locking up due to type mismatch. This solved
the problem
Version
V10
V11
V12
V13
V13a
V14
V15
V16
V17
V17a
V18
V19
V20
V21
V22
V23
V24
V24a
V25
V26
V27
V27a
V27b
V28
V29
V30
V30a
V31
V30p
V30-2
V32
Next version to do
Added form for displaying cognitive feedback
Added email capability using routing slip
Added machine number and version number to output worksheet
Increased rows for ID's on Output workbook to 10.
Tested and improved Reduced feedback algorithm (see ReducedFBAlgorithmTest.xls
Reduced FB algorithm now spans 3 blocks
Added code to disable X close icon on forms
Added blank worksheets to Task and Output files
Added code to activate blank spreadsheets for background
Added code to make worksheets invisible, but I think that is not necessary
Revised instructions
Major failure prior to DAPS meeting. Eventually ran file through Office 97 maching and saved it. That seemed to
correct the problem
Change to customs officer task
Added condition code to welcome sheet. Preparing for using printed instructions.
Revised MCPL case presentation
Revised decision case presentation
Revised cue help
Revised decision scoring reminder
Revised subjective report questions for only decision cases
Revised output file to accept data from new subjective report form
Increased Decision blocks to 10 (many ranges have to be named on Experiment, Output, and Task data books
Added stopping rule and bonus payment calculation
Restored question about cutoff when we decided to force judgments before decisions
Cases and blocks can be easily changed
Defined worksheet objects
Generally cleaned up program - no changes from V12 should be visible to user
Corrected some problems with version 13
Replaced Excel 2000 Round function with old Worksheet function that will work in Excel 97
Change performance cutoff to 1750 based on simulations with new decision task data (20 blocks)
Correct error that caused score to be displayed wrong if subject stopped due to meeting performance standard
Removed all need to change program when program version changes
Adjusted forms that show points to work with 5 digits scores
Tested cue and decision display for 20 blocks. Seems ok. I did only one case per block.
Added storage of score and pctcorrect by block
Performance criterion was tested based on total score. I corrected to base it on block score.
This was used for group test on 9-30-05. Stopping threshold was set high so everyone had to do 20 blocks
Requires ComputerID.xls file with computer number in cell A1 of sheet1. Don't enter computer number on Intro
sheet.
Subject number is now automatically assigned as Year-Month-Day-Hour-Minute-MachineMachineNumber
The proportion of feedback in reduced feedback (Condition 1C) is now controlled by the
"ProportionPositive" cell (B4) on the Parameters sheet. This should be set equal to the mean proportion
of positive decisions made by the subjects in the partial feedback (1B) condition. NEEDS TESTING
First version used for actual experiment. Messages to testers removed
Perfomance cutoff disabled by setting at 2500 per block. "Good score" set at 2000 per block. This determines
the dollars per point.
Commented out the code on frmSubjectReport Weight text boxes that was meant to reject out of range entries.
We have been having problems with crashing when some (unknown) data was entered on that text box. Hope
this fixes it
Added "On Error Resume Next" to further try to prevent crashing when text is entered.
Subject number is now assigned after subject presses begin, rather than when the program starts. This makes
the time that is part of the subject number represent the actual start time, rather than the time the program was
started.
Corrected an error in clearing decision data from previous subject. 18 responses from block 20 were not cleared.
This inflated the scores up to block 20, and then gave a very small score for block 20 as the uncleared responses
were overwritten. We should delete the first 6 subjects.
Set up ProportionPositive = .5, based on average proportion of positive decisions by 12 people in the Partial
Feedback condition (.508). Also corrected program so this actually works. Reduced feedback changes
described above for version 16 got lost somewhere.
Enabled OK button for demographics and subjective report. Subjects do not have to enter anything on these
pages.
Added code so that program will start if TaskData and Ouput workbooks are already open.
Implemented base rate conditions
11/14/2005
Now runs Albany or UConn task (UConn is casereduced feedback condition.exactly this way onafter running
Enabled different proportion feedback cases for sensitive. Must be entered This must be set Intro sheet)
subjects in the corresponding Partial feedback condition and computing the mean number of Yes decisions
made.
This version requires the judgment slider to be moved before OK button is enabled.
11/20/2005
This provides for a new calculation of the bonus, taking into account the accuracy of judgment.
External bar gifs no longer needed. Bar images are stored on a form internally
Some unused forms deleted and stored.
MCPL now works with UConn task
TaskData file no longer needs to have data in every block named. Additional data for MCPL and decision can be
added without naming blocks -- however, the time data output may need to be revised if blocks are added.
2/1/2006
Computer ID is now part of name of output file. The file named FeedbackExperimentOutputTestPC.xls must be
in the same directory as this file. The name will be changed the first time the program is run.
Implemented the different payoff conditions. They are controlled by the "Values" sheet
Removed the table of conditions from the Intro sheet. It was becoming too large as additional conditions were
added. It also might become confusing having the properties of conditions stored in two different places. All the
controls for conditons are now on the "Condition Table" worksheet. The range is named "ConditionCodeTable."
Added column to Conditon Table so that UConn can use different base rates.
Table added to "Parameters" sheet converts base rates to thresholds. Thresholds must be determined manually.
If you try to use a base rate that is not in this table, you will get an error message and the program will terminate.
Changed condition table to use low base rate for experiment 3. Previously we had planned to use moderate
base rate, but that seems too easy.
Corrected the page number on page 2 of the subjective report.
Added validation so incorrect conditions cannot be entered. Also condition can be optionally chosen from drop
down list.
Added code to check status of condition and display status if present. Stops program to prevent attempts to run
invalid conditions.
Values for the VN and VP conditions were changed.
2/8/2006
Only formatting changes were made so that the program will look ok on the screens at the CIFA lab (ESB19).
TaskID's "Albany" and "Uconn" run on XGA (1024x778) 96 DPI.
"ESB19" TaskID runs on 1400x1050, 120 DPI screen. This is the same task as "Albany" but the forms are set to
cover the larger screen.
It should be possible to set up scaling for all screen sizes, but I haven't done that.
Versions 22 and later were created with Excel 2003. In order to run them on Excel 97, the program must be
compiled on a machine running Excel 97:
1. Press Alt-F11 to open the Visual Basic window.
2. Click Debug/Compile VBAProject.
3. Pres Alt-Q to close and return to Excel.
4. Save the Excel file.
2/24/2006
An extra column was added to the condition table recognizing that UConn is going to have different proportions
for reduced feedback. If task ID is "Uconn," the proportion is read from this column
Anyone in condition 2C prior to this version did not receive any feedback.
People in condition 1C at UConn received feedback based on a proportion of .5, which was based on Albany
subjects. will now terminateshould have aattempt to run.35
Program UConn subjects if there is an proportion of a reduced feedback condition when the proportion
positive is 0.
3-27-06 One subject actually met the vestigial 2500 point performance cutoff on two blocks. I have raised it to
3000 so this will not happen. If the performance cutoff is implemented in the future, frmStopDecisionTask will
have to be modified so it will work.
5/6/2006
This version will run from a flash drive (changes made to file names because ChDir will not change drives.
5/8/2006
Added a cell on the intro sheet to indicate use of USB drive. Entering Yes will cause output after each block to be
backed up on the c drive instead of the USBdrive, eliminating delays due to slow writing on USB drive. The
backup file is c:/My Documents/FeedbackOutputBackup.xls . It is deleted after the output is saved to the flash
drive.
This works for the computers set up in Draper 023. It may not work on other computers. For Tom Stewart's
laptop, for example, another method, involving "environ," is needed. This method is commented out in the code.
Useful website for finding the My Documents directory:
http://blogs.officezealot.com/charles/archive/2004/12/10/3574.aspx
Useful website for file operations:
http://www.exceltip.com/st/Basic_file_and_folder_examples_using_VBA_in_Microsoft_Excel/443.html
Started 5-9-2006
Adding error handling to Case presentation. Displays error message, creates error log, provides restarting
options. This has not been tested on a real error.
This version sets calculation to automatic. Program will not run if calculation is not automatic.
Enabled double click on "Set" label to provide restart options (same as error options).
Added button to delete output file after testing.
6/14/2006
Activates the reduced feedback conditions for Albany based on data gathered up to 6/13/06
6/23/2006
Does not save temporary output after each block
6/23/2006
Some minor changes related to the removal of saving the temporary backup output file
On 8-28 I disabled the command that turns autocalcualtion on because it does not work on Excel 97 computers in
Milne 323. Users should manually check to make sure autocalculation is on, or enable the command if they are
running Excel 2003
10/5/2006
Implements low and high uncertainty conditions (RL and RH) - Requires task data version 5
Reduced FB for experiment 4 not yet implemented. Need to run partial feedback first.
Eliminates blank row between blocks that were causing problems
Checks to make sure task data file has been updated to version 5
10/16/2006
Provides option for embedded instructions for Albany and UConn
11/7/06, changed properties on Close button for scoring feedback to try to make it work on Jim's machine.
