Cognitive psychology report

REPRESENTATION and
ORGANIZATION of KNOWLEDGE
in MEMORY:
Concepts, Categories, Networks and
Schemas

REPRESENTATION and ORGANIZATION of KNOWLEDGE in MEMORY:
Concepts, Categories, Networks and Schemas
1. How are representations of words and
symbols organized in the mind?
2. How do we represent other forms of
knowledge in the mind?
3. How does declarative knowledge interact
with procedural knowledge?
INTRODUCTION

ORGANIZATION
OF
DECLARATIVE KNOWLEDGE

ORGANIZATION OF DECLARATIVE KNOWLEDGE
-CONCEPT –
the fundamental unit of symbolic
knowledge (knowledge of
correspondence between symbols
and their meaning for example, that
symbol “3” means three), an idea
about something that provides a
means of understanding the world.
-CATEGORY-
is a group of items into
which different objects or
concepts can be placed that
belong together because
they share some common
features, or because they
are similar to a certain
prototype.

Concepts and Categories
Concepts and categories can be divided in various ways:
NATURAL CATEGORIES
ARTIFACT CATEGORIES
are groupings that occur naturally in
the world like birds or tree.
are groupings that are designed or invented by
humans to serve particular purposes or
functions.
Natural and Artifact Categories are relatively stable and
people tend to agree on criteria for membership.

On the contrary..
-CONCEPTS-
are not always stable but
can change. They are
described not in words but
rather in phrases.
they also appear to have a
basic level (sometimes
termed as a natural level) of
specificity, a level within a
hierarchy that is preferred to
other levels.

In general, the basic level is
neither the most abstract nor
the most specific. This basic
level can be manipulated by
context or expertise.

The basic level is the one that most people find to be
maximally distinctive. By means of training, the basic level
can be shifted to a more subordinate level.
For example, the more a person learns about cars, the
more he or she is likely to make elaborate distinction
among cars.
Research suggests that the difference between experts
and novices are not due to qualitative mechanisms but
rather quantitative differences in processing efficacy.

So, how do we decide what
objects to put into a
category?

-FEATURE-BASED CATEGORIES
-PROTOTYPE THEORY
-THEORY BASED VIEW OF
CATEGORIZATION
-SEMANTIC-NETWORK MODELS
-SCHEMATIC REPRESENTATIONS

FEATURE-BASED CATEGORIES:
A DEFINING VIEW
the classic view of categories disassembles a
concept into a set of featural components. All
those features are then necessary (and sufficient)
to define the category. This means that each
feature is an essential element of the category.
Together, the features uniquely define the
category; they are defining features.
For a thing to be an X, it must have that feature. Otherwise it is not an “X”.

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The Problem:
1. Some categories do not readily lend
themselves to featural analysis.
2. A violation of those defining features does
not seem to change the category we use to
define them.

In sum, the feature-based
theory has some attractive
features, but it does not give a
complete account of categories.

PROTOTYPE THEORY:
A CHARACTERISTIC VIEW
grouping things together not by their defining
features rather by their similarity to an
averaged model of the category.
PROTOTYPE
is an abstract average of
all the objects the
category we have
encountered before
CHARACTERISTIC FEATURE
describe (characterize or typify)
prototype but are not necessary for
it.

So what exactly is a
characteristic feature?
whereas a defining feature is shared by every single
object in a category, a characteristic feature need not
to be Instead, many or most instances possess each
character feature..

Psychologists differentiate two kinds of categories:
Classical concepts and Fuzzy Concepts.
CLASSICAL CONCEPTS FUZZY CONCEPTS
-Can be readily defined
through defining
features
-May be built on
defining features
-Cannot be so easily
defined
-Built around prototypes

Real-World Examples: Using Exemplars
Some Psychologists suggests that instead of
using a single abstract prototype for
categorizing a concept, we use multiple,
specific exemplars.
EXEMPLARS are typical representatives of a category
In particular, categories are set up by creating a rule and then by
storing examples as exemplars. Objects are then compared to the
exemplars to decide whether or not they belong in the category the
exemplars represent.

A SYNTHESIS:
COMBINING FEATURE-BASED and
PROTOTYPE THEORIIES
A full theory of categorization can combine both defining and
characteristic features, so that each category has both prototype and a
CORE.
CORE
refers to the defining features something must have to be
considered an example of category.
The prototype encompasses the characteristic features that tend to be typical
of an example but that are not necessary for being considered as an example.
The core requires that someone labeled as a robber be a person who takes
things from others without permission. The prototype, however, tends to
identify particular people as more likely to be robbers.

