CogPsych Exam 3 Flashcards
conceptual knowledge
knowledge that allows us to recognize objects and events
concept
mental representation of a class or individual
categorization
the process by which things are placed into categorical groups
why are categories essential?
allow us to quickly understand/recognize things
3 ways we categorize things
definitional approach, prototype theory, and exemplar theory
hierarchical organization of categories
global (superordinate) —> basic —> specific (subordinate)
hierarchical organization is
relative for each person depending on their expertise in the subject
semantic networks
represent how concepts are organized in the mind; hierarchal w/ most general at the top
the part of a semantic network that represents a category/concept
node
cognitive economy
shared properties for different categories are stored at “higher level” more general nodes to conserve space
collins and quillion’s semantic network
reaction time to a stimuli should take longer depending on how many “nodes” you have to travel; was not supported from sentence verification studies
spreading activation
when a node is activated, the activity spreads among the connected links (essentially what priming is!)
prototype categorization
a typical category member represents an “average” case; we compare to our prototype
advantages to prototype categorization
verify things faster, better for large categories, shows stronger priming effects
exemplar categorization
an actual member of the category that a person has encountered in the past represents the exemplar
advantages to exemplar categorization
handles atypical cases better than prototypes; explains the same effects as the prototype approach
who hypothesized the hierarchal levels of categorization
Rosch
importance of basic level categories
they provide a good balance between being informative and still specific
critique of semantic network model
doesn’t predict typicality effects, there’s little evidence of cognitive economy, and “reverse distance effects” exist
reverse distance effects
“a cat is an animal” is verified faster than “a cat is a mammal” despite cat to animal being a further distance
connectionist model
computer models that simulate cognition in real brain networks
4 elements of a connectionist model
input (to receive), hidden units (to receive), output units, and connection weights (to determine if theres activity in the next unit)
back propagation
an error signal transmitted back through the circuit-changes connection weight until there is no more error signal
the connectionist approach also explains
the generalization of learning, and the “graceful degradation” of machines and the human mind