11/22/2006
Implemented partial feedback conditions for Experiment 4-UConn.
1/25/2007 (Demo version. Demo capability is not included in subsequent versions)
For Jeryl's 611 class, I wanted to not require judgments, but this caused some problems, which have now been
fixed. The feedback screens still refer to judgment. They will have to be changed if the no judgment case is used
in research.
Added a "Demo" condition on the condition table. The conditions can be set for specific demos.
Attempted to make restart options available on the final screen if want to cut off demo early. Did not work, and
would require a lot of work to implement. Best to start the next block and then terminate.
1/26/2007 (Demo version. Demo capability is not included in subsequent versions)
This version implements "DEMO" capability so the program can be used as a stand alone for class exercises. Be
careful with this because it hides all the spreadsheets and tries to prevent any user control of the program or
spreadsheets. There is a "back door" available: double click on the intro label.
This version does not include the capabilty for Experiment 5
2/11/2007
Implemented partial feedback conditions for Experiment 4, screening task (based on UConn subjects).
4/9/2007
Implemented experiment 5 - same as experiment 4, but with low base rate (.1)
Condition Table changed to full/conditional/partial
2/11/2008
Implemented experiment 6 - same as experiment 4, but with high base rate (.8)
Next version to do
Separate screen resolution from task ("ESB19") so either task can be run at either resolution.
Table of conditions
The row in this table will determine the parameters of the program for each subject in each condition.
Condition Code
Feedback (full, partial, and
UConn base rate (Task =
Albany base rate (Task =
Uncertainty (.5, .7, .9)
Value Structure
Instructions
Base Rate
Reduced Reduced Status (program
Feedback
"UConn")
"Albany")
reduced)
Value structure (Equal penalty,
feedback feedback will not run for
R2
Description greater penalty for false positives, Experiment Notes
proportion- proportion- condition if this is
greater penalty for false negatives)
Albany UConn NOT blank)
1A Base case 1A RM BM VE FF 0.7 0.5 0.5 Equal Full Experiment I, main effect for feedback
1B Base case 1B RM BM VE FP 0.7 0.5 0.5 Equal Conditional Experiment I, main effect for feedback
1C Base case 1C RM BM VE FR 0.7 0.5 0.5 Equal Partial 0.5 0.35 Experiment I, main effect for feedback 11 people at Uconn were run with the Albany proportion of .5
2A Base case 1A RM BM VE FF 0.7 0.5 0.5 Equal Full Experiment II – base rate x feedback
2B Base case 1B RM BM VE FP 0.7 0.5 0.5 Equal Conditional Experiment II – base rate x feedback
2C Base case 1C RM BM VE FR 0.7 0.5 0.5 Equal Partial 0.5 0.35 Experiment II – base rate x feedback 12 people at Uconn were run before the proportion was set
2D Low base rate 1A RM BL VE FF 0.7 0.1 0.1 Equal Full Experiment II – base rate x feedback
2E Low base rate 1B RM BL VE FP 0.7 0.1 0.1 Equal Conditional Experiment II – base rate x feedback
2F Low base rate 1C RM BL VE FR 0.7 0.1 0.1 Equal Partial 0.15 0.15 Experiment II – base rate x feedback
2G High base rate 1A RM BH VE FF 0.7 0.8 0.8 Equal Full Experiment II – base rate x feedback
2H High base rate 1B RM BH VE FP 0.7 0.8 0.8 Equal Conditional Experiment II – base rate x feedback
2I High base rate 1C RM BH VE FR 0.7 0.8 0.8 Equal Partial 0.66 0.66 Experiment II – base rate x feedback
3A Albany-Same as 2D 1A RM BL VE FF 0.7 0.1 0.2 Equal Full Experiment III –payoff x feedback
3B Albany-Same as 2E 1B RM BL VE FP 0.7 0.1 0.2 Equal Conditional Experiment III –payoff x feedback
3C Albany-Same as 2F 1C RM BL VE FR 0.7 0.1 0.2 Equal Partial 0.15781 0.16 Experiment III –payoff x feedback
3D Penalize false negatives 3D RM BL VN FF 0.7 0.1 0.2 Greater penalty for false negatives Full Experiment III –payoff x feedback
3E Penalize false negatives 3E RM BL VN FP 0.7 0.1 0.2 Greater penalty for false negatives Conditional Experiment III –payoff x feedback
3F Penalize false negatives 3F RM BL VN FR 0.7 0.1 0.2 Greater penalty for false negatives Partial 0.222095 0.21 Experiment III –payoff x feedback
3G Penalize false positives 3G RM BL VP FF 0.7 0.1 0.2 Greater penalty for false positives Full Experiment III –payoff x feedback
3H Penalize false positives 3H RM BL VP FP 0.7 0.1 0.2 Greater penalty for false positives Conditional Experiment III –payoff x feedback
3I Penalize false positives 3I RM BL VP FR 0.7 0.1 0.2 Greater penalty for false positives Partial 0.130857 0.19 Experiment III –payoff x feedback
4A Base case 1A RM BM VE FF 0.7 0.5 0.5 Equal Full Experiment IV – uncertainty x feedback
4B Base case 1B RM BM VE FP 0.7 0.5 0.5 Equal Conditional Experiment IV – uncertainty x feedback
4C Base case 1C RM BM VE FR 0.7 0.5 0.5 Equal Partial Experiment IV – uncertainty x feedback Reduced feedback not implemented. Choose another condition
4D Less uncertainty 1A RH BM VE FF 0.9 0.5 0.5 Equal Full Experiment IV – uncertainty x feedback
4E Less uncertainty 1B RH BM VE FP 0.9 0.5 0.5 Equal Conditional Experiment IV – uncertainty x feedback
4F Less uncertainty 1C RH BM VE FR 0.9 0.5 0.5 Equal Partial 0.484435 0.4643657 Experiment IV – uncertainty x feedback
4G More uncertainty 1A RL BM VE FF 0.5 0.5 0.5 Equal Full Experiment IV – uncertainty x feedback
4H More uncertainty 1B RL BM VE FP 0.5 0.5 0.5 Equal Conditional Experiment IV – uncertainty x feedback
4I More uncertainty 1C RL BM VE FR 0.5 0.5 0.5 Equal Partial 0.482696 0.4197 Experiment IV – uncertainty x feedback
5A Base case 1A RM BL VE FF 0.7 0.1 0.1 Equal Full Experiment 5 – uncertainty x feedback - low BR
5B Base case 1B RM BL VE FP 0.7 0.1 0.1 Equal Conditional Experiment 5 – uncertainty x feedback - low BR
5C Base case 1C RM BL VE FR 0.7 0.1 0.1 Equal Partial Experiment 5 – uncertainty x feedback - low BR Reduced feedback not implemented. Choose another condition
5D Less uncertainty 1A RH BL VE FF 0.9 0.1 0.1 Equal Full Experiment 5 – uncertainty x feedback - low BR
5E Less uncertainty 1B RH BL VE FP 0.9 0.1 0.1 Equal Conditional Experiment 5 – uncertainty x feedback - low BR
5F Less uncertainty 1C RH BL VE FR 0.9 0.1 0.1 Equal Partial Experiment 5 – uncertainty x feedback - low BR Reduced feedback not implemented. Choose another condition
5G More uncertainty 1A RL BL VE FF 0.5 0.1 0.1 Equal Full Experiment 5 – uncertainty x feedback - low BR
5H More uncertainty 1B RL BL VE FP 0.5 0.1 0.1 Equal Conditional Experiment 5 – uncertainty x feedback - low BR
5I More uncertainty 1C RL BL VE FR 0.5 0.1 0.1 Equal Partial Experiment 5 – uncertainty x feedback - low BR Reduced feedback not implemented. Choose another condition
6A Base case 1A RM BH VE FF 0.7 0.8 0.8 Equal Full Experiment 6 – uncertainty x feedback - high BR
6B Base case 1B RM BH VE FP 0.7 0.8 0.8 Equal Conditional Experiment 6 – uncertainty x feedback - high BR
6C Base case 1C RM BH VE FR 0.7 0.8 0.8 Equal Partial Experiment 6 – uncertainty x feedback - high BR Reduced feedback not implemented. Choose another condition
6D Less uncertainty 1A RH BH VE FF 0.9 0.8 0.8 Equal Full Experiment 6 – uncertainty x feedback - high BR
6E Less uncertainty 1B RH BH VE FP 0.9 0.8 0.8 Equal Conditional Experiment 6 – uncertainty x feedback - high BR
6F Less uncertainty 1C RH BH VE FR 0.9 0.8 0.8 Equal Partial Experiment 6 – uncertainty x feedback - high BR Reduced feedback not implemented. Choose another condition
6G More uncertainty 1A RL BH VE FF 0.5 0.8 0.8 Equal Full Experiment 6 – uncertainty x feedback - high BR
6H More uncertainty 1B RL BH VE FP 0.5 0.8 0.8 Equal Conditional Experiment 6 – uncertainty x feedback - high BR
6I More uncertainty 1C RL BH VE FR 0.5 0.8 0.8 Equal Partial Experiment 6 – uncertainty x feedback - high BR Reduced feedback not implemented. Choose another condition
Greyed out conditions are not yet implemented in this program
Various parameters are set here.
