lecture 4 - concepts and categories - what things are Flashcards
cognition
attention
language
memory episodic memory
concepts semantic memory
memory systems
diagram in notes
tromp et al ageing research reviews 2015
selective attention = stm and working memory
long term memory
semantic memory
inference
do you know if the following things have a liver?
* It probably never occurred to you to wonder if these things have livers.
* But for most of the items, you probably had a pretty good idea. Is this “memory of a particular event”?
* Not really - you don’t “remember” that the aardvark has a liver. You “infer” it from things you know about aardvarks and other animals.
* Knowledge is inferred from what we know about other related things
Helps us to make sense of the world and make predictions
semantic memory
- Knowledge about things in the world and their inter-relationships: words & meanings, objects, places, people
- “encyclopaedic” knowledge of facts shared by members of a community
- For example “Snow is white”,
- “Margaret Thatcher” was the first female prime minister of the UK”
brief recap
- Semantic memory is knowledge about things in the world (ideas, concepts, facts)
Episodic memory (next lecture) is memory about specific events
concepts and meanings - how are they organised?
triangles - three straight lines, three angles
dogs - animals, mammals, fur, four legs, bark, pet
fruit - parts of a plant, edible, sweet
concepts for organising the world
- We don’t just experience things in the world, we organise that experience
- We see individual instances (exemplars) as belonging to categories/classes/concept
- Knowing what something is
- ≈ what category it belongs to
- ≈ what it’s similar to/belongs with
- ≈ we know lots about it even though we haven’t actually encountered it before
Meaning of a word = accessing its concept
organisation of semantic information
Semantic Networks
Defining Attributes or prototype account
semantic networks 1
organising knowledge of what things are
defining attributes - an attribute possessed by all members of a category and one that distinguishes it from members of other categories
diagram in notes
basic level categories - critical to many activities eg afford particular modes of interaction. balance between generality (economy of information storage / access) and specificity (‘practicality”). a product of experience.
Semantic networks 2
how to investigate organisation? - reaction times (how long it takes to retrieve information)
economy - each subordinate category inherits attributes of superordinate ones - only need to be stored once
accessibility - information about categories accessed by searching through ‘nodes’
prediction - predictions about specific instances can be made, even if they haven’t been directly encountered. important as every situation we encounter is novel, even if its is similar to a previous one - we need to know how to respond to it
evidence for defining -attribute view
‘Does a dog bark?’ Versus ‘Does a dog reproduce?’
‘Is a Dalmatian a dog?’ versus ‘Is a Dalmatian a mammal?’
When asked to list defining attributes, people tend to start with ones on same level as ‘probe’ concept
Speed of response depends on ‘distance’ travelled to find information
problems with defining attributes
Certain attributes seem to be more salient (i.e., ‘stand out’) than others
e.g., people mention pink as an attribute of SALMON more often than has fins
Distance explanation: “has fins” is further away in hierarchy (attribute of superordinate fish)
But could also be Salience: Differences in speed of retrieval might be about difference in salience (more obvious attribute of salmon that distinguish it from other fish) rather than ‘distance’
‘has fins’ further away in hierarchy (attribute of superordinate fish), but also less salient (‘obvious’) than ‘pink’ as an attribute of salmon. Slower reaction time might reflect greater ‘distance’, or it might just be about salience
salient features depend on context
Salient feature changes depending on context, so we can’t even necessarily say that something’s salient defining attributes are properties of the ‘thing itself’
As salience changes, decisions about what things go together change (i.e. different organisation/categories) – so it isn’t determined by the thing ‘out there’
defining attributes - context
An object’s defining attributes seem to vary depending on our perspective, on the context, etc.
Suggests categories might be more flexible than the defining attribute view might imply
i.e., possession of a particular attribute doesn’t necessarily lead to a particular instance being assigned to a particular category
What something is is not
necessarily as obvious as it
might seem
Knowing what some individual thing “is” involves assigning it to a category BUT exactly defining what makes a category is not a simple matter
Difficulty being definitive about what the defining attributes are
What are the defining attributes of a GAME?
A property that all games possess and that distinguishes games from other activities
typicality - prototype view
Not all examples are equivalent, even though they possess the same defining attributes
Some examples are seen as better/more typical instances (prototypes) of a category than others
Robin versus canary
Typicality determines how long it takes people to make judgements about things
Relates to the number of
attributes a given category
member possesses
category membership is ‘graded’ rather than ‘either/or’
Even when all members possess all necessary and sufficient attributes, membership
is graded psychologically (e.g. 3 more typical than 2)
organisation of semantic information - exemplar account
Exemplar (or instance) based accounts:Categories as clusters of exemplars
(specific instances of things we’ve encountered)
Graded membership – certain exemplars are more typical members because we
have experienced them more often
diagram in notes
categories as ‘clusters’
graph in notes
categories as ‘clusters’ of exemplars in multidimensional space. no clear boundaries. correlations amongst features still enables prediction (cf. subordinate ‘inheritance’ account of prediction. Hierarchy as ‘zooming’ in and out
Focussing on different dimensions -> different categories emerge
graded ‘ad hoc’ categories
Focus on different ‘dimension’ of thing in order to form category
Things you could throw at a cat?
Stapler
Shoe
Banana
Guitar
Mobile phone
TV remote
Graded ad hoc categories, people agree on what belongs/doesn’t belong, just like ‘natural’ categories
Defining Attributes c/w exemplar account - Similarities between Defining Attributes (DA) and Exemplar (E) accounts
Importance of similarity – stimuli are placed into categories by
DA: comparing them with defining attributes/ prototypes
E: comparing with other exemplars previously encountered
Assume same general cognitive process:
new stimulus –> activation of concept in memory –> comparison/ judgement about resemblence —> categorisation
Defining Attributes (DA) versus Exemplar (E) accounts
What something is
DA: determined by possession of defining attributes (comparison with prototypes)
E: variable – categories emerge from structure of the world and our interactions with it (comparison to multiple known exemplars)
Prediction/knowledge of novel objects and events
DA: hierarchical inheritance of attributes
E: correlated features
What do we ‘store’?
DA: only details relevant to the particular concept/level
E: everything about everything (?/most salient patterns?)
DA -
pros - economical, fast judgements
cons - less flexible ( can’t account for atypicality, variability)
E -
pros - flexible ( can account for atypicality eg penguin as bird and variability)
E seems to be used with more experience (more encounters of exemplars)
cons - less economical
prototype and exemplar category learning
image in notes
A = exemplar model. accuracy increases the closer example is to previous examples
B = prototype model. accuracy increase the closer example is to prototype.
found people use both models
Bowman, Iwashita and Zeithamova 2020
prototype and exemplar representations in the brain
Prototype-related activation in ventromedial prefrontal cortex and anterior hippocampus (representation of across category attributes -> memory integration)
Exemplar-related activity in inferior frontal gyrus and lateral parietal cortex (representation of individual exemplars -> memory separation)
Depending on task demands, individuals form representations at both levels
measured brain using MRI scanner when people doing task. if higher blood flow to area of brain this area is active in this task.
found use both exemplar and prototype way of processing
Bowman, Iwashita & Zeithamova eLife 2020
Semantic dementia
- Progressive, selective loss of semantic knowledge in any modality
- Profound loss of word meanings: evident in comprehension & production (empty speech)
- Inability to recognise objects
Other cognitive abilities (e.g., episodic memory) and other aspects of language (syntax, phonology, pragmatics) seem to be much better preserved.