TASK 2 - CATEGORISATION Flashcards
category
- = theoretical class of objects (tied to real life objects)
- = mental representation of objects
- = classification systems
categorisation
= put objects we perceive into a clusters/category
reasons why we categorise
- cognitive economy = store less information by ordering/clustering
- communication, definition boundaries
- decision making
- make predictions
category types
NATURAL: animals, plants; reflect correlational structure of environment
FORMAL/ABSTRACT: prime numbers, adverbs
FUNCTIONAL: objects with functions; study material, items for travelling
AD HOC: no stable mental representation, things that are not generally together but can come in same category; things you would save from a burning house
conceptual/taxanomy hierarchy
superordinate level: ANIMAL; children - high coverage: often applicable - low predictive value: not informative basic level: DOG; everyday life - trade-off subordinate: GREY HOUND; expert - low coverage: more explicit - high predictive value: much information
basic level effect
= entry point; first contact between perception and semantic information
- faster classification: thing that comes first to mind
- better discrimination
- more detailed description
- -> shifts with expertise: subordinate level
categorical learning
- concept formation = abstract representation of a thing, gradually link representation to name
- concept learning = explicitly being told what is/isn’t object, applying concept + feedback
structure within categories
= where do things belong in category; affect which category to use to classify object
- stable core
- inferred features, associations
- ideals, myths
- present context; culture and experience
- frequently activated knowledge
similarity-based categorisation
= assess category based on similarities
- tabula rasa assumption: blank slate in categorising new object
1. classical theories
2. prototype theories
3. exemplar-based theories
- classical theories
= category is represented by defining features that specify clear boundaries
x categories either belong or don’t belong to one category
x must be a perfect match
BUT not all members are equally typical to a category –> PROTOTYPE
- prototype theories
= category is represented by characteristic features (= prototype)
- prototype as centre (= average representation)
- membership function: fuzzy sets, family resemblance; typicality effect
- more abstract than exemplar: need more knowledge of category
x ignorance of less important features, features that go together
x people are affected by experience
membership function
= how we determine whether something is part of a category
fuzzy sets
example: dogs, fox, wolf
- dogs and fox are similar BUT dogs and wolfs are even more similar
family resemblance
= things in a category resemble one another in a number of ways (allows variation)
typicality effect
= ability to judge highly prototypical objects more rapidly
- the more typical the object for the category –> the faster identification, easier retrieved from memory
- naming = prototypical members are named first
- priming = prototypical members more affected by priming
- exemplar-based theories
= category consists of set of examples/instance of category
- assign object to category with most similarity to prevalent example (usually most typical)
- use frequently, because easiest
x memory capacity
x sometimes we actually use defining features
x unclear boundaries
hybrid models
= combination of all similarity-based categorisation
rule-based categorisation
= applying a rule to object
- selective attention for every relevant feature (bird)
- for each feature: check whether minor values are correct (characteristics of a bird)
- integrate findings
- analytic, involves high working memory load
- yields certainty and flexibility
theory-based categorisation
= theory underlying category best explains features of category members
- categorisation based on explanations –> knowledge-based
knowledge-based categorisation
= people have knowledge which they apply to categorise
- categorisation is not only based on perception
neuro-imaging evidence
SIMILARITY-BASED: - cerebellum, visual cortex RULE-BASED: - cerebellum, visual cortex, parietal cortex, pre-motor cortex, prefrontal cortex --> more cognitive
multiple systems theory
WM: rule-based
- frontal + parietal
EXPLICIT LTM: similarity-based (exemplars, prototypes)
- medial temporal lobe & parts of PFC + parietal cortex
IMPLICIT LTM: unconscious perception + similarity-based (classical)
- NOT active PFC, posterior parietal cortex, medial temporal lobe
cultural differences in categorising
- Whorf hypothesis: difference in language can lead to differences in categorisation
- -> categorisation advantage from word variety (Russian vs. English)
essentialism
= there is a causal relation between objects; there is an intrinsic cause/essence in everything that cannot be changed
- think a category cannot change (because essence is the same) –> close mindedness = lack of creativity
enhances stereotyping
(lay) racial essentialism
= race is the disposition/essence of things/people; race as fundamental source of human division that has an inalterable biological basis
- inductive potential: group membership is telling of members’ characteristics
- inalterability: group membership is fixed for a relatively long duration
- hypo descent: assigns multiracial individuals to subordinate racial category
is essentialism similarity- or knowledge-based?
MY OPINION: knowledge-based: you base your categorisation on prior knowledge/beliefs
folk biology (PRESENTATION)
= tendency to view humans as one species among many
≠ anthropocentrism = human as centre/most important element of existence
- most in children up to 10 BUT also in adults
educational differences in anthropocentrism (PRESENTATION)
Western psychology students: most likely to exhibit anthropocentrism
Western biology students: perspectives highlighting similarities between among species
Eastern oriental medicine students: holistic thinking, least likely to anthropocentrism