Value Name Purpose
0 ProportionPositive Proportion of positive decisions for people in partial feedback. Controls proport
6G CurrentCondition
25 MaxCases Sets the maximum number of cases for a block of MCPL learning trials. This sh
Colored
0 ShowCases Set the number of cases presented in MCPL learning.
cells are
3 NumCues Sets the number of cues (Note - if you change this, other changes are required
set on
Intro 0 MaxBlocks Sets the number of MCPL learning blocks presented
page
20 MaxBlocksDecision Sets the number of decision learning blocks
25 MaxCasesDecision Sets the maximum number of cases for a block of decision trials. This should r
25 ShowCasesDecision Set the number of cases presented in decision making'
20.0 CueWtIdeal01 Ideal cue weight for Cue 1
50.0 CueWtIdeal02 Ideal cue weight for Cue 2
30.0 CueWtIdeal03 Ideal cue weight for Cue 3
Clothing CueName01 Name for cue 1
Emotionality CueName02 Name for cue 2
Bulging CueName03 Name for cue 3
Emotional maturityCueNameUConn01 Name for cue 1
CueNameUConn02 Name for cue 2
Attention to detail
CueNameUConn03 Name for cue 3
Interpersonal skill
Threshold for uncert. cond.
Base Rate RM RL RH
0.100 7.860 7.000 7.608 BL
Table for converting
base rates to 0.200 7.240 6.396 6.736 BL
thresholds. Only 0.500 5.640 5.012 5.204 BM
base rates listed here 0.800 4.300 3.569 3.652 BH
will work properly. Put future base rates here, sort t
Put future base rates here, sort t
Put future base rates here, sort t
Decision task Performance cutoffs
CutoffDollarsTable 10 Dollars maximum bonus
Points required on Exponent
two consecutive Dollars for
Condition blocks to quit per point correlation
1A 3000 0.00017 1 In the "New" performance bonus
1B 3000 0.00017 1 system, the bonus is the number of
1C 3000 0.00017 1 points X dollars per point X judgment
2D 3000 0.00017 1 accuracy correlation.
2E 3000 0.00017 1
2F 3000 0.00017 1 The judgment accuracy correlation is the
2G 3000 0.00017 1 correlation between the judgment and
2H 3000 0.00017 1 the calculated Y using the ideal weights.
2I 3000 0.00017 1 It is taken to a power determined by the
3A 3000 0.00017 1 exponent.
3B 3000 0.00017 1
3C 3000 0.00017 1 To get the old performance bonus, set
dollars per point to .00025 for all
3D 3000 0.00017 1
conditions and set the exponent to 0. If
3E 3000 0.00017 1
you use the old bonus, make sure to set
3F 3000 0.00017 1 the feedback flag on the intro page to
3G 3000 0.00017 1 get the feedback that only mentions the
decision points.
get the feedback that only mentions the
3H 3000 0.00017 1 decision points.
3I 3000 0.00017 1
4A 3000 0.00017 1
4B 3000 0.00017 1
4C 3000 0.00017 1
4D 3000 0.00017 1
4E 3000 0.00017 1
4F 3000 0.00017 1
4G 3000 0.00017 1
4H 3000 0.00017 1
4I 3000 0.00017 1
5A 3000 0.00017 1
5B 3000 0.00017 1
5C 3000 0.00017 1
5D 3000 0.00017 1
5E 3000 0.00017 1
5F 3000 0.00017 1
5G 3000 0.00017 1
5H 3000 0.00017 1
5I 3000 0.00017 1
6A 3000 0.00017 1
6B 3000 0.00017 1
6C 3000 0.00017 1
6D 3000 0.00017 1
6E 3000 0.00017 1
6F 3000 0.00017 1
6G 3000 0.00017 1
6H 3000 0.00017 1
6I 3000 0.00017 1
Set these at 2500 to eliminate performance cutoff.
1750 was used as a reasonable cutoff
Various indicators of current status are stored below.
Value Name Purpose
1 CurrentBlock
VE Current Values condition (need to look up values on TaskDataDecision)
Original order and order
for storage Presentation order
Within
block Overall
randomiza randomizat
Block Case Random numbers tion ion
1 1 0.341868894 1 1
1 4 0.462218468 4 2
1 21 0.986538618 21 3
1 16 0.944875174 16 4
1 3 0.413197534 3 5
1 22 0.207936904 22 6
1 9 0.341496419 9 7
1 14 0.61633664 14 8
1 10 0.657305431 10 9
1 2 0.419745386 2 10
1 17 0.099259428 17 11
1 25 0.082628946 25 12
1 24 0.735131255 24 13
1 15 0.12189003 15 14
1 13 0.594197503 13 15
1 18 0.593025211 18 16
1 8 0.067349459 8 17
1 19 0.284282548 19 18
1 12 0.85765772 12 19
1 5 0.092633694 5 20
1 20 0.169490721 20 21
1 7 0.158088749 7 22
1 23 0.043788226 23 23
1 6 0.14377668 6 24
1 11 0.055894042 11 25
2 1 0.323365793 1 26
2 2 0.94324492 2 27
2 3 0.7024273 3 28
2 4 0.115722927 4 29
2 5 0.433692611 5 30
2 6 0.678992416 6 31
2 7 0.010332093 7 32
2 8 0.374608685 8 33
2 9 0.834338457 9 34
2 10 0.369131233 10 35
2 11 0.752089605 11 36
2 12 0.998186123 12 37
2 13 0.122756968 13 38
2 14 0.859157792 14 39
2 15 0.844928701 15 40
2 16 0.463247748 16 41
2 17 0.556644766 17 42
2 18 0.738932645 18 43
2 19 0.307803918 19 44
2 20 0.738390618 20 45
2 21 0.485617452 21 46
2 22 0.028885199 22 47
2 23 0.401437916 23 48
2 24 0.809256507 24 49
2 25 0.96065688 25 50
3 1 0.163622656 1 51
3 2 0.590835368 2 52
3 3 0.245910337 3 53
3 4 0.3142447 4 54
3 5 0.579746717 5 55
3 6 0.951158406 6 56
3 7 0.436571876 7 57
3 8 0.634309265 8 58
3 9 0.995483244 9 59
3 10 0.210091062 10 60
3 11 0.186109022 11 61
3 12 0.283353712 12 62
3 13 0.53519201 13 63
3 14 0.895343265 14 64
3 15 0.813471563 15 65
3 16 0.250080443 16 66
3 17 0.859404315 17 67
3 18 0.611353396 18 68
3 19 0.386792062 19 69
3 20 0.769490539 20 70
3 21 0.747749773 21 71
3 22 0.233437119 22 72
3 23 0.020365957 23 73
3 24 0.922878099 24 74
3 25 0.794605989 25 75
4 1 0.540842338 1 76
4 2 0.588882014 2 77
4 3 0.334817003 3 78
4 4 0.54870997 4 79
4 5 0.823162993 5 80
4 6 0.85747574 6 81
4 7 0.654720313 7 82
4 8 0.307206527 8 83
4 9 0.556067957 9 84
4 10 0.804317152 10 85
4 11 0.035197946 11 86
4 12 0.625294624 12 87
4 13 0.621422117 13 88
4 14 0.412215838 14 89
4 15 0.320271599 15 90
4 16 0.192985253 16 91
4 17 0.155714593 17 92
4 18 0.476336019 18 93
4 19 0.196706977 19 94
4 20 0.970542615 20 95
4 21 0.432743091 21 96
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19 16 0.991765022 16 466
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19 18 0.642400481 18 468
19 19 0.16194979 19 469
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20 1 0.522259069 1 476
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20 5 0.231562815 5 480
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20 15 0.492608377 15 490
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20 17 0.994974381 17 492
20 18 0.777144702 18 493
20 19 0.110770449 19 494
20 20 0.245176626 20 495
20 21 0.592192755 21 496
20 22 0.568678962 22 497
20 23 0.097014532 23 498
20 24 0.051574023 24 499
20 25 0.394042298 25 500
partial feedback. Controls proportion of feedback in reduced feedback condition
ck of MCPL learning trials. This should represent the storage allocated.
e this, other changes are required as well. See Program Instructions)
ck of decision trials. This should represent the storage allocated.
