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SUSS PSY 305 STUDY UNIT 5 Knowledge - Coggle Diagram
SUSS PSY 305 STUDY UNIT 5 Knowledge
Conceptual knowledge
knowledge that
enables us to recognize objects and events
and to
make inferences
about their properties.
This knowledge exists in the form of concepts (i.e. meaning of objects, events, and abstract ideas)
Conceptual representation
In the process of identifying concepts/things we perceive, we tend to categorise them
There are
three main theories
of concept representation
Exemplar approach
The exemplar approach assumes that a
concept is represented by many exemplars or examples
of that concept.
For example, the concept of a chair may be represented by the couch, the swivel chair and the dining chair.
Hence, category membership is
based on individual representations
or exemplars
stored in memory.
Exemplars
Exemplars are
actual members of the category
that a person has encountered in the past.
Thus if a person encountered a baby high chair or a bar stool, these would be an exemplar for the category of 'chairs'
Addressing definitional issues
The exemplar approach addressed the problem encountered in the definitional approach to concept representation, where coming up with an all-encompassing set of defining features that defined a natural concept was difficult.
None
of the members was
left out
in the exemplar approach
to concept representation
critical view of examplar approach
One strength of the exemplar approach in terms of explaining categorisation, is that category membership is
not restricted to a set of defining features
.
However, an obvious weakness of this approach to categorisation is that
cognitive economy is not achieved
Prototype approach
According to the prototype approach to categorising
concepts
, a category is
represented by
summary or ideal
representation known as a “prototype”
.
The prototype has all the typical features of the concept concerned and is considered
the most typical member of the category
.
Category membership is determined by how similar the new item encountered is to the prototype.
The more typical members will share more features with the prototype while the atypical members will share fewer features with the prototype.
The idea behind the prototype approach to concept representation is that
when we encounter a new item, we tend to compare it to our prototype
of the category and decide whether or not it is similar enough, or shares enough traits to be considered a member of that category.
Critical view of Prototype
The prototype approach
achieves cognitive economy.
The significance of achieving cognitive economy will be further elaborated when examining Collins and Quillian’s (1969) hierarchical model.
Definitional approach
A logical way to organise concepts is to
categorise them by defining the features
that characterises the concept.
According to this approach, the defining features of a category must be both
necessary and sufficient
(i.e. clearly defined.
For example, the following are necessary features of a square: has four straight sides, sides are joined at their ends, angles add up to 360 degrees.
But these features are insufficient as such features also represent other shapes such as rectangle/ rhombus.
By specifying that the four straight lines are of equal length, and each of the four angles are 90 degrees, the set of features is now both necessary and sufficient for defining a square.
Defining natural concept
With
artificial concepts, a clear boundary of category
membership is established.
However, when it comes to defining natural concepts, such as a “bird”, the boundary becomes more difficult to establish, and coming up with a set of defining features is no longer a straight-forward task.
issues with this approach
According to the definitional approach to categorisation, category
membership is determined by a set of defining features of the concept concerned
.
a member of the category
must have all the defining features of that category
.
For example, if one of the defining features of a bird is that it can fly, then a sparrow would qualify as a member while a penguin would not because it cannot fly, even though it shares most of the other defining features of a bird, such as having feathers, wings, a beak, and they lay eggs. Even if a penguin can’t fly, it is still considered a bird.
Hence, the
definitional approach does not fully explain how we categorise natural concepts.
Family resemblance
Wittgenstein proposed the idea of family resemblance to deal with the problem that definitions often do not include all members of a category.
Family resemblance refers to the idea that
things in a particular category resemble one another in a number of ways
.
Thus, instead of setting definite criteria that every member of a category must meet,
the family resemblance approach allows for some variation within a category.
Basic level category
The basic level category is a concept first proposed by Rosch who conducted a series of experiments to determine where the basic level was for various categories (Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1976).
The basic level represents the
level of category we learnt earliest and used most often
(e.g., “cat” as opposed to “animal” or “tabby”).
It is the highest level at which we can
form a generalised image of the category
(e.g., “car” as opposed to “vehicle” or “Toyota”).
It is that natural level of categorisation which is
neither too specific nor too general
(e.g., “apple” as opposed to “fruit” or “granny smith”). - It
maximises distinctiveness and informativeness
(e.g., fish–bird–cat, as opposed to the global level of animals or the specific levels covering the various species of fish, bird, and cat).
What is the significance of the basic level
Rosch proposed three levels of category
Global level (i.e animal, vehicle)
Basic level (i.e. cat, car)
Specific level (Persian cat, Toyota)
The basic level is psychologically special because going above it (to
global
) results in a
large loss of information
(i.e a loss in categorical features) and going below it (to
specific
) results in little
gain of information
.
HOW KNOWLEDGE CAN AFFECT CATEGORIZATION
our ability to categorize is learned from experience;
it depends on which objects we typically encounter and what characteristics of objects we pay attention to
In an experiment conducted by Coley and colleagues, it was discovered that
experts
responded to the independent variable – pictures of birds – by
specify
ing the birds’ species (robin, sparrow, jay, or cardinal), but the
nonexperts responded by saying “bird.