“ROBBER
”

A departure from feature-based,
prototype-based, and exemplar-based
views of meaning is a THEORY BASED
VIEW of meaning also sometimes
called an EXPLANATION-BASED VIEW.

HOW DO PEOPLE USE
THEIR THEORIES FOR
CATEGORIZATION?

A THEORY BASED VIEW OF MEANING hold that people
understand and categorize concepts in terms of
implicit theories, or general ideas they have regarding
those concepts.
For example, what makes a GOOD
SPORT?

In the PROTOTYPE VIEW,
you would try to find characteristic
features of a good sport.
In the COMPONENTIAL VIEW,
you would try to isolate features of a
good sport.
In the EXAMPLAR VIEW, you
might try to find some good examples
you have known in your life.

In the theory-based view, you would
use your experience to construct an
explanation for what makes someone
a good sport.

SO what does it means to be a GOOD
SPORT in a theory-based view?
A good sport is someone who, when he or she wins, is
gracious in victory and dos not mock losers or otherwise
make them feel bad about losing. It is also someone
who, when he or she loses, loses graciously and does
not blame the winner, the referee, or find excuses.
Rather, he or she takes the defeat in stride,
congratulates the winner, and then moves on.

FINDING THE “ESSENCE”
OF THINGS

SEMANTIC-NETWORK MODELS
Semantic Network Models suggests that knowledge
is represented in our minds in the form of concepts
that are connected with each other in a web-like
form

Collins and Quillan’s Network Models
Knowledge is represented in terms of hierarchal
semantic network.
A SEMANTIC NETWORK is a web of elements of
meaning (nodes) that are connected with each
other through links.

ORGANIZED KNOWLEDGE representation takes the form of
a hierarchal tree diagram. The elements are called nodes
they are typically concepts.
The connections between the nodes are labeled
relationships. They may indicate category membership,
attributes, or some other semantic relationship. Thus a
network provides a means of organizing concepts.

INHERITAN
CE
This concept implies that
lower-level items inherit
the properties of higher-
level items.
Whatever was known about
items at higher levels in a
hierarchy was applied to all
items at lower levels in the
hierarchy

COMPARISON OF SEMANTIC
FEATURES
Knowledge is organized based on a comparison of
semantic features, rather than on a strict hierarchy of
concepts

SCHEMATIC
REPRESENTATIONS
SCHEMA SCRIPT

ORGANIZATION
OF
PROCEDURAL KNOWLEDGE

ORGANIZATION OF PROCEDURAL KNOWLEDGE
PROCEDURAL
KNOWLEDGE
REPRESENTATION- acquired to practicing the implementation of a
procedure

Psychologists have developed a variety of models for how
procedural information is processed. Each of the models
involves:
SERIAL PROCESSING of information
in which information handled through
a linear sequence of operations, one
operation at a time
One way in which computer can represent and organize procedural
knowledge is in the form of set of rules governing a PRODUCTION
which includes the generation and output of procedure

In the same way computer simulation of production follow
production rules, people may use the same form of
organizing knowledge of very close to it this production rules
is the:
“if-then” rules
The “if” clause includes a set of conditions that must be
met to implement the “then’ clause.
The “then” clause is an action or a series of actions that
are a response to “if” clause.

When the rules are described precisely and all the relevant
conditions and actions are noted, a huge number of rules are
required to perform even a very simple task. These rules are
organized into a structure of
ROUTINES SUBROUTINES
instructions regarding
procedures for
implementing a task.
Instructions for implementing a
subtask within a larger task
governed by a routine
Many of these routines and subroutines are
ITERATIVE
they are repeated many times during the
performance of a task

NONDECLARATIVE KNOWLEDGE
knowledge traditionally has been has been described as either declarative or
procedural. One can expand the traditional distinction between declarative and
procedural knowledge to suggest that NONDECLARATIVE KNOWLEDGE may
encompass a broader range of mental representations than just procedural
knowledge.
We mentally represent the following forms of non declarative knowledge:
PERCEPTAL, MOTOR, AND COGNITIVE SKILLS (procedural
knowledge)
SIMPLE ASSOCIATIVE KNOWLEDGE (classical and operant
conditioning)
SIMPLE NON-ASSOCIATIVE KNOWLEDGE (habituation and
sensitization)
PRIMING

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The preceding example illustrate situations in which an item may
prime another item that somehow related in meaning.
TWO TYPES OF PRIMING
SEMANTIC PRIMING REPITITION PRIMING
We are primed a meaningful context or
by meaningful information. Such
information typically is a word or cue
that meaningfully related to the target
that is used.
A prior exposure to a word or other
stimulus primes a subsequent retrieval
of the information