Low base rate
Low base rate for UConn experiment 3
Medium base rate
High base rate
Put future base rates here, sort table by base rate
Put future base rates here, sort table by base rate
Put future base rates here, sort table by base rate
mance bonus
s the number of
point X judgment
acy correlation is the
the judgment and
ng the ideal weights.
r determined by the
mance bonus, set
00025 for all
he exponent to 0. If
us, make sure to set
the intro page to
at only mentions the
at only mentions the
Feedback
condition
Full
Partial
Reduced
Payoff
condition
Equal
Greater
penalty for
false
negatives
Greater
penalty for
false
positives
This text is used to construct the embedded instructions (frmInstEmbed1)
Albany
Feedback text
After you enter your decision, you will be shown the correct decision, as if an experienced
inspector were by your side teaching you.
After you enter your decision, you will be shown the correct decision, as if an experienced
inspector were by your side teaching you. But if you decide not to search someone, they will
walk out the door and you will not receive any feedback about whether your decision was
right or wrong. This simulates what happens in real life.
After you enter your decision, you will be shown the correct decision, as if an experienced
inspector were by your side teaching you. However, the experienced inspectors have to watch
trainees at other checkpoints, so they won’t be around all the time. When they are not
observing, you won’t be told whether you made the right decision.
Points Story
You don’t want to let smugglers pass, but searching people who are not smuggling is
degrading to them and unpleasant. Too many unnecessary searches may harm our diplomatic
relations with Kenswik, a major trading partner of the U.S. Therefore, you will be penalized
equally for two kinds of incorrect decisions.
If you let someone through who is smuggling drugs, the drugs will be on the street
contributing to drug addiction and crime. They may be sold to children. Therefore, you are
penalized more severely for this kind of error.
Although your job is to catch drug smugglers, relations between the U.S. and Kenswik are
currently at a very sensitive state. If you search too many nationals from Kenswik, or
mistakenly search a high government official, or a relative of one, you may be fired.
Therefore you are penalized more severely for searches that are not necessary.
Points text
You will receive or lose points
for every decision.
You will receive or lose points
for every decision, even if you
are not told what the correct
decision was.
You will receive or lose points
for every decision, even if you
are not told what the correct
decision was.
Payoff amounts
Points lost Payoff Text (created by concatenating payoff cond
True True False
False Negative positive Negative Positive False Negative
4. If you decide NO and
the best decision is YES,
you are INCORRECT and
50 100 100 50 you will lose 50 points.
4. If you decide NO and
the best decision is YES,
you are INCORRECT and
100 100 100 50 you will lose 100 points.
4. If you decide NO and
the best decision is YES,
you are INCORRECT and
50 100 100 100 you will lose 50 points.
created by concatenating payoff condition)
True positive True Negative False Positive
2. If you decide NO 3. If you decide YES
1. If you decide YES and the best and the best decision and the best decision
decision is YES, you are is NO, you are is NO, you are
CORRECT and you will gain 100 CORRECT and you INCORRECT and you
points. will gain 100 points. will lose 50 points.
2. If you decide NO 3. If you decide YES
1. If you decide YES and the best and the best decision and the best decision
decision is YES, you are is NO, you are is NO, you are
CORRECT and you will gain 100 CORRECT and you INCORRECT and you
points. will gain 100 points. will lose 50 points.
2. If you decide NO 3. If you decide YES
1. If you decide YES and the best and the best decision and the best decision
decision is YES, you are is NO, you are is NO, you are
CORRECT and you will gain 100 CORRECT and you INCORRECT and you
points. will gain 100 points. will lose 100 points.
Feedback
condition
Full
Partial
Reduced
Payoff
condition
Equal
Greater
penalty for
false
negatives
Greater
penalty for
false
positives
This text is used to construct the embedded instructions (frmInstEmbed1)
Cell names are used by program. Do not change them.
UConn
Feedback text
After you enter your decision, you will be shown the correct decision, as if an experienced
personnel interviewer were by your side teaching you.
After you enter your decision, you will be shown the correct decision, as if an experienced
interviewer were by your side teaching you. But if you decide not to hire someone, he or she
will walk out the door and you will not receive any feedback about whether your decision was
right or wrong. This simulates what happens in real life.
After you enter your decision, you will be shown the correct decision, as if an experienced
interviewer were by your side teaching you. However, the experienced interviewers have to
watch other trainees, so they won’t be around all the time. When they are not observing, you
won’t be told whether you made the right decision.
Points Story
You don’t want to turn away qualified workers, but hiring workers who do not perform
successfully is contrary to our goal of improving homeland security. Therefore, you will be
penalized equally for the two kinds of incorrect decisions.
You do NOT want to turn away qualified workers; good workers are hard to find! And, of
course, hiring workers who do not perform successfully is contrary to our goal of improving
homeland security. Anyone who is hired but then does not perform successfully will be
dismissed during the probationary period. To discourage turning away qualified workers, you
will be penalized differently for the two kinds of incorrect decisions.
You do NOT want to hire unqualified workers, nor do you want to turn away qualified
workers. Hiring workers who do not perform successfully is contrary to our goal of
improving homeland security. To discourage hiring unqualified workers, you will be
penalized differently for the two kinds of incorrect decisions.
Points text
You will receive or lose points
for every decision.
You will receive or lose points
for every decision, even if you
are not told what the correct
decision was.
You will receive or lose points
for every decision, even if you
are not told what the correct
decision was.
Payoff amounts
Points lost Payoff Text (created by concatenating payoff cond
True True False
False Negative positive Negative Positive False Negative
4. If you decide NO and
the best decision is YES,
you are INCORRECT and
50 100 100 50 you will lose 50 points.
4. If you decide NO and
the best decision is YES,
you are INCORRECT and
100 100 100 50 you will lose 100 points.
4. If you decide NO and
the best decision is YES,
you are INCORRECT and
50 100 100 100 you will lose 50 points.
created by concatenating payoff condition)
True positive True Negative False Positive
2. If you decide NO 3. If you decide YES
1. If you decide YES and the best and the best decision and the best decision
decision is YES, you are is NO, you are is NO, you are
CORRECT and you will gain 100 CORRECT and you INCORRECT and you
points. will gain 100 points. will lose 50 points.
2. If you decide NO 3. If you decide YES
1. If you decide YES and the best and the best decision and the best decision
decision is YES, you are is NO, you are is NO, you are
CORRECT and you will gain 100 CORRECT and you INCORRECT and you
points. will gain 100 points. will lose 50 points.
2. If you decide NO 3. If you decide YES
1. If you decide YES and the best and the best decision and the best decision
decision is YES, you are is NO, you are is NO, you are
CORRECT and you will gain 100 CORRECT and you INCORRECT and you
points. will gain 100 points. will lose 100 points.
Table of values
The row in this table will determine the payoff for each outcome.
Points lost Points lost
Payoff condition Code False Negative True positive True Negative False Positive
Equal (VE) VE 50 100 100 50
Greater penalty for false negatives (VN) VN 100 100 100 50
Greater penalty for false positives (VP) VP 50 100 100 100
Original plan, from proposal (has been revised)
A. Equal penalty for false positives B. Greater penalty for false C. Greater penalty for false
and false negatives negatives positives
Decision Decision Decision
Event Negative Positive Negative Positive Negative Positive
Positive 50 100 0 100 50 100
Negative 100 50 100 50 100 0
0 =current subject Stats for calculating score and for final decision feedback
10 =number of cases 100 =Pct correct
5 =number of current case 100 = Pct positive decisions made
No =current De 0.502 R2 (500cases) 100 =100*Sample base rate
Threshold Sum = 0 1 0
Mean 4.980821 3.569165 Value= 50 100 100
Std. Dev. 1.58958 0.802 =base rate Total = 100 Accumlated over blocks
Accuracy False True
Case Ye Ys De Correlation Ds Negative True positive Negative
1 6.628849 6 1 1 0 1 0
2 5.23407 1
3 2.756836 0
4 8.835499 1
5 6.517632 1
6 7.513529 1
7 6.041814 1
8 6.615223 1
9 5.749591 1
10 6.589146 1
Don't delete formulas!!
11 5.693119 1
12 8.591425 1
13 3.250407 0
14 4.842529 1
15 4.780322 1
16 6.570962 1
17 4.984693 1
18 4.685442 1
19 1.981335 0
20 6.443958 1
21 3.085308 0
22 1.993666 0 R2 (check)
23 7.871413 1 0.6151026
24 4.674287 1
25 7.220755 1 #DIV/0!
1 3.602556 1
2 2.834636 0
3 5.538623 1
4 8.225841 1
5 5.437145 1
6 3.904519 1
7 2.880937 0
8 4.068754 1
9 4.552033 1
10 2.671264 0
11 4.149025 1
12 4.025476 1
13 6.72872 1
14 1.282098 0
15 4.530826 1
16 8.009139 1
17 2.996041 0
18 3.395594 0
19 3.256572 0
20 3.960248 1
21 5.139069 1
22 4.029363 1 R2 (check)
23 5.183331 1 0.3991321
24 3.174886 0
25 5.414523 1 #DIV/0!
