This is a brief overview for the EBM Biostat meeting at USF

1. Knowledge Structures
A Brief Primer on What We Know about What You Know.
L. Jo Elliott, Ph.D.
Assistant Professor
University of South Florida
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2. Without Knowledge…
• Decision Making is based on Knowledge
– What is known about the topic
– What is projected about the topic
– How the topic is framed
– And many, many more bits of knowledge….
– Goes into each and every decision every day
– We acquire knowledge about everything all the
time
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3. Outline
• Inception of knowledge inquiry
• Theories contributed to framing the “knowledge”
• Knowledge retrieval/aka Memory
• Categorization, learning, implicit and explicit memory
• Capturing Knowledge.
• What works and doesn’t work and why.
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4. Knowledge Structures
• Comparative Psychology
– Human Language does more than communicate
• Animals mimic (Bever, 2004).
• Humans use language to do many additional things- status,
social bonds, reason, decide,
• Developmental Psychology suggests…
– As language ability changes so does knowledge
• After 2 y. o. memories can be recalled instead of just
recognized. Process of knowledge manipulation begins.
– Memory processes are intimately connected to
language development, growth and loss.
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5. Psycholinguistics?
• Study of how language interacts, is organized, etc.
• Beginnings of Knowledge studies (Rumelhardt…).
• Why? Language = Knowledge
• Human Language is
– Iterative (it refers)
– Generative (each construction is novel)
– Not based entirely on stimulus response (I hate you
mommy).
– Thank you Noam Chomsky!
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6. Language |Knowledge
• Cannot have one without the other
• Language structures model knowledge
structures.
• Many theories…
– Categorical theories
– Parallel process theories
– Hierarchical process theories
– Combinations of these
– Other theories… etc… etc…
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7. Language is Categorical
• Memory may also be categorical
– Exemplar Model
• -Preconceived notion of the ‘perfect’ category member
– Prototype Model
• -hypothetical and most typical features21
– Evidence in Dementia- Semantic loss of a category
• Unable to name ‘fruit trees’, then ‘fruit’, then ‘fruit and
vegetables’ increasing loss by category.
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8. Categorical Viewpoint
• Knowledge is also categorical
– Tip of the tongue phenomena-
• category based, can name similar members of the category
but fail to retrieve the exact word
– Speech errors- category based (female, family
member, older… Jenny).
• Fewer members in category less errors
– Associations within a category are faster than
between
– Semantic distances and neighborhoods
– www.visualthesaurus.com “Gaelic”
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9. Chunking
• Language and knowledge can be chunked.
– HP, MS, VB, DoD
• As a person’s expertise increases so does
her/his chunking ability.
– The ability to retrieve the chunk is faster just like a
word- chunks (acronyms) act like words
– New knowledge- acronym can lose its original
meaning and other meanings are assigned to it.
• FPS, TOW, AGL, SOP
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10. Associations
• As language is learned…
– Semantics-meaning
• Children’s generalization errors- all four legged animals
are “dogs” or “cats”.
• As experience increases, semantic categories and
associations between and within categories is refined.
– Syntax- order of words determines meaning
• Syntax is processed before semantics- misspellins
• there, they’re and their
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11. Associations
• One word may have many
– associations (neighborhoods)
– categories (bins)
– syntactical uses.
• Experts will shorten communication by
chunking.
• Also acts as an “expertise” check. If you don’t
know the acronyms, it tags you as an outsider.
• SOAP, programmers
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12. Implicit Knowledge
• Latent, very difficult to access
– Know how Knowledge
– Procedural knowledge
– ‘motor memory’
– ‘everyone knows’ knowledge
• Assumed knowledge & heuristics within a
profession or domain.
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13. Explicit Knowledge
• Easy to access, people will forget but they can
tell you or draw a picture of it.
– “know that” knowledge
– Factual knowledge
– Relational knowledge
– Semantic knowledge
– ‘memories’
– So, how do we access this ‘knowledge’?
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14. Elicitation Methods
• Testing- different types- computer,
standardized…
• Interviewing
• Talk Aloud Procedure – tell me your thoughts
• Task Analysis
• Observation
• Critical Decision Making Method
• Two main avenues….
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15. Naturalistic Methods
– Interviewing/observation/talk to me
methodologies
– Strengths-
• People can talk about their knowledge
• What they cannot say you can infer through observing
their actions
– Weaknesses
• Knowledge has many different forms that may not
adapt to standard measurement procedures.
• We know additional knowledge is there
– cannot access.
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16. Computerized Testing
• Language based measurement
– MDSAs, latent models
– Assumption-
• Language is used to express and transmit knowledge
• The measurement of language equals a measurement of
knowledge
• Knowledge is discrete and definable
• Strengths-
– implicit knowledge is revealed, participant isn’t aware.
• Weaknesses-
– Limited to human input- only as good as the programmer.
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17. Pathfinder- MDSAs
• MDSA- Multi Dimensional Semantic
Association
• Assumptions-
– As a person gains knowledge in a particular area,
their knowledge representations become more
representative of an expert.
– This can be measured by word choice
– This can be measured by the speed of word choice
– Quantified by a distance measure
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18. Latent Models
• Latent Semantic Analysis/Latent Semantic
Indexing
– Compares given text to known text and calculates a
similarity score.
– Each word has a value that changes in relationship to
its syntax and semantic use in a sentence.
– Assumptions-
• The relationships between the words within a passage of
text represents knowledge.
• Many texts written by experts reflects what is known.
• closest approximation to complete knowledge.
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19. Examples
• Google vs. Alz.org for “Six Stages”
• Latent Semantic Indexing site.
• http://peterhoggan.hubpages.com/hub/latent
-semantic-indexing
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20. Take Home
• At least two types of knowledge
– Implicit- cannot be expressed
– Explicit- can be expressed
– As knowledge is practiced it can move from explicit to
implicit. Can move implicit to explicit but effortful.
• Knowledge Elicitation
– is a term describing a methodology
– captures a person’s knowledge base, structure, depth
or frequency.
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21. Take Home
• Language Based methods- MDSA, LSA
– Captures some explicit
– Very good at capturing implicit
• Naturalistic methods
– Very good at capturing explicit
– Captures some implicit, but with difficulty
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22. More Information
• Pathfinder
– http://www.sciencedirect.com/science/article/pii/
S002073738780086X
• Latent Semantic Analysis
– http://lsa.colorado.edu/papers/dp2.foltz.pdf
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