https://www.youtube.com/watch?v=b5qR4urr0vU&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=11 A mental representation or cognitive representation is a hypothetical internal cognitive symbol that represents external reality[1], or else a mental process that makes use of such a symbol: "a formal system for making explicit certain entities or types of information, together with a specification of how the system does this”[3]. To define the “Human Mental Representation”, four concepts have been described; Similarity, Analogy, Relationships at the Heart of Semantic Web. Similarity is defined as “learning information about one is generally true of the other”, and this becomes more and more true as the probability that the two causal/source variables is the same increases. The relationships used identifying similarities differs between experts and novices, with novices using surface features and experts using deeper structural relationships. Similarly, people relied on similarity mappings when the relational roles were more complex. The purpose of categorization is twofold, to be able to infer the properties of the entity and to adapt the category itself. This description is essentially Piaget’s theory of development through assimilation and accommodation. Communication is similar to categorization, but rather than resolving for oneself, the issue is resolving new or developing shared concepts between people, which relates to many of the psycholinguistic conceptual grounding discussions (i.e., Herb Clark). Analogy is a special kind of similarity. Two situations are analogous if they share a common pattern of relationships among their constituent elements even though the elements themselves differ across the two situations. Typically, one analog, termed the source or base, is more familiar or better understood than the second analog, termed the target” (p. 117). Therefore, theoretical models of analogical inference need to focus on binding and mapping. We explained the “Knowledge Representation”, and in the end, We provided the examples of “ Ontology and Knowledge Base” from Relationships at the Heart of Semantic Web p:15 [2]. References: 1- Chapters: 2, 3, 4, 6, Book: The Cambridge Handbook of Thinking and Reasoning (pp. 117-142). New York: Cambridge University Press. By By: Keith J. Holyoak and Robert G. Morrison 2- Relationships at the Heart of Semantic Web: Modeling, Discovering, and Exploiting Complex Semantic Relationships, Book Title: Enhancing the Power of the Internet. By Amit Sheth, Ismailcem Budak Arpinar, Vipul Kashvap 3- Marr, David (2010). Vision. A Computational Investigation into the Human Representation and Processing of Visual Information. The MIT Press. ISBN 978-0262514620

1. Human Mental Representations
Farahnaz Golroo
Kno.e.sis Center
Ohio Center of Excellence in Knowledge-Enabled Computing

2. Books:
1- Modeling Human Mental Representations
Book Title: The Cambridge Handbook of Thinking and Reasoning
By: Keith J. Holyoak and Robert G. Morrison
2- Relationships at the Heart of Semantic Web: Modeling, Discovering,
and Exploiting Complex Semantic Relationships
Book Title: Enhancing the Power of the Internet
By: Amit Sheth, Ismailcem Budak Arpinar, Vipul Kashvap

3. • Chapter 2 Similarity
Goldstone, R. L., & Son, J. Y. (2005). In K. J. Holyoak & R. G. Morrison (Eds.), The Cambridge Handbook of Thinking and Reasoning (pp.
13-36). New York: Cambridge University Press.
• Chapter 3 Concepts and Categories: Memory, Meaning, and Metaphysics
Medin, D.L., & Rips, L. J. (2005). In K. J. Holyoak & R.G. Morrison (Eds.), The Cambridge Handbook of Thinking and Reasoning (pp. 37-
72). New York: Cambridge University Press.
• Chapter 4 Approaches to Modeling Human Mental Representations: What Works,
What Doesn’t, and Why
Leonidas A. Doumas and John E. Hummed ). In K. J. Holyoak & R.G. Morrison (Eds.), The Cambridge Handbook of Thinking and
Reasoning (pp. 73-94). New York: Cambridge University Press.
• Chapter 6 Analogy
Holyoak, K. J. (2005). In K. J. Holyoak & R.G. Morrison (Eds.), The Cambridge Handbook of Thinking and Reasoning (pp. 117-142).
New York: Cambridge University Press.
1- Modeling Human Mental Representations

4. Concepts and Categories: Memory, Meaning
• Distinguishing between the use of concepts for communication and
categorization:
• The purpose of categorization is to infer the properties of the entity and to adapt the
category itself.

5. Concepts:
• In one sense, it is general mental image of the
objects/persons /events experienced or perceived
earlier.
• A concept is a generalization or abstraction from
experience or the result of a transformation of
existing ideas(Wikipedia).
• When the mind makes a generalization such as the
concept of tree, it extracts similarities from
numerous examples; the simplification
enables higher-level thinking.

6. Similarity
A concept is a generalization or abstraction from experience or the result of a
transformation of existing ideas(Wikipedia).
When the mind makes a generalization such as the concept of tree, it extracts
similarities from numerous examples; the simplification enables higher-level
thinking.

7. Similarity
• Learning information about one is generally true of the other.
• When two causal/source variables is the same, the probability of similarity increases
(If information is known about the outcome).
• Similarity is based largely on visual parameters, whereas categorization is rooted in
“biological, genetic, or historical knowledge.”
These definitions contrasted with evidence showing an interaction between similarity
and past experience/ context in category formation.

8. Human Mental Representations
• Mental Model: The model people
have of themselves, others, the
environment, and the things with
which they interact. People form
mental models through experience,
training and instruction.

9. Mental Representation
Figure 1 (adapted from Norman (1988) p. 16)`

10. Analogy
• An extended comparison between two things
(events, idea, people, etc).
• Show how two are related
• Builds a mental picture
• Describe something unfamiliar using
something familiar
• Analogy is a special kind of similarity.
Typically, one analog, termed the source or base,
is more familiar or better understood than the
second analog, termed the target”

11. Analogy vs Metaphor
Both are pertaining to a relationship between two things. So
where does the difference lie?
Metaphor: means to transfer.
Analogy : means proportion.
• She showered her with gifts.
• Fire is to hot as ice is to cold.

13. Relationships at the Heart of Semantic Web p:15 http://knoesis.org/sites/default/files/SAK02-TM.pdf
• The figure shows a simple ontology
containing information about Professors,
Students, Courses, Books, and Book Authors.
The top part of the figure shows the
description part of ontology which contains
the entity types (i.e., classes)
• The bottom part of the figure shows
assertion component of the ontology, i.e.,
instances of the classes, and dotted lines
illustrate instanceOf relations.

18. “Text and picture comprehension” (SCHNOTZ, BANNERT & SEUFERT, 2002, P. 349).

19. Semantic normalization
Relationships at the Heart of Semantic Web p:22 http://knoesis.org/sites/default/files/SAK02-TM.pdf
• “All the resources are mapped to this
integrated view and this helps to
resolve the source differences and
makes schema integration easier. An
example of “disaster” ontology is
shown in Figure 7.”
• Ontology provides a structured,
homogeneous view over all the
available data sources.
• It is used to standardize the
meaning, description and the
representation of the attributes
across the sources

20. Incremental Query Expansion to Multiple Ontologies
Relationships at the Heart of Semantic Web p:29 http://knoesis.org/sites/default/files/SAK02-TM.pdf
• If the user is not satisfied with the
answer, the system retrieves more data
from other ontologies in the Information
System to “enrich” the answer in an
incremental manner
Use of inter-ontological relationships to integrate multiple ontologies

29. Complex Relationship

30. Thank you