1. Lecture 13 – Psyco 350, B1 Winter, 2011
N. R. Brown, U of Alberta
Lecture 13 – Psyco 350, B1 Winter, 2011
N. R. Brown
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2. Implicit Memory & Real-World Estimation Semantic Memory
Outline
N. R. Brown, U of Alberta
Implicit Memory & Real-World Estimation
Semantic Memory
Network Models
Priming
Scripts & Schemata
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3. Brown & Siegler (1992): Method
N. R. Brown, U of Alberta
Materials: 100 countries
Participants: 24 CMU undergrads
Tasks:
Rate knowledge
Estimate population
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4. Brown & Siegler (1992): Results
N. R. Brown, U of Alberta
Brown & Siegler (1992): Results
Mean Rank-Order Correlations
True
Pop
NYT
Index
Est
Estimated Population
.41
.57
Rated Knowledge
.37
.70
.58
Important results:
As predicted, R(est w/ know) quite high (.58)
[R(est w/know) = .58] >> [R(est/true) = .41]
Interpretation:
pop-estimates based in availability-base intuitions
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5. Availability & Population Estimation
N. R. Brown, U of Alberta
Interpretation:
pop-estimates based in availability-base intuitions
An Alterative Interpretation:
People hold preexisting beliefs about the size of well-known countries.
These beliefs are biased by media coverage.
People infer that unknown countries are small.
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6. Availability & Population Estimation
N. R. Brown, U of Alberta
People can and do justify their estimates with reference to task relevant knowledge.
Size categories are often mentioned.
Comparisons w/ other countries also occur
Key question:
Are size categories retrieved or inferred?
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7. Phase 2 – estimate populations
Brown, Cui, & Gordon (2002)
N. R. Brown, U of Alberta
Aim: Determine whether population estimation is sensitive to priming, as Availability account predicts.
Method:
Phase 1 – rate knowledge
52 countries (primed set)
Phase 2 – estimate populations
52 primed countries & 52 unprimed countries*
* primed & unprimed sets matched for estimated pop, rated kn, actual pop, actual area & region
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8. Availability Prediction: Primed > Unprimed Results:
Brown et al. (2002): Results
N. R. Brown, U of Alberta
Availability Prediction: Primed > Unprimed
Results:
Primed: million
Unprimed: million
 million*
%  %
Interpretation:
knowledge ratings  availability/fluency in primed set
Availability/fluency influenced estimation process
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9. Another Example: Fatality Estimates
N. R. Brown, U of Alberta
Task: How many Canadians died of CauseX last year?
Results:
reasonable correlation between estimated & true fatality rate.
Availability Bias: holding true frequency constant, more vivid causes elicit  estimates
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10. Another Example: Fatality Estimates
N. R. Brown, U of Alberta
Task: How many Canadians died of CauseX last year?
Results:
reasonable correlation between estimated & true fatality rate.
Availability Bias: holding true frequency constant, more vivid causes elicit  estimates
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11. Importance of Availability
N. R. Brown, U of Alberta
Importance of availability differs across tasks.
___________________________________________________________________________________________
Determinants:
actual/perceived correlation between propx and memory
quantity & credibility of competing information
__________________________________________________________________________________________
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12. Importance of Availability
N. R. Brown, U of Alberta
Judgment/estimation tasks that (sometimes) display an availability bias:
recency (dates, recognition), truth, fatality rates, frequency, probability, corporate sales, wealth, population
Judgment/estimation tasks that do not display an availability bias:
age, distance, area, latitude, longitude
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13. Summary: Implicit Memory & Judgment
N. R. Brown, U of Alberta
Prior exposure (priming) facilitates subsequent processing (increased fluency).
People are sensitive to differences in fluency.
Fluency/availability (sometimes) treated as important cue to the value of target property.
This happens when:
prop x correlates w/ memory
other task-relevant information sparse/non-predictive
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14. Summary: Implicit Memory & Judgment
N. R. Brown, U of Alberta
Use of fluency/availability in judgment often produces biased performance
Reason: many factors impact availability.
For example: cultural & physical distance, economic power, & group conflict affect rated knowledge, but do not necessarily correlated w/ actual population
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15. Semantic Memory: Original Definition
N. R. Brown, U of Alberta
“Semantic memory is the memory necessary for the use of language. It is a mental thesaurus, organized knowledge a person possesses about words and other verbal symbols, their meaning and referents, about relations among them, and about rules, formulas, and algorithms for the manipulation of these symbols, concepts and relations.” (Endel Tulving, 1972, p. 36)
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16. Contents of Semantic Memory
N. R. Brown, U of Alberta
We “know” many, many things:
word meanings (< 20K), geography, history, sports, pop culture, genealogy, social relations, biological facts, etc, etc
Most of this knowledge:
easily & readily accessed
decontextualized
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17. Semantic Memory: Important Issues
N. R. Brown, U of Alberta
How is complex information…
acquired, revised, represented, organized, accessed, manipulated.
Some basic elements are understood in a general way.
semantic networks
spreading activation
schemata/scripts
Potential building blocks for a more complete understanding of the psychology of complex knowledge.
