The Multiple-Strategies Strategy:
When It Works It REALLY Works
Norman R. Brown
University of Alberta
[email protected]
Edmonton, Alberta, Canada
54°N, 113° W
Working w/ Bob
•  1985-1989
– RSM, IBM Watson Research Center
•  Working to little, getting payed too much
•  1989 to 1992
– NIMH Fellow (1st two years)
•  Intersection of Interests
– Understanding and improving real-world (me)
quantitative estimation (Bob)
Working w/ Bob: Main Contributions
Insight:
•  Judgment as weighted blended of heuristicbased intuition & domain-specific knowledge
Theory
•  Metrics & Mappings Framework
Method
•  Seeding the Knowledge-Base
Publications w/ Bob
•  Brown, N. R. & Siegler, R. S. (2001). Seeds aren t anchors. Memory &
Cognition, 29, 405-412.
•  Brown, N. R. & Siegler, R. S. (1996). Long-term benefits of seeding the
knowledge-base. Psychonomic Bulletin & Review, 3, 385-388.
•  Brown, N. R. & Siegler, R. S. (1993). Metrics and mappings: A framework for
understanding real-world quantitative estimation. Psychological Review, 100,
511-534
•  Brown, N. R. & Siegler, R. S. (1992). The role of availability in the estimation
of national populations. Memory & Cognition, 20, 406-412.
•  Brown, N. R. & Siegler, R. S. (1991a). Subjective organization of U.S.
presidents. American Journal of Psychology, 104, 1-33.
•  Brown, N. R. & Siegler, R. S. (1991b). Understanding and improving realworld quantitative estimation. Proceedings of the Thirteenth Annual
Conference of the Cognitive Science Society (pp. 209-215). Hillsdale, NJ:
Erlbaum.
The Best Review!Ever
Brown & Siegler (1996)
Procedure:
•  Phase 1: Rate knowledge of 99 countries
•  Phase 2: Estimate pop. of 99 countries
•  Phase 3: Learn pop. of seed 24 countries
•  Phase 4: Re-estimation pop. of 99 countries
Wait 4 months
Phase 5: Re-estimate pop. of 99 countries.
Participants: 24 Carnegie-Mellon undergrads
Metric Improvement: Seeds & Transfer
Mapping Improvement: Just Seeds
Wait 4 Months
Seeds Forgotten
Transfer Countries: Metric ! Retained
Transfer Countries: Mapping knowledge Unaffected
Shameless
Plug
•  Seeding – efficient, effective method for
reducing real-world innumeracy
•  Seeding has not yet been tested in the
classroom.
• TRY IT!
Publications w/ Bob
•  Brown, N. R. & Siegler, R. S. (2001). Seeds aren t anchors. Memory &
Cognition, 29, 405-412.
•  Brown, N. R. & Siegler, R. S. (1996). Long-term benefits of seeding the
knowledge-base. Psychonomic Bulletin & Review, 3, 385-388.
•  Brown, N. R. & Siegler, R. S. (1993). Metrics and mappings: A framework for
understanding real-world quantitative estimation. Psychological Review, 100,
511-534
•  Brown, N. R. & Siegler, R. S. (1992). The role of availability in the estimation
of national populations. Memory & Cognition, 20, 406-412.
•  Brown, N. R. & Siegler, R. S. (1991a). Subjective organization of U.S.
presidents. American Journal of Psychology, 104, 1-33.
•  Brown, N. R. & Siegler, R. S. (1991b). Understanding and improving realworld quantitative estimation. Proceedings of the Thirteenth Annual
Conference of the Cognitive Science Society (pp. 209-215). Hillsdale, NJ:
Erlbaum.
Publications w/out Bob
• 
Schweickart, O. & Brown, N. R. (2014). Magnitude comparison extended: How lack of knowledge informs
comparative judgments under uncertainty. Journal of Experimental Psychology: General, 143, 273-294.
• 
Murray, K. & Brown, N. R. (2009). A feature-based inference model of numerical estimation: The split-seed
effect. Acta Psychologica, 131, 221-234.
