The document discusses various models of how knowledge is represented and organized in semantic memory. It describes semantic network models including feature comparison models, Collins and Quillian's network model of a hierarchical semantic structure, and spreading activation theory. It also discusses propositional models such as HAM and ACT-R that represent knowledge as propositions connected in a network.

1. Representation of knowledge

2. • The Representation of Knowledge: Semantic
organization –Associationistic approach
• Semantic memory model – Set theoretical model –
Semantic feature – Feature Comparison model –
Network model – Propositional model networks.
Representation of Knowledge – Neurocognitivie
consideration – Connectionism and the
Representation of Knowledge.

3. Introduction
• Science is organized knowledge
• Knowledge is “the storage, integration and
organization of information in memory.”
• some estimates place the no. of words a person know
the meaning of at 20,000 to 40000.
• Words derive their vitality not from some intrinsic
worth, but from the concepts and relationships that
they reflect.

4. How is knowledge stored in memory ?
• For e.g. a bus schedule can be represented in the form
of a map or a timetable.
• On the one hand, a timetable provides quick and easy
access to the arrival time for each bus, but does little
for finding where a particular stop is situated.
• On the other hand, a map provides a detailed picture
of each bus stop's location, but cannot efficiently
communicate bus schedules.
• Both forms of representation are useful, but it is
important to select the representation most
appropriate for the task at hand.

5. Components of Representation of Knowledge
Cognitive scientists are concerned with
• Content of knowledge representation
• Structure of knowledge representation
• Process of retrieval
Two perspectives
• Associationism – the doctrine that there are
functional relationships between psychological
phenomenon.
• Connectionism – a theory of the mind that posits
a large set of simple units connected in a parallel
distributed network, (PDP).

6. Semantic organization of knowledge
• Normally conceptualized (as in clustering model) as a
grouping or clustering of elements that are alike in
meaning – For e.g. Rajendra prasad, V.V.giri, Zail
singh, R.Venkatraman, Abdul kalam, pratibha patel.
• The above group can be further classified as : Rajendra
prasad, Zail singh, Pratibha patel (North Indians);
V.V.giri, R.Venkatraman, Abdul kalam (South
Indians)
• Further semantic models deal with relationship of
concepts to one another – For e.g. Kalam was a rocket
scientist, Kalam is a tamilian, Kalam has a bizarre
hairstyle, kalam is a muslim etc.

7. Associationist approach
• View of human intellectual function as a
network of associations connected to other
associations and to other associations and so
on.
• Gordon bower believed organization of
semantic entities in memory has much more
powerful influence on memory and recall.
• Experiment – constructed several conceptual
hierarchies and demonstrated the potent
influence on recall of organizational variables.

8. • Bower et at (1969) also demonstrated the
power of organisation.
• Subjects had 112 words to learn, presented in
4 trials, 28 words a trial.
• Half the subjects were presented with the
words organised into conceptual hierarchies
(see Figure 1), the other half were simply
shown lists of words.
• They found that subjects presented with the
organised lists remembered around 47%
more words than subjects presented a list of
words without organisation

9. conceptual hierarchies for the
word vehicle

11. Semantic memory :cognitive models
• A semantic network is a method of representing
knowledge as a system of connections between
concepts in memory
• knowledge is organized based on meaning, such
that semantically related concepts are
interconnected.
1. Set-theoretical model
2. Feature Comparison Model
3. Network models
1. Collins & quillian
2. Spreading activation theory – Collins and loftus
4. Propositional networks
1. HAM
2. ACT*

12. Set-theoretical model
• Semantic concepts are represented by sets of
elements, or collections of information.
• A word that encompasses a concept may be
represented not only by exemplars of the concept but
also by its attributes.
• For e.g. the concept of birds may include the names
of types of birds – crow, robin, canary, sparrow,
parrot and so on – as well as the attributes of the
concept – sings, flies, has feathers.

13. Set-theoretical model
• Memory consists of numerous sets of attributes
and retrieval involves verification – a search
through two or more sets of information to find
overlapping exemplars.
• Verification of propositions (e.g. robin is a bird) is
done by comparing the attributes of one set (robin)
with the attributes of another (bird)
• The degree of overlap of attributes forms the basis
for a decision about the validity of the proposition.
• More the distance between the sets, greater the
reaction time in making a decision.