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18. Semantic Networks I: Collins & Loftus (1975)
N. R. Brown, U of Alberta
Semantic memory is organized as a network of interrelated concepts
Each concept is represented as a node
Concepts are linked together by pathways
“Length” of pathways reflects degree of semantic relatedness/ associative strength
Concepts close in meaning / highly related (e.g., red, fire) stored close together.
Unrelated concepts (red, street) are stored far away
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19. An Example of a Semantic Network
N. R. Brown, U of Alberta
An Example of a Semantic Network
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20. Spreading Activation & Semantic Networks
N. R. Brown, U of Alberta
Accessed node is a source of activation
Activation spreads from source to related nodes.
The amount of activation received by a given node is a function of path length.
The more active a concept is, the more readily it will be retrieved.
Implication: Retrieving a given concept should be easier when it is proceeded by a related concept than when it is not.
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21. An Example of a Semantic Network
N. R. Brown, U of Alberta
An Example of a Semantic Network
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22. Implication of spreading activation account:
Semantic Priming
N. R. Brown, U of Alberta
Implication of spreading activation account:
A given concept should be primed – responded to more rapidly/accurately – when it is preceded by a related concept than when it is not.
Therefore, lexical decisions should be faster when target preceded by related word than when not.
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23. Semantic Priming: Meyer & Schvaneveldt (1972)
N. R. Brown, U of Alberta
An early demonstration of Semantic Priming
Task: Lexical Decision -- with word pairs
Both items are word  Yes
One item is a non-word  No
Both items are non-words  No
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24. Semantic Priming: Meyer & Schvaneveldt (1972)
N. R. Brown, U of Alberta
Stimuli:
Associated word pairs: nurse-doctor
Unrelated word pairs: bread-doctor
word & non-word: bread-treb
non-word & word: treb-bread
non-word & non-word: treb-manty
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25. Meyer & Schvaneveldt (1972): Results
N. R. Brown, U of Alberta
Main Finding:
word-word pairs: associated RT < unassociated RT
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26. Spreading Activation:
N. R. Brown, U of Alberta
Spreading activation assumed automatic.
Do expectancies affect semantic access?
Can activation be control?
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27. Basic Premises: 2 components to priming Automatic component:
Neely (1977)
N. R. Brown, U of Alberta
Basic Premises: 2 components to priming
Automatic component:
fast, effortless, unaffected by intention/expectation
Controlled component:
Attentional, Slow, Effortful,
Benefits (if correct)
Costs (if incorrect)
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28. Goal: Contrast automatic & controlled priming Task:
Neely (1977)
N. R. Brown, U of Alberta
Goal: Contrast automatic & controlled priming
Task:
Lexical Decision -- Timed Word/Non-word Decision
Trial:
Prime  Target  Response
SOA RT
SOA = Stimulus Onset Asynchrony
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29. Expectation X Relation X SOA . No Shift 250 msec Shift 400 msec
Neely (1977): Design
N. R. Brown, U of Alberta
Prime-Target*
Expectation X Relation X SOA .
No Shift msec
Shift msec
700 msec
2000 msec
* see next slide
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30. No Shift Trials: See Bird as prime expect a bird name as target.
N. R. Brown, U of Alberta
No Shift Trials: See Bird as prime expect a bird name as target.
1. Neutral XXXX -- robin
2. No Shift BIRD -- robin
3. Shift (unexpected) BIRD -- arm
Shift Trials: See Building as prime, expect a Body Part as target.
1. Neutral XXXXX -- window
2. No Shift BUILDING -- window
3. Shift (expected) BUILDING -- leg
4. Shift (unexpected) BUILDING -- robin
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31. No Shift Trials: See Bird as prime expect a bird name as target.
N. R. Brown, U of Alberta
No Shift Trials: See Bird as prime expect a bird name as target.
1. Neutral XXXX -- robin
2. No Shift BIRD -- robin 80% primed trials
3. Shift (unexpected) BIRD -- arm 20% primed trials
Shift Trials: See Building as prime, expect a Body Part as target.
1. Neutral XXXXX -- window
2. No Shift BODY -- leg 10% primed trials
3. Shift (expected) BODY – window 80% primed trials
4. Shift (unexpected) BODY – robin 10% primed trials
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32. A Priming Effect: Neutral Trials - Primed Trials
Neely (1977): Results
N. R. Brown, U of Alberta
A Priming Effect: Neutral Trials - Primed Trials
Two Type of Priming Effects:
Facilitation Effects -- Positive Priming
Priming effect is positive -- Neutral > Primed
Inhibition Effect -- Negative Priming
Priming effect is negative -- Neutral < Primed
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33. Neely (1977) Results – NO Shift Expected
N. R. Brown, U of Alberta
No-shift, same-category pairs (Bird-robin):
Substantial facilitation at all SOA.