• 
Friedman, A., Kerkman, D., Brown, N. R. , Stea, D., & Cappello, H. (2005). Cross-cultural similarities and
differences in North Americans geographical location judgments. Psychonomic Bulletin and Review, 12,
1054-1060.
• 
Kerkman, D., Friedman, A., Brown, N. R., Stea, D., & Carmicheal, A. (2003). The development of geographic
categories and biases. Journal of Experimental Child Psychology, 84, 265-285.
• 
Brown, N. R. (2002b). Real-world estimation: Estimation modes and seeding effects. In B. H. Ross (Ed.).
Psychology of Learning and Motivation: Vol. 41 (pp 321-360). New York: Academic Press.
• 
Brown, N. R., Cui, X., & Gordon, R. (2002). Estimating national populations: Cross-cultural differences and
availability effects. Applied Cognitive Psychology, 16, 811-827.
• 
Friedman, A., Brown, N. R., & McGaffey, A. (2002). A basis for bias in geographical judgements.
Psychonomic Bulletin and Review, 9, 151-159.
• 
Friedman, A., Kerkman, D., & Brown, N. R. (2002). Spatial location judgments: A cross-national comparison
of estimation bias in subjective North American geography. Psychonomic Bulletin and Review, 9, 615-623.
• 
Friedman, A., & Brown, N. R. (2000a). Reasoning about geography. Journal of Experimental Psychology:
General. 129, 193-219.
• 
Friedman, A., & Brown, N. R. (2000b) Updating geographical knowledge: Principles of coherence and inertia.
Journal of Experimental Psychology: Learning, Memory, & Cognition, 26, 900-914.
Publications Inspired by Bob
•  Uzer, T. & Brown, N.R. (2015). Disruptive individual experiences create lifetime periods: A study of autobiographical memory in persons with spinal cord
injury. Applied Cognitive Psychology, 29, 768-774.
•  Uzer, T., Lee, P. J., & Brown, N. R. (2012). On the prevalence of directly retrieved autobiographical memories. Journal of Experimental Psychology:
Learning, Memory & Cognition, 38, 1296-1308.
•  Brown, N. R. & Tan, S. (2011). Magnitude comparison revisited: An alternative approach to binary decision making under uncertainty. Psychonomic Bulletin
and Review, 18, 392-398.
•  Brown, N. R (2008). How metastrategic considerations influence the selection of frequency estimation strategies. Journal of Memory and Language, 58,
3-18.
•  Bogart, L. M., Walt, L. C., Pavlovic, J. D., Ober, A. J., Brown, N.R., & Kalichman, S. C. (2007) Cognitive strategies affecting recall of sexual behavior among
high-risk men and women. Health Psychology. 26, 787-793.
•  Brown, N. R., Williams, R. L., Barker, E.T., & Galambos, N. L. (2007). Estimating frequencies of emotions and actions: A web-based diary study. Applied
Cognitive Psychology, 21, 259-276.
•  Lee, P. J., & Brown, N. R. (2004). The role of guessing and boundaries in the telescoping of public events. Psychonomic Bulletin and Review, 11, 748-754.
•  Conrad, F., Brown, N. R., & Dashen, M. (2003). Estimating the frequency of events from unnatural categories. Memory & Cognition, 31, 552-562.
•  Brown, N. R. (2002a). Encoding, representing, and estimating event frequencies: A multiple strategy perspective. In P. Sedlmeier & T. Betsch (Eds.),
Frequency processing and cognition (pp. 37-53). Oxford: Oxford.
•  Conrad, F. G, Brown, N. R., & Dashen, M. (2000). Estimating the frequency of events from unnatural categories. American Statistical Association,
Proceedings of the Section on Survey Methods Research. Alexandria, VA: American Statistical Association.
•  Brown, N. R. & Sinclair, R. C. (1999). Estimating number of lifetime sexual partners: Men and women do it differently. Journal of Sex Research, 36, 292-297.
•  Conrad, F., Brown, N. R., & Cashman, E. (1998). Strategies for estimating behavioral frequency in survey interviews. Memory, 6, 339-366.