14. Attributes of two sets with high
degrees of overlap
Robin Bird
• Physical object • Physical object
• Living • Living
• Animate • Animate
• Feathered • Feathered
• Red-breasted

15. Universal affirmative (UA) and
particular affirmative (PA)
• UA  all members of one catehory are
subsumed in another category which is
represented as “All S are P” ( e.g. all
canaries are birds)
• PA  only a portion of the members of one
category make up the member of another
category.; represented as “some S are P”
(e.g. some animals are birds)

16. Feature Comparison Model
Smith, Shoben and Rips (1974)
• Assumes that “the meaning of a word is NOT an
unanalyzable unit but rather can be represented
as a set of semantic features”.
• A broad set of features related to any word
varies along a continuum from very important
to trivial.
• A robin – flies, has wings, is a biped, has a red
breast, perches in trees, likes worms, is a
harbinger of spring.

17. Two Types of Features:
1. characteristic features:
– features that are descriptive, common, and
frequent, but not essential to the meaning of
the item
– ROBIN: flies, perches in trees
– the robin does not have to fly or perch to be
considered a robin
2. defining features:
– features absolutely essential to the meaning of
the item
– ROBIN: animate, has wings, has red breast

18. True or False ?
• A robin is a bird.
• A sparrow is a bird. True statements
• A parrot is a bird.
• A chicken is a bird.
• A duck is a bird. Technical speaking
• A goose is a bird.
• A bat is a bird.
• A butterfly is a bird. False statements
• A moth is a bird.

19. Two Stages of Processing
• 1.Process all features of subject with predicate;
comparison of characteristic features.
• if low similarity between features --> respond 'false'
• if high similarity between features --> respond 'true'
• if intermediate similarity, Stage 2 processing
• 2. Create comparison question; comparison of
defining features.
• A bat is a bird --> a bat is a mammal; is a bird a
mammal? A chicken is a bird. --> does not fly, does
chicken have feathers?
• takes more time to respond

20. Research FOR Feature
Comparison Model
• typicality effect:
• A carrot is a vegetable.
• A rutabaga is a vegetable.
• we make faster sentence verification decisions
when an item is a typical member of a category,
rather than an unusual member
• WHY? high similarity between features allows
for Stage 1 processing only for 'A carrot is a
vegetable'; Stage 1 and Stage 2 processing is
necessary for 'A rutabaga is a vegetable'

21. Research AGAINST Feature
Comparison Model:
• category size effect:
• A poodle is a dog.
• A squirrel is an animal.
• we make faster sentence verification decisions
when an item is a member of a small category
• small categories contain more defining
features; therefore, FC model would predict
that there should be more Stage 2 processing
for small categories and thus longer RTs

22. Semantic Network Models
1. Teachable language comprehender (TLC)
2. Spreading activation-Collins & Loftus (1975)
3. ACT*, ACT-R
• Characteristics:
– concepts represented as nodes in network
– nodes are linked together by pathways
– proposition = node 1 --- pathway --- node 2
– spreading activation
– frequently used links have greater strengths
– intersection search
– priming

23. Collins & Quillian (1969)
Teachable language comprehender
• developed a model of memory based on
semantic organisation.
• Their model was an example of a network
model of semantic memory.
• A network is a structure consisting of a set of
nodes with links or paths interconnecting them.
• For example, the knowledge that an ostrich is an
animal or that a fish can swim is represented in
a network like that shown in Figure 2.

24. Collins and quillian (1969) – three level hierarchy
memory structure

25. Spreading Activation theory
Collins & Loftus, 1975
1. New assumptions:
a) Not hierarchical: length of links represent
degree
of relatedness. Search time depends on link
length
b) Spreading Activation: retrieval (activation)
of one
of the links leads to partial activation of
connected
nodes. Degree of activation decreases with the

27. • 2. New predictions:
• a) typicality effects:
• A robin is a bird. vs. A chicken is a bird.
• b) semantic priming:
• lexical decision (word/nonword) task:
• type of trial prime target
React.Time
• related prime bread butter
600
• unrelated prime nurse butter

28. Propositional networks
• HAM – human associative memory
• ACT* - Adaptive control of thought

29. ACT-R model (Adaptive Character of
Thought, Revised) by John Anderson (1996, 2000)
• Anderson's model developed from the
human associative learning (HAM) model
proposed by Anderson and Bower (1973).
• ACT-R proposes three interactive memory
systems that support adaptive thinking,
including declarative knowledge,
procedural knowledge, and working
memory.