”
Apparently the experts had learned to pay attention to features of birds that nonexperts were unaware of.
Thus, in order to fully understand how people categorize objects,
we need to consider
not only the properties of the objects but also
the learning and experience of the people perceiving those objects
.
Generally,
people with more expertise
and familiarity with a particular category tend to
focus on more specific information
that Rosch associated with the specific level.
Representing Relationships Between Categories: Semantic Networks
Rosch have demonstrated that categories can be arranged in a hierarchy of levels, from global (at the top) to specific (at the bottom).
Here, our main concern is to explain
how categories or concepts are organized
in the mind.
The semantic network approach ,
proposes that concepts are arranged in networks
COLLINS AND QUILLIAN’S HIERARCHICAL MODEL
Collins and Quillian proposed a
hierarchical model
(aka C & Q model) where concepts are
represented in nodes
that are connected to other nodes via links.
At the
higher level
, each node
carries all the attributes
of that concept.
As we move down the hierarchy, all the
attributes stored at the higher-level nodes are automatically inherited
by the lower-level nodes.
For example, at the higher level, the category ‘bird’ may be attached with the attribute ‘can fly’ thus, all the lower level nodes will inherit the attribute of ‘can fly’)
Sometimes, certain
inherited attributes need to be overwritten
in order to accommodate exceptions as illustrated below in the case of the penguin not being able to fly.
Also additional, specific attributes, not featured in the higher nodes, are
added to the lower-level nodes.
Cognitive economy
You might wonder why we have to travel from a lower level (i.e. “canary”) to a higher level (i.e. “bird”) to find out a particular attribute of a lower level node (i.e. that a canary can fly).
Collins and Quillian proposed that
including
the
general attribute
of “can fly” at the node
for every bird
(canary, robin, vulture, etc.) was
inefficient and would use up too much storage space
.
Thus, instead of indicating the properties “can fly” and every relevant feature for every kind of bird, these properties are placed at the node for “bird” because this property holds for most birds.
This way of storing shared properties just once at a higher-level node is called cognitive economy.
Sentence Verification Technique
The beauty of the network’s
hierarchical organization
, in which general concepts are at the top and specific ones at the bottom, is that it
results in the testable prediction
that the
time
it takes for a person
to retrieve information
about a concept should be
determined by the distance that must be travelled through the network.
Thus, the model predicts that when using the sentence verification technique, in which subjects are asked to answer “yes” or “no” to statements about concepts, it should take longer to answer “yes” to the statement “A canary is an animal” (global level)than to “A canary is a bird (Basic level)
Upon conducting sentence verification test, it was
confirmed
that As predicted, statements
that required further travel from “canary” resulted in longer reaction times
Spreading activation
Spreading activation is an activity that
spreads out along any link
that is connected to an
activated node.
This effect,
primes the activation of other nodes
, both lower and higher level resulting in
easier retrieval of information
memory of associated, primed nodes.
For example, activating the canary-to-bird pathway activates additional concepts that are connected to “bird,” such as “animal” and other types of birds such as ‘crow’ or ‘ostrich’.
The result of this spreading activation is that the additional concepts that receive this activation become “primed” and so can be retrieved more easily from memory.
this was confined in as study which
involved lexical decision task
Lexical decision task
In the lexical decision task. Their task is to indicate as quickly as possible whether each stimuli/stimulus is a word or a nonword.
For example, the correct responses for bloog would be “no” and for bloat would be “yes.”
Meyer and Schvaneveldt Lexical Decision Task
Results from this study showed that
reaction time was faster when the two words were associated
Discussion
Meyer and Schvaneveldt proposed that this might have occurred because
retrieving one word
from memory
triggered a spread
of activation
to other nearby locations
in a network.
Because more activation would spread to words that were related, the response to the related words was faster than the response to unrelated words
Criticism against the CnQ model
The typicality effect
Researchers pointed out that the theory
couldn’t explain
the typicality effect, in which
reaction times for statements about an object are faster for more typical members
of a category than for less typical members.
I.e. the statement “A canary is a bird” is verified more quickly than “An ostrich is a bird,” but the model predicts equally fast reaction times because “canary” and “ostrich” are both one node away from “bird.”
Issues with cognitive economy
.
Empirical evidence suggests that there are instances when
responses do not correlate with the hierarchical connection
between nodes.
“A pig is an animal” is verified more quickly, but the Collins and Quillian model predicts that “A pig is a mammal” should be verified more quickly because a link leads directly from “pig” to “mammal,” but we need to travel one link past the “mammal” node to get to “animal
Collins and Loftus Semantic networks theory
To overcome the shortcomings of the C & Q model, Collins and Loftus (1975) proposed a
semantic networks model based on a person’s experience
.
Rather than a hierarchical structure,
nodes containing concepts were linked
to one another in such a way that the distance between the nodes varied
depending on how closely associated they were with one another experiantially
If the concepts were
closely associated
, they had a
short link
between them and if the concepts were not so closely associated, they had a longer link between them.
The length of these experientially-based links varied from person to person as the length of the links between concepts depended on the individual’s experience and knowledge. Consequently, the length of the link determined the time it would take to retrieve that particular information