•  Brown, N. R. (1997). Context memory and the selection of frequency estimation strategies. Journal of Experimental Psychology: Learning, Memory, and
Cognition, 23, 898-914.
•  Conrad, F. G. & Brown, N. R. (1996). Estimating frequency: A multiple strategy perspective. In D. Herrmann, M. Johnson, C. McEvoy, C. Hertzog, & P.
Hertel (Eds.), Basic and applied memory: Research on Practical Aspects of Memory, Vol. 2 (pp. 167-178). Hillsdale, NJ: Erlbaum.
•  Brown, N. R. (1995). Estimation strategies and the judgment of event frequency. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21,
1539-1553.
•  Conrad, F. G. & Brown, N. R. (1994). Strategies for estimating category frequency: Effects of abstractness and distinctiveness. American Statistical
Association, Proceedings of the Section on Survey Methods Research (pp. 1345-1350). Alexandria, VA: American Statistical Association.
•  Conrad, F. G., Brown, N. R., & Cashman, E. (1993). How the memorability of events affects frequency judgments. American Statistical Association,
Proceedings of the Section on Survey Methods Research, Volume 2 (pp. 1058-1063). Alexandria, VA: American Statistical Association.
Actually Inspired by Siegler (????)
•  Uzer, T. & Brown, N.R. (2015). Disruptive individual experiences create lifetime periods: A study of autobiographical memory in persons with spinal cord
injury. Applied Cognitive Psychology, 29, 768-774.
•  Uzer, T., Lee, P. J., & Brown, N. R. (2012). On the prevalence of directly retrieved autobiographical memories. Journal of Experimental Psychology:
Learning, Memory & Cognition, 38, 1296-1308.
•  Brown, N. R. & Tan, S. (2011). Magnitude comparison revisited: An alternative approach to binary decision making under uncertainty. Psychonomic Bulletin
and Review, 18, 392-398.
•  Brown, N. R (2008). How metastrategic considerations influence the selection of frequency estimation strategies. Journal of Memory and Language, 58,
3-18.
•  Bogart, L. M., Walt, L. C., Pavlovic, J. D., Ober, A. J., Brown, N.R., & Kalichman, S. C. (2007) Cognitive strategies affecting recall of sexual behavior among
high-risk men and women. Health Psychology. 26, 787-793.
•  Brown, N. R., Williams, R. L., Barker, E.T., & Galambos, N. L. (2007). Estimating frequencies of emotions and actions: A web-based diary study. Applied
Cognitive Psychology, 21, 259-276.
•  Lee, P. J., & Brown, N. R. (2004). The role of guessing and boundaries in the telescoping of public events. Psychonomic Bulletin and Review, 11, 748-754.
•  Conrad, F., Brown, N. R., & Dashen, M. (2003). Estimating the frequency of events from unnatural categories. Memory & Cognition, 31, 552-562.
•  Brown, N. R. (2002a). Encoding, representing, and estimating event frequencies: A multiple strategy perspective. In P. Sedlmeier & T. Betsch (Eds.),
Frequency processing and cognition (pp. 37-53). Oxford: Oxford.
•  Conrad, F. G, Brown, N. R., & Dashen, M. (2000). Estimating the frequency of events from unnatural categories. American Statistical Association,
Proceedings of the Section on Survey Methods Research. Alexandria, VA: American Statistical Association.
•  Brown, N. R. & Sinclair, R. C. (1999). Estimating number of lifetime sexual partners: Men and women do it differently. Journal of Sex Research, 36, 292-297.
•  Conrad, F., Brown, N. R., & Cashman, E. (1998). Strategies for estimating behavioral frequency in survey interviews. Memory, 6, 339-366.
•  Brown, N. R. (1997). Context memory and the selection of frequency estimation strategies. Journal of Experimental Psychology: Learning, Memory, and
Cognition, 23, 898-914.
•  Conrad, F. G. & Brown, N. R. (1996). Estimating frequency: A multiple strategy perspective. In D. Herrmann, M. Johnson, C. McEvoy, C. Hertzog, & P.