INTEGRATIVE MODELS
FOR
REPRESENTING
DECLARATIVE AND
NONDECLARATIVE KNOWLEDGE

ADAPTIVE CONTROL of
THOUGHT- RATIONAL (ACT-R)
PARALLEL PROCESSING: THE
CONNECTIONIST MODEL

COMBINING REPRESENTATIONS:
ACT-R (ADAPTIVE CONTROL OF THOUGHT-
RATIONAL)
PROCEDURAL KNOWLEDGE
DECLARATIVE KNOWLEDGE
represented in the form of production systems
represented in the form of propositional networks
Proposition
the smallest unit of knowledge that can be judged
to either true or false

In ACT-R, networks include images of objects and
corresponding spatial configurations and
relationships. They also include TEMPORAL
INFORMATION, such as relationships involving the
sequencing of actions, events, or even the order in
which items appear.
TEMPORAL INFORMATION = TEMPORAL STRINGS
they contain information about
the relative time sequence

DECLARATIVE KNOWLEDGE within ACT-R
it contains a MECHANISM by which information can be
retrieved and also a STRUCTURE for storing information.
within a semantic network, concepts are stored at various
nodes within the network these nodes can be either inactive
or active at a given time.
ACTIVE NODE - is one that is in a sense, "turned on".
a node can be turned on- activated- directly by external stimuli, such as
sensations, or it can be activated by internal stimuli, such as memories or
thought processes, and it can also be activated indirectly, by the activity of one
or more neighboring nodes

there is SPREADING ACTIVATION
within the network from one node to
another due to each nodes receptivity to
stimulation from neighboring nodes.

ACT-R also suggests means by which the
network changes as a result of activation.
For one thing, the more often particular links
between nodes are used, the stronger the links
become.

PROCEDURAL KNOWLEDGE within ACT-R
Such knowledge is represented in production systems rather
than in semantic networks. Knowledge representation of
procedural skill occurs in three stages:
COGNITIVE ASSOCIATIVE AUTONOMOUS
we think about
explicit rules for
implementing the
procedure
we consciously
practice using the
explicit rules
extensively, usually
in a highly
consistent manner
is the overall process which
we transform slow, explicit
information about procedure
("knowing that") into
speedy, implicit,
implementation of
procedures ("knowing how“)

COMPOSITION
a means by which we make this
transformation.
PRODUCTION TUNING
another aspect of proceduralization, it involves the
two complementary processes of generalization
and discrimination.

PARALLEL PROCESSING:
THE CONNECTIONIST MODEL
Computer-inspired information-processing theories are processed one step
after another. Some aspects of human cognition may indeed be explained in
terms of serial processing, but psychological findings and other cognitive
research seem to indicate other aspects of human cognition.
Parallel Processing
multiple operations can go all at
once

PARALLEL DISTRIBUTED
PROCESSING (PDP) MODELS or
CONNECTIONIST MODELS
states that we handle very large numbers of
cognitive operations at once through a
network distributed across incalculable
numbers of locations in the brain

HOW THE PDP MODEL
WORKS
The mental structure which parallel processing is
believed to occur is a network. In connectionist
networks, all forms of knowledge are represented within
the network structure.
In the PDP model, the network comprises neuron-like units.
They do not, in and of themselves, actually represent the
concepts, propositions, or any other type of information. Thus
the pattern of connections represents knowledge not the
specific units.

Differing cognitive processes are handled by differing patterns
of activation, rather than a result of a different set of
instructions from a computer’s central processing unit. In the
brain, at any one time, a given neuron may be :
INACTIVE EXCITATORY INHIBITORY
neurons are not
stimulated
beyond their
threshold of
excitation.
neurons release
neurotransmitters that
stimulate receptive
neurons at the
synapse.
neurons release
neurotransmitters that
inhibit receptive
neurons.
The more often a particular connection is activated, the greater is the
strength of the connection, whether the connection is excitatory or
inhibitory

According to the PDP model, whenever we use
knowledge, we change our presentation of it.
Thus KNOWLEDGE REPRESENTATION
is not really a final product
Rather, it is a process or even a potential process.
It is pattern of potential excitatory or inhibitory
connection strengths.

When we receive new information, the activation from that
information either:
STRENGTHENS WEAKENS
the connections between units

The ability to create new information by drawing inferences and
making generalizations allows for almost infinite versatility in
knowledge and manipulation.
HUMAN MINDS ARE
FLEXIBLE
This versatility allow as to accommodate incomplete
and distorted information

By using the PDP model, cognitive
psychologists attempt to explain various
general characteristics of human
cognition.

CRITICISMS OF THE
CONNECTIONIST MODEL

COMPARING CONNECTIONIST WITH
NETWORK REPRESENTATIONS

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