Chapter 9: Conceptual Knowledge Flashcards
Conceptual Knowledge
knowledge that enables people to recognize objects and events and to make inferences about their properties
Concepts
a mental representation of a class or individual, also, the meaning of objects, events, and abstract ideas
an example of a concept would be the way a person mentally represents “cat” or “house”
Category
groups of objects that belong together because they belong to the same class of objects, such as “houses”, “furniture”, or “schools”
Categorization
the process by which objects are placed in categories
Differential Approach to Categorization?
the idea that we can decide whether something is a member of a category by determining whether the object meets the definition of the category
Family Resemblance
in considering the process of categorization, the idea that things in a particular category resemble each other in a number of ways
this approach can be contrasted with states that an object belongs to a category only when it meets a definite set of criteria
Prototype Approach to Categorization
the idea that we decide whether something is a member of a category by determining whether it is similar to a standard representation of the category, called a prototype
Prototype
a standard use in categorization that is formed by averaging the category members a person has encountered in the past
Sentence Variation Technique
a technique in which the participant is asked to indicate whether a particular sentence is true or false
for example, sentences like “an apple is a fruit” have been used in studies on categorization
Typicality Effect
the ability to judge the truth or falsity of sentences involving high-prototypical members of a category more rapidly than sentences involving low-prototypical members of a category
Exemplar Approach to Categorization
the approach to categorization in which members of a category are judged against exemplars, which are examples of members of the category that the person has encountered in the past
Exemplars
in categorization, members of a category that a person has experienced in the past
Hierarchical Organization
organization of categories in which longer, more general categories are divided into smaller, more specific categories
these smaller categories can, in turn, be divided into even more specific categories to create a number of levels
Superordinate Level
the most general category level distinguished by Rosch
for example; “furniture”
Global Level
the highest level in Rosch’s organization scheme (e.g. “furniture” or “vehicles”)
Basic Level
in Rosch’s categorization scheme, the level below the global level
according to Rosch, the basic level is psychologically special because it is the level above which much information is lost and below which little is gained
Subordinate Level
the most specific category level distinguished by Rosch
e.g. “kitchen table”
Specific Level
in Rosch’s categorization scheme, the level below the basic level
Semantic Approach
an approach to understanding how concepts are organized in the mind that proposes that concepts are arranged in networks
Hierarchical Model
as applied to knowledge representation, a model that consists of levels arranged so that more specific concepts, such as canary or salmon, are at the bottom and more general concepts, such as bird, fish, or animal are at higher levels
Cognitive Economy
a feature of some semantic network models in which properties of a category that are shared by many members of a category are stored at a higher-level node in the network
for example, the property “can fly” would be stored at the node for “bird” rather than “canary”
Spreading Activation
activity that spreads out along any link in a semantic network that is connected to an activated node
Lexical Decision Task
a procedure in which a person is asked to decide as quickly as possible whether a particular stimulus is a word or a nonword
Connectionism
a network model of mental operation that proposes that concepts are represented in networks that are modeled after neural network
this approach to describing the mental representation of concepts is also called the parallel distributed processing (PDDP) approach
Connectionist Network
the type of network proposed by the connectionist approach to the presentation of concepts
connectionist networks are based on neural networks but are not necessarily identical to them
one of the key properties of a connectionist network is that a specific category is represented by activity that is distributed over many units in the network
this contrasts semantic networks, in which specific categories are represented at individual nodes
Units
“neuron-like processing units” in a connectionist network
Input Units
units in a connectionist network that are activated by stimulation from the environment
Hidden Units
units in a connectionist network that are located between input units and output units
Output Units
units in a connectionist network that contain the final output of the network
Connection Weight
in connectionist models, a connection weight determines the degree to which signals sent from one unit either increase or decrease the activity of the next unit
Error Signal
during learning in a connectionist network, the difference between the output signal generated by a particular stimulus and the output that actually represents that stimulus
Back Propagation
a process by which learning can occur in a connectionist network, in which an error signal is transmitted backward through the network
this backward-transmitted error signal provides the information needed to adjust the weights in the network to achieve the correct output signal for a stimulus