Hertel (Eds.), Basic and applied memory: Research on Practical Aspects of Memory, Vol. 2 (pp. 167-178). Hillsdale, NJ: Erlbaum.
•  Brown, N. R. (1995). Estimation strategies and the judgment of event frequency. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21,
1539-1553.
•  Conrad, F. G. & Brown, N. R. (1994). Strategies for estimating category frequency: Effects of abstractness and distinctiveness. American Statistical
Association, Proceedings of the Section on Survey Methods Research (pp. 1345-1350). Alexandria, VA: American Statistical Association.
•  Conrad, F. G., Brown, N. R., & Cashman, E. (1993). How the memorability of events affects frequency judgments. American Statistical Association,
Proceedings of the Section on Survey Methods Research, Volume 2 (pp. 1058-1063). Alexandria, VA: American Statistical Association.
Actually Inspired by Siegler (1987)
The Multiple-Strategies Strategy at Work
3 Demonstrations
1.  Telescoping Bias in Date Estimation
2.  Voluntary Retrieval of Autobiographical
Memories
3.  Estimation of Event Frequencies (plus Sex)
Telescoping Bias in Date Estimation
In collaboration w/ Peter J. Lee
Event Dating Biases
Biasing Effects in Date Estimation
2009
2007
2006
2005
2004
True Year
09
20
08
20
07
20
06
20
05
20
04
20
03
2003
20
Estimated Year
2008
Event Dating Biases
Backwards Telescoping
2009
Estimated Year
2008
2007
2006
2005
2004
09
20
08
20
07
20
06
20
05
20
04
20
20
03
2003
True Year
Event Dating Biases
Forwards Telescoping
2009
2007
2006
2005
2004
True Year
09
20
08
20
07
20
06
20
05
20
04
20
03
2003
20
Estimated Year
2008
The Boundary Effects Model
Huttenlocher, Hedges & Prohaska, 1988
• Assumption 1: Unbounded estimates are unbiased.
James Byrd dragged to death by white
racists in Jasper, Texas: May 1998
Frequency of Responses
100
Mean est. date = true date
80
60
40
20
0
-10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
Estimated Date: Years From the Present
The Boundary Effects Model
Frequency of Responses
•  Assumption 2: Boundaries truncate variance, resulting in
a net forward bias
•  Assumption 3: Variance increases with event age
Note – Single-Process Account
Boundary
= May 1997
Forward biasing of
mean estimated
date
Estimated Date: Years From the Present
The Multiple Strategies Perspective
•  Quantitative estimation often involves different cognitive
strategies (e.g. Brown, 1995; Siegler, 1987)
•  Different strategies can effect the type and strength of
estimation bias
•  Misinterpretation from collapsing over strategies
two strategies: Guessing
and Reconstruction (or knowledge based
inferences)
•  This study focuses on
Boundary Dependent Guessing
•  Assumption 1: Guesses approximate the middle of the
range
Percentage of Zero Knowledge
Responses
Frequency of Guessed Responses by
Estimated Year
Estimated Date: Years From the Present
Boundary Dependent Guessing
•  Assumption 2: There’s a lot of guessing going on
•  Assumption 3: That non-guessed responses are
reconstructed & not particularly biased (e.g. Brown, 1990;
Friedman, 1993; Thompson et al., 1996)
•  Assumption 4: Non-guessed responses are unaffected by
boundaries
Experimental Overview
•  Event age held constant: Participants estimate the same
events
•  Boundary manipulations: 4 conditions:
boundaries (true boundary) !
! ! + unbounded
Predictions
All Responses: As boundaries recede
to the past….
All Responses
2002
1999
02
20
00
01
20
19
No Interaction
20
1996
99
Main Effect of True Year
19
1997
96
Main Effect of Condition
98
1998
19
Backwards telescoping #
2000
97
Forward Telescoping !
2001
19
•  Accuracy "
Median Estimated Date
Both Models
True Year
Predictions
Guesses Responses: As boundaries
recede to the past….
