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Knowledge - Coggle Diagram
Knowledge
The Process of Categorisation
The tendency to place objects/experiences into categories
Allows us to respond to the almost infinite variety of objects/events/ppl/impressions in our environments
Understand indiv objects/items that we've never seen before
Understand behaviours that we may find otherwise confusing
Making inferences
Definitional Approach to Categorisation
Categorise based on features!!!
Solution: Family resemblance
Things in a category resemble each other in a number of ways
Allows for variation w/in the category
Problem: single definition may not encompass all members
Categorise based on whether the object meets the definition of the category
Not good for everyday, real-life, natural, human-made objects
Good for geometric objects
Prototype Approach to Categorisation
Prototype
Typical/standard/average representation of category
Contains characteristic and most salient features
Based on previous, common, learned experiences
Rosch (1973)
Prototypes=average of category members commonly encountered
New objects have differing degrees of prototypicality (not definitions)
Prototypicality
High
Closely resembles category prototype
A typical category member
High family resemblance
High prototypical objects are...
Named first
Identified more quickly
Typicality Effect
1 more item...
Sentence Verification Technique
More susceptible to priming
Priming
1 more item...
Rosch and the 'Green' Circles
Low
Doesn't closely resemble the category prototype
Compare new object to prototype of category
Exemplar Approach
Exemplar
Actual member/s of a category
Real-world examples
Easily takes into account 'atypical' members of a category
Easily deals w/ more variable categories
Compare new object to a standard example
Prototypes or Examplars?
Prototypes
Average/amalgamation of our experiences
Used in initial learning
Best for large categories
Exemplars
Multiple real-world examples
Used as we increase our knowledge
Best for small categories or categories w/ a lot of variation
Levels of Categories
Hierarchical Organisation
Global (Superordinate)
Can lose a lot of info
Lots of variability in the category
Basic
Always named in free-naming tasks
Quicker to identify
Kids learn concepts in this level sooner
Most common in adult discourse
Diff cultures tend to use the same basic-level categories (for living things)
Specific (Subordinate)
Don't gain much additional info
It's too specific!
Our knowledge of particular topics affects our categorisation
Need to consider
Properties of objects
Learning and experience of perceivers
Experts
Tend to use the specific level
Knowledge Networks
Semantic Network Model
For describing objects in a category
We use our hierarchical model and work backwards
Concepts arranged in networks
Represents the way concepts are arranged in mind
More efficient (doesn't take up too much storage capacity)
Cognitive economy
Store shared info at the highest level
Exceptions stored at lower level
RT to identify category membership
Spreading activation
Activation of one node activates other connected concepts in the network
Primes connected concepts to be more easily retrieved
Lexical Decision Task
Criticisms
Can't explain the typicality effect
E.g. pig-mammal-animal
Connectionist Model
How info is represented in the brain
Networks consist of neuron-like units
Input units
Hidden units
Output units
Processing occurs in parallel
Parallel distributed processing
Concurrent activation across many units at the same time
Connection weights
Support For
Networks are not totally disrupted by damage
Explains generalisation of learning
Categories in the Brain
Diff areas of the brain respond to diff categories (specialisation)
Brain scans
Multiple Factors Approach
Looks at characteristics that are hared w/in a category
Crowding
The Embodied Approach
Sensory + Motor Processes
Perception + action
Mirror Neurons
Semantic Somatotopy