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Summary Chapter 9 Cognitive Psychology, ISBN: 9781473734524 PYC3703 - Cognition: Thinking, Memory And Problem Solving
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Concise summary including key terms and explanations.
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PYC3703 – COGNITIVE PSYCHOLOGYCHAPTER 9: KNOWLEDGE
Key Terms Explanation Pg.
Anterior temporal lobe 289
Back propagation Signals sent backwards in the network starting 281
from the property units
Basic level Example - table 269
Categorisation The process by which things are placed in 259
categories
Category Includes all possible examples of a particular 259
concept
Category-specific memory An impairment in which the ability to identify 283
impairment one type of object is lost but the ability to
identify other types of objects is retained
Central tendency Whereby the best or most typical example(s) 267
have been found to share many features with
other category members
Also known as averageness approach to
typicality
Cognitive economy The way of storing shared properties just once 275
at a higher-level node
Concept The mental representation of a class or 258
individual
The meaning of objects, events and abstract
ideas
Conceptual knowledge The knowledge that enables us to recognise 258
objects and events and to make inferences
about their properties
Connection weight Determines how signals sent from one unit 279
either increase or decrease the activity of the
next unit
Connectionism An approach to create computer models for 278
representing cognitive processes
Connectionist network Look at diagram 279
Crowding Refers to the fact that animals tend to share 286
many properties while artefacts do not
Definitional approach to We can decide whether something is a 260
categorisation member of a category by determining whether
a particular object meets the definition of that
category
Embodied approach States that our knowledge of concepts is 287
based on reactivation of sensory and motor
processes that occur when we interact with the
object
Error signal The learning process occurs when the 281
erroneous responses in the property units
cause an error signal to be. Sent back through
, the network, by a process called back
propagation
Exemplar Actual members of the category that a person 266
has encountered in the past
Exemplar approach to Involves determining whether an object is 266
categorisation similar to other objects
Differs from the prototype approach as the
standard involves many examples, each one
called an exemplar
Family resemblance Refers to the idea that things in a particular 261
category resemble one another in a number of
ways
Global level Or superordinate level 269
Example - furniture
Graceful degradation Disruption of performance occurs only 283
gradually as parts of the system are damaged
Hidden units Receive signals from input units 279
Hierarchical model Look at diagram on page 274
Hierarchical organisation Kind of organisation in which larger, more 269
general categories are divided into smaller,
more specific categories, creating a number of
levels of categories
Hub and spoke model Areas of the brain that are associated with 289
specific functions are connected to the anterior
temporal lobe, which serves as a “hub” that
integrates the information from these areas
Input units Units activated by stimuli from the environment 279
Lexical decision task 277
Mirror neurons Figure 9.26a 287
Multiple-factor approach Focuses on searching for more factors or 285
dimensions that determine how concepts are
divided up within a category
Output units Receive signals from hidden units 279
Parallel distributed Connectionist models – designed to represent 278
processing (PDP) concepts
Propose that concepts are represented by
activity that is distributed across a network
Prototype A “typical” member of a category 261
Prototype approach to Membership in a category is determined by 261
categorisation comparing the object to a prototype that
represents the category
Semantic category Proposes that there are specific neural circuits 284
approach in the brain for some specific categories
Semantic dementia Patients with this disorder experience 275
increasing difficulty with semantic tasks such
as naming objects, sorting objects into
categories and verifying object and category
properties
Semantic network Proposes that concepts are arranged in 274
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