Guessed Responses
2002
1999
No Interaction
True Year
2
20
0
1
20
0
0
19
9
20
0
1996
9
No Main Effect of True Year
19
9
1997
6
Main Effect of Condition
8
1998
19
9
Backwards telescoping #
2000
7
Forward Telescoping !
2001
19
9
•  Accuracy !
Median Estimated Date
Both Models
Predictions
Knowledge based responses:
As boundaries recede to the past….
Knowledge Driven Responses
2002
1999
1998
1997
Sig. Boundary x True Date
Interaction
02
20
01
20
00
20
99
19
19
19
96
1996
98
Backwards telescoping "
2000
19
Forward Telescoping !
2001
97
•  Accuracy #
Median Estimated Date
B. E. M.
True Year
Predictions
Knowledge based responses:
As boundaries recede to the past….
Knowledge Driven Responses
2002
1999
No interaction
02
20
01
00
True Year
20
19
20
1996
99
No main effect of Cond.
19
1997
96
Main effect of True Date
98
1998
19
Backwards telescoping "
2000
97
Forward Telescoping "
2001
19
•  Accuracy "
Median Estimated Date
B. D. G.
Procedure
•  Task 1:
•  Sequential presentation of 64 news events (Jan. 1997 Jun. 2001).
•  Participants rate knowledge for events using a 0-8 scale.
Zero = “never heard of the event”
Eight = “know almost all the event’s salient details”
Example: Russian space station Mir crashes back to earth in
S. Pacific, Mar. 2001
Procedure
•  Task 2:
•  Events shown again, P’s date events to the nearest month
and year.
•  P’s
• 
• 
• 
• 
assigned to one of four conditions:
Condition 1: Jan. 1997 (true boundary)
Condition 2: Jan. 1994
Condition 3: Jan. 1991
Condition 4: Unbounded
•  256 participants tested between Oct. 2001 and Dec. 2001.
•  Participants tested individually. Materials presented/data
collected by computer
Data Reduction and Analysis
•  Guessing: Prevalence of guessing and
the distribution of guessed responses
•  Percentage of responses eliciting “zero”
knowledge
•  Distribution of “zero” knowledge
responses by estimated year
•  Biasing: All events collapsed by true
year
•  The biasing associated with guessed and
non-guessed responses and their effect on
overall performance
Results: Event Knowledge
Guessing Functions by Condition
Percentage of Guessed Responses
35
30
25
•  37% of responses
elicited zero
knowledge ratings.
True Mid-Point
20
15
10
5
1
0
20
0
9
20
0
8
Estimated Year
19
9
7
19
9
6
19
9
5
19
9
4
19
9
3
19
9
2
19
9
1
19
9
19
9
19
9
0
0
Results: Biasing
The Effects of Guessing and Boundaries on Telescoping
All Responses
Median Estimated Date
2002
2001
2000
Main Effect of Condition
F = 11.2, p < 0.001
1999
Main Effect of True Year
F = 179, p < 0.001
1998
1997
No interaction
F = 0.82, p < 0.63
02
20
01
20
00
20
99
19
98
19
97
19
19
96
1996
True Year
Results: Biasing
The Effects of Guessing and Boundaries on Telescoping
Guessed Responses
2001
2000
Main Effect of Condition
F = 2.76, p < 0.04
1999
No Main Effect of True Year
F = 0.67, p < 0.56
1998
1997
Sig. interaction
F = 2.23, p < 0.01
True Year
20
02
20
01
20
00
19
99
19
98
19
97
1996
19
96
Median Estimated Date
2002
Results: Biasing
The Effects of Guessing and Boundaries on Telescoping
Knowledge Driven Responses
Median Estimated Date
2002
2001
2000
No Main Effect of Condition
F = 0.73, p < 0.53
1999
Main Effect of True Year
F = 37, p < 0.001
1998
1997
No interaction
F = 1.04, p < 0.408
02
20
01
20
00
20
99
19
98
19
97
19
19
96
1996
True Year
Results: Biasing
Guessed Responses
02
01
20
00
20
19
20
20
20
19
19
19
19
True Year
99
1996
20
1996
98
1996
19
1997
19
1997
96
1997
02
1998
01
1998
00
1998
99
1999
98
1999
97
1999
96
2000
20
02
2000
20
01
2000
20
00
2001
19
99
2001
19
98
2001
19
97
2002
97
Non-Guessed Responses
2002
19
All Responses
2002
19
96
Median Estimated Year
The Effects of Guessing and Boundaries on Telescoping
