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.

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.

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.

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

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 specifying 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.

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

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.

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