Knowledge Flashcards
Definitional approach to categorisation
Determine category membership based on whether the object meets the definition of the category
Not good for natural objects e.g. chairs can look different but still be a chair
Family resemblance
Things in a category resemble one another in a number of ways (see definitional approach)
Prototype approach to categorisation
Membership in a category is determined by comparing the object to a prototype that represents the category.
Average representation of the category.
Prototype
A standard representation of a category, an ‘average’. E.g. sparrow is a typical bird
High prototypicality
High prototypical objects have high family resemblance
Are the first objects to be recalled (typicality effect)
More susceptible to priming
Sentence verification technique
Highly prototypical objects are identified more quickly (typicality effect).
Eg. respond quicker to ‘Apple is a fruit’ than ‘Pomegranate is a fruit’
Exemplary approach to categorisation
Actual members of the category that a person has encountered in the past.
Easily takes into account ‘atypical’ members of a category
Sparrow recalled faster because similar to more bird exemplars.
Prototypes or exemplars?
We use both
Learn a category using prototypes then more to exemplars.
Exemplars best for small groups.
Prototypes best for large groups.
Hierarchical categories
Different levels of categories (large, general categories divided into smaller, more specific categories)
Basic level of categories
Optimal level
Superordinate level of categories
Global/general
Lose a lot of information
Subordinate level of categories
Specific.
Gain little information
Evidence that basic level is special
People almost exclusively use basic-level names in free naming tasks.
Quicker to identify basic-level category member as a member of a category.
Children learn basic-level concepts sooner than other levels.
Basic-level is much more common in adult discourse than names for superordinate categories.
Different cultures tend to use the same basic-level categories, at least for living things.
Semantic network approach
Concepts are arranged in networks that represent the way concepts are arranged in the mind
Hierarchical model - specific concepts at the bottom and more general concepts at higher levels
Cognitive economy
Shared properties stored at higher level node, exceptions at lower level nodes.
Storing shared properties just once at a higher level node
Spreading activation
Activity spreads out from one node to other con vector nodes
Primed concepts are easier to retrieve from memory because of the spreading
Lexical decision task
Participants read stimuli and are asked to say as quickly as possible whether the items are words or not
Semantic network model problems
Can’t explain the typicality effect
Connectionist model
Networks consist of units (inspired by neurons).
Processing occurs in parallel (at the same time) - parallel distributed processing
Connection weights
Input, hidden, output units
Input units are activated by stimuli from the environment.
Input units send signals to hidden units, which send signals to output units.
Connection weights
Determines how signals sent from one unit either increase or decrease the activity of the next unit.
Parallel distributed processing
Concurrent activation across many units at the same time
Support for connectionist model
Networks are not totally disrupted by damage.
Explains generalisation of learning.
Categories in the brain
Different areas of the brain may be specialised to process particular category information
Category-specific memory impairment
Individual loses the ability to identify one type of object but retains the ability to identify other types of objects
E.g. can identify non-animals but not living animals.
Sensory-functional hypothesis
States that our ability to differentiate living things and artifacts depends on a semantic memory system that distinguishes sensory attributes and a system that distinguishes function
E.g. recognise living things by sensory features, non-living things by function.
Semantic category approach
Proposes that there are specific neural circuits in the brain for some specific categories.
Multiple factor approach
Looks at how concepts are divided up within a category rather than identifying specific brain areas of networks for different concepts
Crowding
When different concepts within a category share many properties
E.g. animals all share eyes, legs, and the ability to move
Boat/car/plane only share ‘vehicle’
Embodied approach
Our knowledge of concepts is based on reactivation of sensory and motor processes that occur when we interact with the object
Mirror neurons
Neurons that fire when we do a task or when we observe another doing that same task
Semantic somatotopy
Correspondence between words related to specific body parts and the location of brain activation
E.g. leg words and leg movements elicit activity near the brain’s centre.