Conclusions
•  Different cognitive strategies account for
different telescoping biases
•  Biased Guessing accounts for forwards telescoping
• Boundaries are numerically informative
• Forward telescoping – an artefact of biased
guessing
•  No support for the Boundary Effects Model
Voluntary Retrieval of Autobiographical
Memories
In collaboration w/ Tugba Uzer & Peter J. Lee
Context
•  Two processes used to retrieve
autobiographical memories
– Generation – effortful, time consuming
– Direct retrieval –automatic, rapid
•  General Assumption:
– Generation (almost) always required for
deliberate retrieval
•  Significance: Reconstruction central process in
dominant theory, Conway’s SMS Model
Uzer, Lee, & Brown (2012)
•  Aim: Assess frequency of direct retrieval
•  General Method:
– Timed retrieve AM in response cue word
– Provide immediate post hoc-strategy report
– Input event memory
•  Cues:
– 10 object terms
– 10 emotion terms
•  Previous research – RT: objects < emotions
Three Experiments
Exp
Concurrent
Verbal
Protocol
Strategy-menu Wording
YES
Response
1
Yes
Did this memory come immediately to mind?
Direct
2A
No
Did this memory come immediately to mind?
Direct
2B
No
Did you actively searched in order to find a
memory?
3A
No
This memory was triggered by the cue word,
so I did not have to use information about my
life to help me recall this memory.
Direct
3B
No
This memory wasn t triggered by the cue
word, so I had to use information about my life
to help me recall this memory.
Generative
Generative
Wording of strategy menus manipulated to gauge
task demands
Replicate Cue-type Effect
RTs: Objects < Emotions
Direct Retrieval
Direct Retrieval:
•  Very Common
•  Objects > Emotions
RTs: Direct Retrieval << Generation
Memory Retrieval: Additional Points
•  Personally Relevant Cues: Dir Ret $ 80%
•  Replicated across several labs
•  Under-cut “constructionist” theories of
autobiographical memory
•  Unites research on voluntary & involuntary
memory memories
Multiple-Strategies Perspective
on Event Frequency
Collaborators: Fred Conrad & Bob Sinclair
Studying Event Frequency
•  Theoretical Interest
– How does repetition affect memory?
– Why are frequency estimates so “accurate”?
•  Applied Interest
– “Behavioral frequency” questions common on
surveys
– Assess accuracy & bias of responses
Studying Frequency Estimation in the Lab
Evidence for MSP: Brown (1995)
Two Experiments:
•  common materials
•  common estimation task
•  different process-based based measures
– Experiment 1 – concurrent verbal protocols
– Experiment 2 – RT
Materials & Task
Study Phase:
260 word pairs
visual presentation: 6 s / pair
Word Pairs:
category label – category instance
CITY – Boston
Presentation Frequency of category labels:
2, 4, 8, 12, 16
Test Phase: 36 category labels
“How many times did the word CITY appear on the study
list”?
Two Types of Study Lists:
Different Context & Same Context
Different
Context Lists
Target Context
city
Paris
sport
football
color
red
.
.
.
.
color
green
city
Cleveland
metal iron
.
.
.
.
woman Mary
city
Boston
.
.
.
.
Same
Context Lists
Target
Context
city
Boston
sport
football
color
red
.
.
.
.
color
red
city
Boston
metal iron
.
.
.
.
woman Mary
city
Boston
.
.
.
.
Results: Process Measures
Enumeration
Impressions
Unjustified
Different
Context
57%
20%
25%
Same
Context
--24%
69%
•  Different Context: enumeration very common
•  Same Context: unjustified very common –
–  fluency/availability-based estimates
Results: Protocol Results
Interpretation:
•  People enumerate when possible because it provides
concrete credible basis for an estimate
•  Readily-retrieval instances are a precondition for
enumeration.