1 3.86352 1
2 4.245321 1
3 5.690408 1
4 7.319642 1
5 5.640533 1
6 8.540134 1
7 6.423183 1
8 4.893267 1
9 5.188201 1
10 5.554528 1
11 5.483446 1
12 5.253118 1
13 4.942711 1
14 7.002326 1
15 5.432707 1
16 4.139405 1
17 2.617382 0
18 7.422967 1
19 3.631776 1
20 5.249423 1
21 5.164714 1
22 6.49297 1 R2 (check)
23 3.129449 0 0.5066752
24 5.123716 1
25 4.235893 1 #DIV/0!
1 1.965861 0
2 4.350805 1
3 7.60354 1
4 1.949092 0
5 4.591857 1
6 3.004485 0
7 5.23407 1
8 5.453914 1
9 4.767872 1
10 7.910684 1
11 3.373092 0
12 6.692903 1
13 5.121988 1
14 8.3812 1
15 4.622684 1
16 5.21946 1
17 3.215022 0
18 5.604835 1
19 5.342145 1
20 3.320074 0
21 6.543277 1
22 7.419393 1
23 3.287951 0
24 5.869997 1
25 6.451851 1 #DIV/0!
1 4.025908 1
2 5.469268 1
3 5.673639 1
4 3.431843 0
5 3.533753 0
6 1.539487 0
7 4.909605 1
8 3.920424 1
9 5.25614 1
10 4.619661 1
11 4.457585 1
12 3.296276 0
13 2.316283 0
14 5.931891 1
15 4.243162 1
16 2.201611 0
17 5.042893 1
18 5.364216 1
19 3.600829 1
20 3.242946 0
21 6.947149 1
22 5.946933 1
23 3.653847 1
24 4.232007 1
25 5.949524 1 #DIV/0!
1 4.167641 1
2 5.837011 1
3 4.476633 1
4 3.853348 1
5 2.75782 0
6 5.761922 1
7 4.134535 1
8 5.267176 1
9 6.848262 1
10 4.255805 1
11 8.231575 1
12 1.053929 0
13 3.218164 0
14 2.514609 0
15 3.722219 1
16 4.755109 1
17 6.925078 1
18 6.841113 1
19 3.928437 1
20 5.203986 1
21 2.963054 0
22 4.814724 1
23 4.447965 1
24 3.524133 0
25 6.527803 1 #DIV/0!
1 6.191439 1
2 5.333268 1
3 6.24748 1
4 4.175102 1
5 4.304504 1
6 5.543061 1
7 6.823049 1
8 5.181052 1
9 5.898353 1
10 4.207345 1
11 2.962191 0
12 3.306448 0
13 4.324296 1
14 6.734765 1
15 5.741698 1
16 5.2755 1
17 2.544141 0
18 9.10781 1
19 6.710535 1
20 4.853132 1
21 2.909605 0
22 3.400464 0
23 3.723082 1
24 6.10402 1
25 5.498368 1 #DIV/0!
1 8.555176 1
2 5.090609 1
3 4.132256 1
4 7.553545 1
5 5.229512 1
6 6.178677 1
7 5.867958 1
8 2.388661 0
9 6.216221 1
10 6.102292 1
11 1.419824 0
12 4.17954 1
13 4.048843 1
14 3.561557 0
15 5.042461 1
16 5.956121 1
17 6.643459 1
18 7.225625 1
19 5.295844 1
20 3.716485 1
21 7.282649 1
22 5.415386 1
23 3.977328 1
24 4.946717 1
25 5.192087 1 #DIV/0!
1 5.135183 1
2 5.042461 1
3 3.30472 0
4 6.390077 1
5 6.210799 1
6 5.188633 1
7 3.576599 1
8 7.707609 1
9 6.633719 1
10 5.314772 1
11 5.117982 1
12 7.864935 1
13 6.180836 1
14 7.871844 1
15 5.23407 1
16 5.418529 1
17 4.370597 1
18 2.954178 0
19 2.581253 0
20 5.241099 1
21 5.445902 1
22 5.213294 1
23 5.749159 1
24 6.532122 1
25 3.807359 1 #DIV/0!
1 3.704586 1
2 5.498919 1
3 5.645403 1
4 2.501294 0
5 5.453362 1
6 1.77123 0
7 6.652648 1
8 4.467756 1
9 3.759763 1
10 6.562205 1
11 2.332741 0
12 4.846967 1
13 5.245105 1
14 6.432491 1
15 3.199236 0
16 4.029051 1
17 3.278211 0
18 4.404999 1
19 5.320074 1
20 5.207009 1
21 6.961639 1
22 4.173807 1
23 5.460511 1
24 4.473058 1
25 3.369949 0 #DIV/0!
1 3.117118 0
2 4.543277 1
3 4.869781 1
4 6.542845 1
5 5.897802 1
6 5.505949 1
7 3.505949 0
8 5.154111 1
9 5.929732 1
10 3.110953 0
11 4.953746 1
12 5.793301 1
13 2.584276 0
14 3.872276 1
15 6.399385 1
16 6.751534 1
17 6.412699 1
18 5.383264 1
19 5.631776 1
20 6.051122 1
21 9.272909 1
22 7.958832 1
23 5.272046 1
24 4.295196 1
25 0.087251 0 #DIV/0!
1 6.108458 1
2 5.537759 1
3 5.715189 1
4 3.658285 1
5 5.478576 1
6 5.439304 1
7 3.093632 0
8 6.942711 1
9 4.569666 1
10 6.411716 1
11 7.286968 1
12 3.311749 0
13 3.479008 0
14 3.373955 0
15 6.67527 1
16 5.907542 1
17 5.264897 1
18 4.75295 1
19 4.838522 1
20 5.374939 1
21 4.580821 1
22 5.775548 1
23 5.801745 1
24 3.871844 1
25 -0.11798 0 #DIV/0!
1 6.206049 1
2 5.832141 1
3 2.272262 0
4 6.083244 1
5 5.918577 1
6 3.148377 0
7 4.70437 1
8 3.583196 1
9 6.84296 1
10 6.50746 1
11 4.905718 1
12 5.591641 1
13 4.779027 1
14 6.632856 1
15 6.333604 1
16 3.510267 0
17 6.929204 1
18 3.038455 0
19 7.21459 1
20 3.478144 0
21 6.604619 1
22 7.520127 1
23 3.503789 0
24 4.926374 1
25 5.388997 1 #DIV/0!
1 7.209288 1
2 6.174239 1
3 4.079358 1
4 2.283729 0
5 6.96922 1
6 4.691056 1
7 3.060094 0
8 3.837443 1
9 4.984262 1
10 5.533321 1
11 3.229631 0
12 3.580486 1
13 4.841665 1
14 5.000167 1
15 7.015089 1
16 4.181268 1
17 4.326455 1
18 6.299634 1
19 1.727089 0
20 3.762354 1
21 5.142332 1
22 4.779459 1
23 4.302657 1
24 4.233734 1
25 7.291838 1 #DIV/0!
1 5.846319 1
2 4.878346 1
3 3.710751 1
4 7.197821 1
5 6.079358 1
6 8.440816 1
7 8.250623 1
8 4.790374 1
9 6.365847 1
10 5.487884 1
11 5.730663 1
12 5.616734 1
13 2.897274 0
14 6.763985 1
15 5.354907 1
16 4.367574 1
17 7.163851 1
18 2.635879 0
19 4.280466 1
20 8.163635 1
21 4.587419 1
22 5.068538 1
23 4.675702 1
24 4.13108 1
25 3.305152 0 #DIV/0!
1 5.339554 1
2 6.374603 1
3 2.826623 0
4 5.45521 1
5 6.34464 1
6 5.343992 1
7 2.375899 0
8 4.280154 1
9 5.796324 1
10 4.425774 1
11 2.813429 0
12 4.872492 1
13 3.844592 1
14 7.196093 1
15 3.286104 0
16 5.300162 1
17 5.928317 1
18 4.441247 1
19 4.17467 1
20 6.290326 1
21 3.672343 1
22 2.831061 0
23 5.746017 1
24 3.084012 0
25 1.929732 0 #DIV/0!
1 4.085092 1
2 6.105315 1
3 3.653415 1
4 4.525956 1
5 7.792437 1
6 4.885375 1
7 7.651136 1
8 5.324512 1
9 5.751007 1
10 2.248344 0
11 4.668553 1
12 6.555608 1
13 3.307743 0
14 1.456073 0
15 4.695062 1
16 5.483878 1
17 4.856587 1
18 4.124363 1
19 4.716269 1
20 6.047547 1
21 6.499135 1
22 3.410636 0
23 5.924862 1
24 6.991291 1
25 3.06897 0 #DIV/0!
