Representation of knowledge

• 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.

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.

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.

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).

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.

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.

• 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

conceptual hierarchies for the word vehicle

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 model2. Feature Comparison Model3. Network models

1. Collins & quillian2. Spreading activation theory – Collins and loftus

4. Propositional networks1. HAM2. ACT*

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.

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.

Attributes of two sets with high degrees of overlap

Robin

• Physical object

• Living

• Animate

• Feathered

• Red-breasted

Bird

• Physical object

• Living

• Animate

• Feathered

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)

Feature Comparison ModelSmith, 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.

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

True or False ?

• A robin is a bird.

• A sparrow is a bird.

• A parrot is a bird.

• A chicken is a bird.

• A duck is a bird.

• A goose is a bird.

• A bat is a bird.

• A butterfly is a bird.

• A moth is a bird.

True statements

Technical speaking

False statements

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

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'

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

Semantic Network Models1. 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

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.

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

Spreading Activation theory Collins & Loftus, 1975

1. New assumptions:a) Not hierarchical: length of links represent degreeof relatedness. Search time depends on link lengthb) Spreading Activation: retrieval (activation) of

oneof the links leads to partial activation of connectednodes. Degree of activation decreases with thedistance.c) Activation decreases with time.

• 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 trialtype of trial primeprime target target

React.TimeReact.Time• related prime bread butter 600• unrelated prime nurse butter 670

Propositional networks

• HAM – human associative memory

• ACT* - Adaptive control of thought

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.

• 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.

• 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.

• 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.

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.

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.

• 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

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

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.

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.

• 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.

• 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

• 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.

Mental Imagery

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