Alternative Interpretation:
•  People generally do not enumerate
•  In protocol study: enumeration common because task
demands imply that participants SAY something relevant to
justify their response.
–  possible in different context condition
–  not possible in same-context condition
Results: Process Measures
Different Context:
•  RTs ! frequency
•  evidence for silent
enumeration
Same Context:
•  RT slope much
shallower.
•  no enumeration
Protocol Study
RT Study
Different Context " underestimation
Same Context " overestimation
Enumeration w/ Available of Retrieval Instances
Brown, 1997
Experimental Summary
Multiple strategies
Strategy selection restricted by memory contents
Bias & Strategy related:
•  enumeration #underestimation
•  rough aprox #overestimation
Multiple Strategies Perspective
Multiple Strategy Perspective
•  Encoding factors
determine task-relevant
contents of memory.
•  Contents of memory
restrict strategy
selection.
•  Strategy selection and
response bias often
related.
Multiple Strategies Perspective
•  Numerous estimation strategies identified
– Lab-strategies subset of real-world strategies
•  Enabling conditions identified
•  Selection among competing strategies difficult
to predict.
A Taxonomy of Estimation Strategies
Relating Encoding, content,
strategy & Performance
encoding
content
‘memorable’
events
‘on-target’
instances
regularity
rate
rate retrieval
$ fast, flat RT
$ heaping
intent
tally
tally retrieval
$ fast, flat RT
$ accurate(?)
frequent
presentation
vague
quantifier
impression retrieval
$ fast, flat RT
$ overestimation
memory assessment
$ fast, flat RT
$ overestimation
indistinct
instances
encoding/test
mismatch
fluency
‘off-target’
instances
Strategy
performance
on-target enumeration $ RT ! freq
$ underestimation
$ SLOW, flat RT
off-target enumeration $ regressive estimates
Applying the MSP
The Partner Discrepancy
The Discrepancy
!s report far more opposite-sex
SP*s than "s
Magnitude:
•  2 X – 4X
Generality:
•  US, UK, France, Canada, Norway, New
Zealand
*sexual partners
SP Discrepancy as Case Study:
Explanations
Sampling
Response
Social
Cognitive
Lifetime Partner Discrepancy from the MSP
Multiple strategies:
•  Enumeration
•  Rough Approximation
•  Others(?)
Strategy & magnitude related.
•  enumeration < rough aprox
Strategy selection related to sex
">!
Rough Aprox: ! > "
•  Enumeration:
• 
A Questionnaire Study: Brown & Sinclair (1999)
Method:
•  Demographics
•  SP reports:
– lifetime estimate & written strategy report
– past-year estimate & written strategy report
•  Attitude measures
A Questionnaire Study: Brown & Sinclair (1999)
Participants:
University Students: AB, PA, NJ
1036 "
687 #
Age: M = 20.7
MD = 19
Percent of Participants
Distribution of SP Estimates -- AlbertaQ
40
35
Men
Women
30
25
Mean SP
3.5
2.4
MD SP
1
1
20
Men
Women
15
10
5
0
0
5
10 15 20 25 30 35 40 45 50 > 50
Estimated Number of Lifetime SPs
Sexual Active Subset -- AlbertaQ
•  Most active 10%; SP % 8
•  Heterosexual
•  90 Females
Age: md = 22
SPs: m = 13.61
•  85 Males
Age: md = 23
SPs: m = 19.91
Percent of Participants
SP Est– Sexaul Active Subset -- AlbertaQ
2.31
Men
Women
1.98
1.65
Mean SP
19.9
13.6
MD SP
15
12
1.32
Men
Women
0.99
0.66
0.33
0.00
10
15
20
25
30
35
40
45
50
Estimated Number of Lifetime SPs
Protocol Content – AlbertaQ
> 50
Sample Protocols -- AlbertaQ
Enumeration (Retrieve & Count)
•  "By retracing chronologically the partners I've had.
Beginning with the first, ending with the present." -M, 20
•  "I recalled and counted."