1 4.263385 1
2 6.329166 1
3 2.577247 0
4 7.585356 1
5 3.761922 1
6 5.523149 1
7 5.445902 1
8 5.388014 1
9 5.989347 1
10 6.026772 1
11 5.73726 1
12 3.610137 1
13 4.402408 1
14 3.709888 1
15 7.14178 1
16 6.676134 1
17 9.023965 1
18 6.25722 1
19 3.818514 1
20 2.160924 0
21 2.859729 0
22 4.77718 1
23 1.903655 0
24 5.067555 1
25 3.918577 1 #DIV/0!
1 6.189712 1
2 6.895427 1
3 4.289343 1
4 3.298555 0
5 7.2755 1
6 7.98263 1
7 4.899865 1
8 4.59001 1
9 4.256788 1
10 2.177261 0
11 3.411932 0
12 6.33977 1
13 4.081949 1
14 4.902144 1
15 7.259715 1
16 4.8064 1
17 6.388781 1
18 5.120693 1
19 7.163851 1
20 2.018879 0
21 6.68976 1
22 4.63643 1
23 4.669848 1
24 4.238172 1
25 4.682299 1 #DIV/0!
1 3.366495 0
2 3.956553 1
3 8.482798 1
4 4.571945 1
5 4.39581 1
6 2.170664 0
7 7.302009 1
8 5.870549 1
9 5.499351 1
10 3.625179 1
11 3.667473 1
12 4.481503 1
13 4.845551 1
14 2.736181 0
15 6.324728 1
16 4.236996 1
17 6.519359 1
18 4.453147 1
19 4.64617 1
20 3.585356 1
21 6.565348 1
22 7.778691 1
23 4.960344 1
24 5.049059 1
25 3.362488 0 #DIV/0!
r final decision feedback
1 = Total decisions made
0
50 4.044 3.858 4.044 #DIV/0! #DIV/0! 0.000 0.000 0.000
ed over blocks 2.269 2.239 2.271 #DIV/0! #DIV/0! 0.000 0.000 0.000
False
Positive Cue 1 Cue 2 Cue 3 Cue 4 Cue 5 Cue 6 Cue 7 Cue 8
0 7 7 2 0 0 0
2 2 7 0 0 0
5 1 7 0 0 0
3 7 4 0 0 0
6 6 3 0 0 0
1 3 7 0 0 0
7 5 2 0 0 0
1 7 2 0 0 0
1 7 3 0 0 0
6 4 6 0 0 0
7 1 7
7 3 6
3 2 2
6 3 3
3 1 6
6 7 7
2 4 2
3 3 1
7 1 1
7 2 6
2 1 5
6 2 2
2 7 7
5 6 3
7 6 6
6 2 7
1 1 2
2 6 2
5 7 7
7 2 2
1 5 3
2 2 3
1 2 6
6 6 1
6 1 2
3 7 1
3 2 6
6 1 5
5 2 3
6 5 4
7 6 5
2 7 2
2 1 7
7 1 2
1 1 2
1 7 6
3 5 7
1 5 1
1 2 6
7 6 3
6 2 1
3 3 3
1 6 7
6 6 6
4 7 3
6 7 6
6 6 2
4 3 6
3 3 2
2 6 1
1 7 1
5 1 2
2 3 7
1 6 5
6 1 6
6 2 1
3 1 6
5 5 6
2 3 2
6 5 6
1 7 3
6 3 4
2 1 5
7 3 2
6 6 7
2 1 6
1 2 7
1 7 3
4 3 1
6 2 1
1 2 7
2 2 7
7 1 4
3 6 4
6 6 7
5 3 1
7 5 2
5 1 4
7 7 7
7 1 7
3 7 1
7 1 6
6 2 6
6 7 1
1 3 7
3 6 2
7 6 6
3 1 2
7 7 6
7 6 3
5 4 2
6 6 3
2 6 6
4 1 2
7 1 4
1 3 1
6 5 1
7 2 3
5 2 5
5 2 2
7 5 1
7 2 2
2 6 2
7 6 1
6 1 1
3 1 3
7 3 5
7 3 7
7 3 2
1 1 2
7 3 6
7 1 7
3 2 3
7 2 7
2 6 6
1 1 7
2 7 1
1 1 7
3 1 6
1 2 1
6 5 5
7 1 3
2 2 7
2 6 5
6 7 2
7 7 6
2 2 2
4 3 1
1 1 2
7 3 2
6 5 1
1 6 5
7 7 6
4 1 2
5 6 3
1 3 1
1 6 1
6 1 2
2 1 1
7 3 6
6 3 7
6 6 6
2 6 6
6 2 5
5 2 1
1 6 1
6 6 6
1 5 6
2 7 2
1 2 7
1 1 3
6 1 3
6 4 2
6 6 6
1 6 1
6 3 7
4 1 3
3 7 7
7 6 2
2 3 5
7 1 3
1 2 5
7 2 5
7 7 1
7 7 3
6 5 7
2 5 2
2 6 3
7 7 7
6 7 6
1 4 6
3 1 7
1 7 2
2 2 7
1 6 4
1 4 2
2 5 1
6 2 6
2 5 1
7 5 1
5 7 4
5 3 7
3 7 6
6 2 6
2 3 5
4 5 3
3 6 7
7 3 4
7 2 3
3 6 3
6 2 5
7 2 6
2 1 3
6 6 2
5 6 7
3 1 6
2 3 2
6 7 7
7 7 1
3 6 5
3 5 1
1 2 6
6 7 1
7 6 6
2 2 7
1 7 3
6 6 2
5 1 5
2 1 2
7 2 6
7 2 7
7 3 4
7 3 5
7 7 6
1 7 3
4 2 4
2 2 7
2 6 5
1 1 7
3 6 6
6 1 1
6 6 7
1 3 7
2 1 7
5 5 6
3 1 1
5 3 2
2 5 2
7 1 7
1 2 1
1 1 6
2 2 3
6 6 4
2 5 7
6 6 7
5 7 6
5 3 2
5 1 7
2 3 6
7 2 5
2 1 3
3 3 3
6 6 6
7 1 7
6 7 7
2 7 1
7 2 2
1 5 7
2 3 6
1 2 1
6 5 1
2 5 5
2 2 5
1 7 1
2 6 2
5 6 7
7 1 2
1 5 3
7 4 1
6 2 5
2 7 7
6 7 3
7 3 1
6 2 3
1 2 1
7 3 6
5 1 7
2 3 7
6 1 1
5 6 1
2 6 2
3 5 2
7 7 1
2 5 7
6 5 3
3 7 7
1 1 6
5 1 6
3 1 7
7 1 7
3 7 7
6 7 1
7 2 2
5 6 1
6 1 2
7 1 2
7 7 5
6 1 5
7 3 3
1 1 1
4 5 2
6 3 6
1 2 1
1 7 7
7 6 6
1 6 1
1 6 1
7 2 2
6 7 1
5 6 5
1 1 5
1 2 6
3 1 4
2 7 3
2 7 1
4 6 4
1 2 1
2 3 6
3 7 6
3 1 6
6 6 1
6 2 7
2 2 2
4 6 1
2 3 6
6 6 6
6 6 1
7 1 3
2 2 1
7 3 6
3 7 3
2 2 7
3 4 2
7 1 3
5 2 7
6 2 2
1 4 7
2 2 6
6 2 1
2 6 7
2 6 2
5 7 1
5 2 6
2 2 2
3 3 7
1 1 4
1 1 6
6 2 6
1 6 7
6 7 4
2 3 5
6 1 2
1 2 7
1 7 7
2 6 2
6 7 6
7 6 3
4 6 4
6 1 7
1 6 1
2 2 7
4 6 2
1 1 5
2 4 6
7 7 3
6 2 4
4 5 7
1 6 2
7 6 7
3 7 6
6 3 1
1 6 1
1 2 6
2 1 1
6 1 1
6 1 7
1 6 7
2 1 6
7 1 6
7 5 1
7 2 3
3 4 3
6 1 7
5 6 6
6 6 2
3 1 2
7 6 1
3 1 1
7 3 7
2 3 5
2 7 7
4 6 6
6 2 7
1 6 1
1 7 3
7 1 5
2 3 1
2 6 7
1 2 2
1 1 2
3 1 6
1 7 7
6 1 2
2 6 6
2 6 6
1 5 1
6 7 5
6 3 6
7 2 1
5 2 4
7 5 3
6 6 2
2 2 6
2 2 1
1 7 2
7 5 1
1 6 6
2 2 6
3 6 1
5 6 2
3 3 7
2 2 3
1 7 1
7 3 6
1 1 6
7 1 6
3 7 2
1 1 5
4 7 7
6 2 6
7 2 7
3 6 3
6 5 7
5 2 4
2 6 6
2 6 2
2 1 6
3 3 7
5 2 3
7 5 5
7 3 5
7 5 6
5 6 2
5 1 2
5 1 7
2 1 6
1 7 1
6 1 1
3 7 5
6 7 1
6 5 1
6 6 7
6 5 6
2 1 2
2 7 7
7 7 5
4 7 1
6 3 5
1 3 5
2 5 2
3 1 6
1 5 6
2 6 2
6 2 3
5 5 1
3 2 2
2 7 1
1 2 3
7 5 5
3 1 2
5 5 7
7 2 1
3 6 6
6 6 2
1 3 6
5 2 1
7 6 1
2 6 7
6 1 6
2 3 6
1 2 6
2 6 7
3 4 6
6 2 5
1 3 1
3 2 7
7 1 2
7 6 2
2 2 1
1 7 5
2 2 1
3 7 6
2 5 6
5 1 2
6 2 6
7 5 1
7 6 7
2 7 2
7 6 1
1 2 6
0.707104 Re, the correlation between Y calculated from weights and the Ye used for d
3.95
1.33 Standard deviation