2. Shift, different-category pairs (Bird-arm):
Inhibition increases with SOA
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34. Neely (1977): Results – Shift Expected
N. R. Brown, U of Alberta
1. Expected Shift (BODY –door):
Facilitation increases with SOA
2. No-shift, same-category (BODY -- heart):
Facilitation at smallest SOA
Increasing inhibition at longer SOAs
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35. Neely (1977): Results – Shift Expected
N. R. Brown, U of Alberta
1. Expected Shift (Building-leg):
Facilitation increases with SOA
2. No-shift, same-category pairs (Building-window):
Facilitation at smallest SOA
Increasing inhibition at longer SOAs
3. Shift to unexpected category (BODY - robin)
Inhibition at all SOAs
Inhibition increases with SOA
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36. Neely (1977): An Explanation
N. R. Brown, U of Alberta
1. Automatic Spreading Activation:
Originates at prime, spreads to related concepts, decays rapidly.
2. Attention required to maintain activation over longer SOAs.
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37. Neely (1977): An Explanation
N. R. Brown, U of Alberta
3. Focusing attention on one category:
facilitates (primes) processing of category members
interferes with the processing (reading/word recognition) of items from other categories
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38. Neely (1977): An Explanation
N. R. Brown, U of Alberta
4. In the Shift-Expected condition, subjects shift attention to & maintain attention for cued category
It takes time to shift attention to new category.
Once attention is shifted, focus is on the new category.
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39. Neely (1977): An Explanation
N. R. Brown, U of Alberta
Shifting categories takes times.
Maintaining focus on indicated category:
facilities processing of focal category members
reduces attentional resources required to read & decide whether letter string is a word.
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40. Semantic Networks & Priming
N. R. Brown, U of Alberta
Semantic Network
general knowledge representation
based on relatedness, meaning-based similarity
Spreading Activation
automatic consequence of processing a related information
preparation for encountering the expected
Activated concepts sometimes equated w/ consciousness & focal elements of WM (Cowan)
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41. Complex, stable knowledge structures
Schemata & Scripts
N. R. Brown, U of Alberta
Schemata:
Complex, stable knowledge structures
occupations, geographical/architectural layouts, story structures, etc.
Scripts:
Schemata representing stereotypical event sequences
Assumption – this knowledge is represented in semantic memory & used extensive in planning, comprehension, and recall (reconstruction)
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42. Bartlett 1st to recognize importance of schemata.
N. R. Brown, U of Alberta
Bartlett 1st to recognize importance of schemata.
“War of the Ghosts” Study
English undgrads read a North American Indian legend twice.
Recalled the story once after 13 min and then over of the course of several month
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43. Bartlett – War of the Ghosts
N. R. Brown, U of Alberta
Main Findings:
Reproduction distorted in ways that brought the story increasing in lines with European:
narrative conventions
beliefs re: physical & biological causality
Interpretation:
Participants combined fragmentary story memory with schematic knowledge to reconstruct a “sensible” story.
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44. Schemata: General Findings
N. R. Brown, U of Alberta
When present:
Schema-consistent info, well remembered
Schema-inconsistent info, less well remembered.
When NOT present:
Schema-consistent info often falsely remembered (schema-driven reconstruction)
Schema-inconstant info generally not falsely remembered
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45. Recognizing Script-based Materials: Hannigan & Reinitz (2001)
N. R. Brown, U of Alberta
Schemata that capture general information about routine event sequences
Eating in a restaurant, attending a movie, a visiting a doctor’s office, attending class, going to the beach
Scripts identify central (& less central) actions & typical (& atypical) roles, & props.
When not specified (or experienced) central actions & typical roles & props inferred/reconstructed
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46. Hannigan & Reinitz (2001): Method
N. R. Brown, U of Alberta
Materials:
four 13-slide sequences
a sequence represented one script-based activity (e.g. grocery shopping)
including
HIGH schema-relevant items (e.g. get shopping cart)
LOW schema-relevant items (e.g., put food on belt)
Presentation: 5.5 s/slide
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47. Hannigan & Reinitz (2001): Method
N. R. Brown, U of Alberta
Hannigan & Reinitz (2001): Method
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48. Hannigan & Reinitz (2001): Method
N. R. Brown, U of Alberta
Test Phase:
0-to-5 Recognition Confidence Judgment on each slide:
0 = certain slide not seen
5 = certain slide was seen
Design:
Item Type X Schema-relevance X Delay
OLD high min
NEW low hr
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49. Hannigan & Reinitz (2001): Results
N. R. Brown, U of Alberta
For OLD items:
Reco very good
high > low
For NEW 13 min delay:
tendency to infer/reconstruct stronger for high-relevance items
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50. Hannigan & Reinitz (2001): Results
N. R. Brown, U of Alberta
Effect of Delay:
OLD items: memory still very good
False recognition  greatly for high-relevance items
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51. Semantic Memory: Main Points
N. R. Brown, U of Alberta
Semantic networks can represent simple facts and reflect conceptual similarity/relatedness
Semantic priming is well established process
serves to prime related information
Schemata/scripts – complex, stable knowledge structures
captures generalizations re: complex, but regular features of experience.
facilitate/bias perception & memory
Challenge:
Develop detailed extensions of these notions to deal with full range of knowledge domains & modalities
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