W3 Semantic Memory Flashcards
Semantic memory
General world knowledge including objects, people, concepts and words.
What predicts what happens next based on regularities in the world?
Categories, schemata and scripts.
Structure: 2 Options
Sematic Memory
Option 1 = Separately stored representations of information and their various relations, problem = not economical.
Option 2 = Storing representations and their relations in a more economical network.
Hierarchical network model
Collins & Quilian’s, 1969
Access of concept representations through spreading activation between nodes via their connecting paths.
Limitation: does not account for semantic relatedness
Semantic dementia
Syndrome of progressive deterioration in semantic memory, leading to the loss of knowledge about objects, people, concepts, and words.
Categorization
Sematic Memory
Semantic memory enables us to form representations of categories (e.g. “dog”) based on regularities in the world, thereby allowing us to make predictions about what will happen next.
Classical theory of categorization
Categories are defined by necessary and sufficient features. (e.g., separating off and even numbers because odd numbers cannot be divided evenly into groups of two.)
Critcism of Classical theory
Family resemblance, central tendency, graded membership
Family resemblance
Critisms of Classical Theory of Categorization
different members of a category can share different features. This doesn’t take into account of central tendency (average ideal).
Central tendency
Critisms of Classical Theory of Categorization
categories exhibit an average ideal
Graded membership
Critisms of Classical Theory of Categorization
Some members are more typical for a category than others.
Typicality rating
Measuring Categorization
Rank the following chairs from being the best example to being the worst example of a chair: DV = average rank or rating.
= we grade things that don’t need to be, graded membership exists even for odd numbers. They are all odd but still have different ratings.
Exemplar production
Measuring Categorization
Recall as many pieces of furniture as you can. (e.g., chair, desk, cupboard, bed, drawer, lamp.) DVs = frequency pf production and/or position in the production.
Category membership varification
Measuring Categorization
(e.g., is this an exemplar of category? Furniture = carpet, bird = robin, fish = shark) DVs: accuracy of responses and/or reaction times.
Prototype theory (categorization)
Modern Theories of Categorization
Categories are determined by a mental representation that is a weighted average of all category members. This prototype may or may not be an actual entity.
Common features = four legs, furry, tail. Distinctive features = barks, is omnivore.
This theory cannot explain how people can tell the sizes of categories (Many types of dogs, fewer types of elephants.). It also cannot explain How can people add new members to a category.