0.000 0.000
0.000 0.000
Feedback Y calculated Std Dev
Cue 9 Cue 10 Given from weights Ys Block
0 0 5.50 1
0 0 3.50 1
0 0 3.60 1
0 0 5.30 1
0 0 5.10 1
0 0 3.80 1
0 0 4.50 1
0 0 4.30 1
0 0 4.60 1
0 0 5.00 1
4.00 1
4.70 1
2.20 1
3.60 1
2.90 1
6.80 1
3.00 1
2.40 1
2.20 1
4.20 1
2.40 1
2.80 1
6.00 1
4.90 1
6.20 0 1
4.30 2
1.30 2
4.00 2
6.60 2
3.00 2
3.60 2
2.30 2
3.00 2
4.50 2
2.30 2
4.40 2
3.40 2
3.20 2
2.90 2
4.90 2
5.90 2
4.50 2
3.00 2
2.50 2
1.30 2
5.50 2
5.20 2
3.00 2
3.00 2
5.30 #DIV/0! 2
2.50 3
3.00 3
5.30 3
6.00 3
5.20 3
6.50 3
4.80 3
4.10 3
2.70 3
3.70 3
4.00 3
2.10 3
4.00 3
4.70 3
3.50 3
2.50 3
2.90 3
5.30 3
2.50 3
5.50 3
4.60 3
3.90 3
2.40 3
3.50 3
6.30 #DIV/0! 3
2.70 4
3.30 4
4.60 4
2.60 4
2.50 4
3.30 4
3.50 4
3.10 4
4.80 4
6.30 4
2.80 4
4.50 4
2.70 4
7.00 4
4.00 4
4.40 4
3.70 4
4.00 4
5.00 4
3.80 4
4.20 4
6.20 4
1.70 4
6.70 4
5.30 #DIV/0! 4
3.60 5
5.10 5
5.20 5
1.90 5
3.10 5
2.00 5
4.00 5
3.30 5
3.50 5
2.60 5
4.20 5
3.00 5
4.00 5
4.70 5
2.00 5
2.00 5
4.40 5
5.00 5
3.50 5
1.30 5
4.70 5
4.00 5
2.50 5
4.50 5
5.20 #DIV/0! 5
2.80 6
4.20 6
2.80 6
2.90 6
1.50 6
5.20 6
2.80 6
3.50 6
4.90 6
5.30 6
6.70 6
2.00 6
2.60 6
1.30 6
3.50 6
4.00 6
4.70 6
6.70 6
1.90 6
4.90 6
2.00 6
3.50 6
2.30 6
1.20 6
4.70 #DIV/0! 6
4.80 7
6.00 7
5.20 7
3.70 7
2.30 7
3.50 7
6.00 7
4.50 7
4.50 7
3.30 7
1.60 7
2.60 7
3.80 7
6.00 7
3.50 7
4.80 7
2.20 7
6.20 7
5.00 7
3.40 7
2.80 7
2.70 7
3.90 7
5.20 7
5.80 #DIV/0! 7
5.80 8
3.50 8
4.30 8
7.00 8
6.50 8
4.00 8
3.20 8
4.30 8
3.50 8
4.40 8
2.80 8
3.20 8
4.00 8
3.20 8
4.20 8
5.70 8
4.60 8
5.90 8
4.00 8
3.40 8
4.20 8
5.70 8
4.10 8
3.30 8
4.50 #DIV/0! 8
3.70 9
4.20 9
1.80 9
4.80 9
6.10 9
2.90 9
2.50 9
6.80 9
5.20 9
5.10 9
3.40 9
3.00 9
5.00 9
6.20 9
3.50 9
4.60 9
4.80 9
3.00 9
1.50 9
4.20 9
4.50 9
4.10 9
4.40 9
6.70 9
4.60 #DIV/0! 9
3.00 10
3.50 10
4.90 10
2.80 10
5.40 10
2.00 10
6.30 10
3.80 10
3.00 10
5.30 10
1.40 10
3.10 10
3.50 10
4.00 10
1.50 10
2.50 10
2.30 10
5.40 10
5.00 10
6.30 10
6.30 10
3.10 10
3.60 10
3.70 10
3.90 #DIV/0! 10
1.80 11
3.00 11
6.00 11
4.00 11
6.80 11
4.20 11
3.00 11
4.80 11
3.70 11
1.50 11
4.00 11
4.40 11
2.90 11
4.00 11
4.00 11
6.10 11
2.50 11
3.60 11
3.70 11
3.70 11
6.00 11
5.60 11
3.20 11
3.10 11
1.50 #DIV/0! 11
4.70 12
3.60 12
4.00 12
2.00 12
4.30 12
4.00 12
3.70 12
5.20 12
5.00 12
4.60 12
6.20 12
2.50 12
3.30 12
3.20 12
4.00 12
6.20 12
5.00 12
3.00 12
4.30 12
2.30 12
2.50 12
6.40 12
3.20 12
3.80 12
1.00 #DIV/0! 12
3.90 13
4.50 13
1.50 13
5.80 13
6.20 13
3.50 13
3.50 13
3.00 13
5.00 13
5.50 13
2.20 13
3.00 13
2.30 13
4.80 13
4.20 13
5.00 13
1.50 13
3.70 13
5.90 13
2.90 13
4.50 13
4.30 13
2.00 13
4.10 13
3.70 #DIV/0! 13
6.00 14
4.50 14
2.80 14
1.70 14
4.70 14
5.00 14
3.50 14
3.20 14
2.80 14
4.10 14
2.80 14
4.30 14
3.20 14
2.50 14
5.50 14
4.00 14
4.80 14
3.80 14
2.00 14
4.20 14
1.90 14
2.50 14
4.00 14
5.30 14
5.90 #DIV/0! 14
3.40 15
2.30 15
3.30 15
5.80 15
4.00 15
6.50 15
5.30 15
5.00 15
3.80 15
3.50 15
3.50 15
4.40 15
2.20 15
4.20 15
5.80 15
3.40 15
5.40 15
3.80 15
6.50 15
5.90 15
3.00 15
3.50 15
3.00 15
1.20 15
2.00 #DIV/0! 15
3.80 16
5.30 16
2.70 16
3.70 16
4.20 16
3.30 16
3.50 16
3.80 16
5.80 16
4.80 16
1.70 16
4.70 16
1.40 16
5.00 16
3.40 16
6.00 16
5.60 16
4.30 16
3.50 16
4.60 16
3.40 16
2.20 16
5.50 16
1.80 16
1.30 #DIV/0! 16
2.90 17
5.80 17
2.30 17
5.20 17
5.20 17
3.00 17
6.20 17
4.50 17
2.70 17
3.20 17
4.80 17
4.80 17
3.20 17
1.70 17
4.30 17
4.20 17
5.00 17
3.20 17
3.90 17
4.60 17
4.20 17
2.30 17
4.00 17
4.70 17
2.50 #DIV/0! 17
3.70 18
4.70 18
2.20 18
6.40 18
4.00 18
4.50 18
4.50 18
5.80 18
3.20 18
5.20 18
4.00 18
2.70 18
4.20 18
2.90 18
5.40 18
4.40 18
5.70 18
4.60 18
2.10 18
3.60 18
2.70 18
4.00 18
2.00 18
5.60 18
5.00 #DIV/0! 18
4.00 19
6.30 19
5.50 19
1.50 19
6.00 19
6.40 19
4.60 19
4.20 19
3.20 19
3.50 19
2.90 19
4.50 19
4.00 19
3.10 19
3.80 19
2.20 19
4.20 19
2.10 19
5.40 19
1.70 19
5.60 19
2.70 19
5.40 19
4.80 19
3.50 #DIV/0! 19
2.30 20
4.70 20
5.50 20
3.50 20
3.70 20
3.00 20
5.50 20
4.40 20
3.70 20
2.00 20
3.70 20
2.50 20
5.00 20
1.70 20
5.20 20
1.70 20
5.90 20
4.70 20
2.10 20
4.00 20
4.20 20
6.50 20
4.50 20
4.70 20
3.00 #DIV/0! 20
m weights and the Ye used for decision cutoff
Range of choices forcombo boxes on SubjectReport
0-10 0-100
10 100
9 95
8 90
7 85
6 80
5 75
4 70
3 65
2 60
1 55
0 50
45
40
35
30
25
20
15
10
5
0
0 =current subject
10 =number of cases
1 =number of current case
#VALUE! =Rs Task Development Spreadsheet
#DIV/0! =ra
Mean 4.980821 #DIV/0! 4.25 3.428571 3.678571 #DIV/0! #DIV/0! #DIV/0!