30. • The declarative knowledge component
consists of schemata and chunks within
schemata that encode specific declarative
knowledge units. The procedural
knowledge component consists of
production rules that break down complex
action sequences into a number of “if-then”
steps, which enable the learner to perform
complex actions using a series of simple
steps.

31. • Declarative and procedural components are
connected to each other, as well as a
working memory system in which activated
declarative and procedural units are used to
solve problems, make decisions, and adapt
to environmental conditions.

32. • ACT-R differs from earlier network models
in that it proposes production rules, which
are combined into production systems,
which enable the brain to represent complex
actions. A production rule specifies the
action to be taken to achieve a specific goal
and the conditions under which each action
is taken.

33. For example, imagine that a person has a ring of
five keys and needs to open an office door.
This scenario can be represented as a simple
production as follows:
IF a person must open a door,
THEN he or she must insert key one and open the
door;
IF key one fails to open the door
THEN the person must insert key two, and so on.

34. Production rule
• This production rule could be subdivided further
into finer grained production rules that specify
how to use each key until the correct key is
identified, or none of the keys open the door.
• In addition, conditions could be added to each
sub-step in the production sequence to assist the
learner.
• For instance, one might add a condition
statement, instructing the person not to attempt
to use long, narrow keys with square heads
because these keys often open car doors rather
than office doors.

35. • Anderson states that complex cognitive activity
can be understood and explained in terms of small
productions, based on simple units of declarative
and procedural knowledge. This suggests that
learning is a systematic process of acquiring
declarative and procedural knowledge through
experience and using this knowledge under
specific conditions to execute complex actions,
which themselves are comprised of many small
productions

38. CONNECTIONIST MODELS
• Connectionist models of knowledge
representation and learning became popular in
the 1980s and sometimes are referred to as
neural networks or parallel distributed
processing (PDP) models
• de-emphasize the intentional role of the
learner, while emphasizing the role of
experience in building neural pathways and
connections

39. Connectionist models differ from network
and production models in two ways.
The first difference is that previous cognitive
models used a computer metaphor to describe
human information processing. In this view,
information passes through an initial sensory
system, is acted upon in working memory,
and represented in permanent store in long-
term memory.

40. Connectionist models replaced the computer
metaphor with a neural pathway metaphor
modeled on the human brain. In this view,
information is represented as patterns of
activation across a variety of units, which
correspond to neurons in the human brain.

41. • A second difference is that network and
production models focus on the representation of
discrete units of information within a node in
memory (e.g., a fact or a simple production rule),
whereas connectionist models view knowledge
representation as continuous across a number of
interconnected units in memory. Thus,
information such as facts, concepts, and
production rules are not represented within single
nodes, but distributed across nodes.

42. • Connectionist models propose a rather simple
architecture based on units, which maintain
elementary information, typically simpler than
corresponding nodes in network and production
models. Multiple units are connected to create
information that one might label as facts or
concepts. The connectivity pattern among these
units is of utmost importance. Any given unit may
be connected to many other units, using a number
of different connectivity patterns. Thus, one unit
may be part of different knowledge
representations much like a single light in a theatre
marquee may be used to spell different words

43. • Connectionist theories have proposed different
types of units. The most important of these are
input units, output units, and hidden units, which
are mediating connections between inputs and
outputs.
• Each unit has an activation value assigned to it
under different processing conditions. Activation
spreads throughout the system, but depends in part
on the connectivity pattern among units, as well as
connection weights, which determine whether one
unit contributes more activation than another unit.

44. Mental Imagery
• Imagery and Cognitive Psychology – Neuro
cognitive Evidence – Cognitive Maps
Storing – Retrieving –Retrieval from
working and Permanent memory – Theories
of retrieval – Forgetting