-- F, 18
•  "Counted all the names I remembered." -- F, 11
•  "I can recall who they were and can count them." -F, 15
Sample Protocols -- AlbertaQ
Rough Approximation
•  “Rough guess, give or take 1 or 2 partners." -- M, 16
•  "Rough estimate plus-or-minus error 5" -- M, 20
• "I used to keep count. # has slowed down is likely
about there" -- M, "30 (or so)“
•  "It is a guess based on the amount of partners I have
had at the minimum.“ -- M, 50
Sample Protocols -- AlbertaQ
Retrieved Tally
•  "Keep track of them as they occurred." -- M, 21
•  "I know the number without thinking as it has
been previously discussed among friends“-- M,
10
•  "I didn't estimate. I've kept count."-- F, 11
•  "I kept track in my diary and I know that my
boyfriend is #27." -- F, 27
Sample Protocols -- AlbertaQ
Rate
•  "Avg of 5/year from 16-21, then remained
monogamous." -- M, 25
•  "The average length of relationship since the time I
became sexually active." -- M, 20
Ambiguous/Unclear
•  "Memory." -- M, 22“
•  “I remember them."-- M, 10
Strategy Usage – Sexual Active – AlbertaQ
Percent of SA Participants
60
55
50
45
Men
Women
40
35
30
25
20
15
10
5
0
EN
TA
AP
AM
EN = ENumeration TA = TAlly AP= rough APproximation AM = AMbiguous
MD Lifetime SPs X Strategy Sexual Active
AlbertaQ
Men
Women
32
MD Lifetime SP
28
24
20
16
12
8
4
0
EN
TA
AM
AP
EN = ENumeration TA = TAlly AM = AMbiguous AP= rough APproximation
Replications
•  US-Based Random Sample Surveys
– Telephone; n =1427
– Web-Base: n =1692
•  Procedure:
–  Estimate # SP
–  Select strategy from menu
Mean SP
All SeH
27
Mean SPs
24
Phone
27
Web
24
21
21
18
18
15
15
12
12
9
9
6
6
3
3
0
0
F
M
F
M
Col 2: 5.27
Col 2: 17.83
Col 2: 11.29
Col 2: 21.96
Tr SeH
Outliers (Se Hs)
Telephone Sample
Web Sample
3000
2000
1000
750
Reported SPs
500
250
100
75
50
40
30
20
10
7
5
3
1
Women
Men
Women
Men
Strategy Selection: Sex Differences
Telephone Survey
Women
45
Men
***
40
35
30
25
er
e
at
O
th
O
th
R
Ta
En
Ap
at
Reported Strategy
lly
0
R
0
*
x
5
um
5
er
10
e
10
lly
15
um
15
ro
x
20
ow
20
***
ro
25
***
En
***
ow
30
Ta
35
Ap
40
Web Survey
***
Kn
45
55
50
50
Kn
Percentage of All Responses
55
Relation between Strategy & SPs: Sex Differences
Web Survey
Telephone Survey
***
20
Median Estimated SP
Women
Men
Women
Men
18
16
14
12
10
***
8
***
6
4
***
2
er
th
O
um
En
x
ro
Ap
ow
Kn
r
th
e
O
um
En
x
ro
Ap
Kn
ow
0
Reported Strategy
Partner Discrepancy: Conclusions
•  Multiple Strategies used.
– ! favor rough approximation
– " favor conservative strategies
•  MSP partial account of Partner Discrepency
Partner Discrepancy: Conclusions
•  Three-pronged Account necessary
– Direct evidence for
•  Strategy Differences
•  Social Desirability
– Sampling
•  PSW – “conspicuous by their absence”
General Conclusions
•  Real-world knowledge is complex.
•  Ubiquitous individual differences:
– knowledge
– motivation
•  Default assumption:
– Multiple strategies ALWAYS at play
General Conclusions
•  The Burden:
– Identify strategies
– Specify their conditions/characteristics
– Alas#not ALWAYS possible
•  The Consequences
– Multiple Strategies Strategy not always
applicable
•  But, when it works, it really works.
Thanks for everything, Bob!
There are indeed many ways
to skin a cat