Std. Dev. 1.58958 #DIV/0! 2.369825 2.274526 2.204761 #DIV/0! #DIV/0! #DIV/0!
Case Ye Ys Cue 1 Cue 2 Cue 3 Cue 4 Cue 5 Cue 6
1 5 1 1 7
2 5 7 1 2
3 6 2 2 6
4 4 2 3 1
5 6 6 3 6
6 6 6 5 4
7 4 7 2 2
8 3 2 1 1
9 7 6 3 7
10 7 7 2 6
11 9 3 6 6
12 7 1 7 5
13 3 6 1 5
14 5 1 7 1
15 4 7 1 2
16 5 5 1 6
17 6 5 7 2
18 3 4 2 1
19 5 1 7 2
20 4 2 2 6
21 4 2 1 2
22 7 1 6 6
23 6 2 3 5
24 5 7 2 1
25 7 7 6 1
Example Cases
Ye Ys Cue 1 Cue 2 Cue 3
5 6 5 2
3 7 2 2
8 6 7 6
#DIV/0! #DIV/0! #DIV/0! #DIV/0!
#DIV/0! #DIV/0! #DIV/0! #DIV/0!
Cue 7 Cue 8 Cue 9 Cue 10
Comparison of your weights with ideal weights
The program has analyzed your
judgments from the last set to after completing set 1
determine how you tended to weigh
the three items of information. The Your weight Ideal weight
results are in the chart.
60
The ideal weights you are trying to
use are in purple. The weights you
50
generally used over the last set of
cases are in blue. We don't expect a
perfect match, but we hope you can 40
get close.
Weight
30
If your weights match the ideal
weights, continue practicing what
you are doing. If your weights don't 20
match up well, study the differences
and try to put more or less weight on 10
the right information.
0
Press Continue when you are ready
to go on. Clothing Emotionality
Information
ights with ideal weights
Ideal weight
Note to testers. If you
have judged fewer than
25 passengers, you
won't get the bars
showing your actual
weights.
Emotionality Bulging
Information
Comparison of your weights with ideal weights
The program has analyzed your
judgments from the last set to after completing set 1
determine how you tended to weigh
the three items of information. The Your weight Ideal weight
results are in the chart.
60
The ideal weights you are trying to
use are in purple. The weights you
50
generally used over the last set of
cases are in blue. We don't expect a
perfect match, but we hope you can 40
get close.
Weight
30
If your weights match the ideal
weights, continue practicing what
you are doing. If your weights don't 20
match up well, study the differences
and try to put more or less weight on 10
the right information.
0
Press Continue when you are ready
to go on. Emotional maturity Attention to detail
Information
ights with ideal weights
Ideal weight
Note to testers. If you
have judged fewer than
25 passengers, you
won't get the bars
showing your actual
weights.
Attention to detail Interpersonal skill
Information
#N/A Accuracy
Relative weight
Cue Beta Weight Your weight Ideal weight
Cue 1 #VALUE! #VALUE! 20.0
Cue 2 #VALUE! #VALUE! 50.0
Cue 3 #VALUE! #VALUE! 30.0
Cue 4
Cue 5
Cue 6
Cue 7
Cue 8
Cue 9
Cue 10
SumAbs #VALUE! #VALUE! 100
This is Elise's demographics sheet
Gender
Male
Female
Age
18 - 19
20 - 24
25 - 29
30 - 34
35 - 44
45 - 54
55 - 64
65 - 69
70 - 74
75 or older
Education
Less than high school
High school graduate
Some college
2-year college degree
4-year college degree
Some graduate school
Graduate degree
Favorite Baseball Team
Boston Red Sox
Boston Red Sox
Boston Red Sox
Boston Red Sox
Boston Red Sox
Boston Red Sox
Boston Red Sox
Mathematics
I have never had any math classes
I had the required high school math classes
I took more math than required in high school
I took some math in college
I majored in a quantitative course in college
Statistics
I have never taken a statistics course
I have taken a statistics course
I have taken at least 3 statistics courses
English Language
English is my first language
English is not my first language, but I'm fluent
English is my second language, and I'm still learning
Please Select from List Below
Please Select from List Below
Please Select from List Below
Please Select from List Below
Please Select from List Below
Please Select from List Below
Please Select from List Below
Weights Sum to 100
Cue1 50 33
Cue2 50 33
Cue3 50 33
150
Accuracy 50
Cutoff Value 5
Used cutoff? Yes
Percent Correct 50
Base Rate 50
Last cue
n n-1 n-2 n-3 n-4 n-5 n-6 n-7
b #VALUE! #VALUE! #VALUE! #VALUE! #VALUE! #VALUE! #VALUE! #VALUE!
se #VALUE! #VALUE! #VALUE! #VALUE! #VALUE! #VALUE! #VALUE! #VALUE!
r2 #VALUE! #VALUE! #VALUE! #VALUE! #VALUE! #VALUE! #VALUE! #VALUE!
F #VALUE! #VALUE! #VALUE! #VALUE! #VALUE! #VALUE! #VALUE! #VALUE!
ss #VALUE! #VALUE! #VALUE! #VALUE! #VALUE! #VALUE! #VALUE! #VALUE!
Space is reserved here for 10 cues. To change the LINEST formula, highlight all the cells,
Insert/formula/LINEST, and after filling out the form, press CTRL-SHFT-ENTER to enter the array
formula. You can also change the formula in the formula bar. X columns cannot contain blank da
n-8 n-9 n-10 Constant
#VALUE! #VALUE! #VALUE! #VALUE!
#VALUE! #VALUE! #VALUE! #VALUE!
#VALUE! #VALUE! #VALUE! #VALUE!
#VALUE! #VALUE! #VALUE! #VALUE!
#VALUE! #VALUE! #VALUE! #VALUE!
ormula, highlight all the cells,
ENTER to enter the array
X columns cannot contain blank data.
The proportion of positive decisions, determined by the average of
people who have completed partial feedback. 0.0000
0
= Proportion of feedback cases needed, based on mean number
of positive decisions made by subjects in partial feedback
= Reduced Feedback Control (O, no feedback, 1, feedback
Worksheet Form Action Result
Intro Run Runs Main, switches to Blank sheet, shows frmWelc
Blank frmWelcome OK frmMCPLinstructions
Blank frmMCPLinstructions OK set CaseNum = 0, frmPresentCases
Blank frmPresentCases OK Store Data, present next case, switch to frmFeedbac
Blank frmPresentCases Click case image Show frmCueHelp -- reminder of cue definitions
Start program from
INTRO worksheet CondR2
sub Main Low?
Module1MCPL Med?
High?
Initialize
Block = 0
Load training block of
cases into
TaskDataMCPL with
appropriate task R2 for
Block
Block = Block+1
Welcome and general
instructions
frmWelcome
Present training block
of cases with
appropriate task R2
Demographics form
frmPresentCases
frmDemographics
NO
Present cognitive
feedback. All
MCPL instructions
subjects must do 3
frmMCPLinstructions
blocks of cases.
Have all
blocks of
cases been
completed?
Yes.
Go to decision task.
to Blank sheet, shows frmWelcome
mPresentCases
ext case, switch to frmFeedbackBlock1
reminder of cue definitions
User form size to fill screen 576 768
Text box size for instructions and text pages 450 650
Background color of user forms White
Background color of buttons Black, white text
Size of buttons 30 90
Display of cues for each case 300 400
Screen size calculations for forms Screen resolution Form Sizs
DPI Width Height Width Height
XGA 96 1024 778 767.65 582.4
96 1400 1050 1049.521 786.0154
ESB19 120 1400 1050 839.6172 628.8123
Naming Condtions
RL,RM,RH R2 (Low, Medium, High)
BL,BM,BH Base Rate (Low, Medium, High)
VE,VN,VP Value structure (Equal, Greater penalty for false negatives, greater penalty for false po
FF,FP,FR Feedback (Full, conditional, partial) (formerly Full, partial,reduced)
Condition lookup tables
R2/Certainty
0.5 RL
0.7 RM
0.9 RH
Base Rate
0.1 BL
0.5 BM
0.8 BH
Value Structure
Equal VE
Greater penalty for false negatives VN
Greater penalty for false positives VP
Feedback
Conditional FP
Full FF
Partial FR
es, greater penalty for false positives
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