Concepts & categories Flashcards
Cognitive processes
Input (masses of information from the environment) –> cognitive processes –> Output (rational behaviour (mostly))
Categories & concepts
Classes of objects in the world, are concepts are mental representations of the categories.
i. e. Category: cats are a class of objects in the real world,
concept: we hold a conceptual representation of cats in our minds.
Why do we form conceptual representations?
- Data reduction: storing information about everything I’ve ever encountered would be an enormous strain on my memory. It is easier to store this information in terms of generalised conceptual representation.
- Generalisation: - i can use my conceptual representation to make inferences about new members of the category ive encountered.
- i can use my conceptual representation to make inferences about novel items that bear a resemblance to members of a given category.
Exemplars
Groups of exemplars form categories.
the properties of exemplars and categories are described by features.
The classic view of concept formation
- dominated our understanding of conceptual representation until the 1970s.
- arose from philosophy rather than psychology.
- Based upon the presence that categories are defined by the presence or absence of specific properties
what are defining properties? - membership of the category ‘squares’ is defined by the following properties: and
- in order for a shape to be a square it is necessary for the shape to have these properties.
- Furthermore, the presence of these properties is sufficient evidence to classify a shape as being a square
- Categories are defined by NECESSARY and SUFFICIENT features.
Necessity: If any of these features are missing, it is definitely not a member of this category.
Sufficiency: If all of them are present, then it is definitely a member of this category.
It therefore follows that - there are no in-between cases, and all members are equal.
Three main claims of the classical view of concept formation
- Concepts are mentally represented as definitions. A definition provides characteristics that are necessary and jointly sufficient for membership of that category.
- Every object is either in or not in a given category, there are no in-between cases
- Every member of a category is considered to be an equally representative member of the category as all other members of the category.
Problems with this classic view of concept formation?
Some different categories don’t work under the classical view… sports, emotions, fruit?
- binary category membership - e.g. A fruit is a fertilised ovary of a flowering plat…
apple, pear, bananas = fruit. but tomatoes, pumpkins, cucumbers = fruit?
- equality of category members - are rollerskates and hot air balloons equally good members of the category vehicles as cars and buses?
Empirical evidence vs the classic view
point2 - every object is either in or not in a given category, there are no in-between cases
Binary category membership?
- Hampton (1979) presented participants with lists of potential members of 8 categories (furniture, sport, birds etc) and asked them to rate their membership on 7 point scale.
- Category membership ratings formed a continuum ranging from 0 to 100 %
- some category members were always included or excluded, others were borderline cases.
This could be due to people having different conceptual representations…
But McCloskey and Glucksberg (1978) demonstrated that even individuals change their inclusion criteria over time:
- asked participants to make repeated category judgements (is olive a fruit? is chess a sport? etc.) for 22% of borderline cases the participants changed their minds.
therefore… category membership is not binary!
Equality of category members?
point 3. Every member of a category is considered to be an equally representative member of the category as all other members of the category.
- intuitively it seems that some category members are more typical/representative category members than others: e.g. a swallow seems more typical bird than a penguin.
- Rosch (1975) asked participants to rate the typicality of members of 10 different categories. The inter-rater reliability was extremely high.
McCloskey & Gluckberg (1978) found that category membership decisions could be predicted by typicality ratings:
- tables (mean typicality =9.83) always furniture
- windows (mean typicality= 2.53) never furniture
- wastebaskets (mean typicality =4.7) furniture 30% of the time.
Rips, shoben & smith (1973) found categorisation decision times could be predicted by typicality. = e.g. people are faster to confirm that robin is a bird than penguin.
Initial claim of the classical view: 1. Concepts are mentally represented as definitions. A definition provides characteristics that are necessary and jointly sufficient for membership of that category.
little harder to test.
An alternative approach - Family Resemblance.
Categories are not defined by necessary or sufficiently features, but by overlapping distributions of features.
‘Members of a category come to be viewed as… typical of the category as a whole in proportion to the extent to which they bear a family resemblance to (have attributes that overlap those of) other members of the category.
Empirical evidence vs the classic view - Rosch and mervis (1975)
- 20 exemplars (category members) from 6 different categories
- one group of participants rated the typicality of the exemplars (how typical is X as a member of category Y)
- Another group generated features for the exemplars (X has the feature ).
- A third group rated the applicability of the features in relation to each of the exemplars the feature ( is applicable to X, M, C, T, A & B).
Family resemblance
the weighted sum of featural overlap amongst categroy members.
strong correlation between typicality and family resemblance
- the graded typicality structure that we see in the categories is well described by the featural overlap of the category members.
- importantly, the features generated by the participants tended to apply to a small sub-set of the category members. Very few features appeared to apply to ALL of the members. None of the features could be construed as necessary and sufficient.
Graded Category Structure
- Typicality ratings suggest that categories have graded structure.
- Generation frequency data, and categorisation decisions and latencies also suggest that categories have graded structure.
- This graded structure is also well described by the Family Resemblance measure, a weighted measure of featural commonality amongst category members.
Example - Blargs
‘these are blargs, they can be described by the following features: body shape, body colour, antannae, number of legs.’
We can use this information to calculate their family resemblance.
‘the more similar an exemplar is to other category members, the higher its family resemblance, and the more typical it is of its category’
The Polymorphous Concept Model
Similar model implemented by Hampton (1979).
- in this case, the representativeness of an exemplar is a function of the degree of overlap between the features associated with the exemplar and the features associated with the category.
- the family resemblance model and the polymorphous concept model both use featural overlap to measure graded category structure.
Similarity
similarity data can be collected via a number of different procedures:
- pairwise or triad similarity ratings
- card-sorting tasks
- feature by exemplar matrices
The contrast model
According to the contrast model the similarity between the exemplars ‘i’ and ‘j’ can be calculated from the features common to the two exemplars, the features of i that are not present in j, and the features of j that are not present in i.
- the family resemblance and the polymorphous concept model are both special cases of the contrast model.
Using similarity to visualise semantic structure
- multi-dimensional scaling (MDS) provides a visual representation of the similarity relations between different objects or entities.
- converts similarities into distances: the more similar two items are, the closer theyw ill be located. The more dissimilar two items are, the further away they will be located.
MDS
- The spatial representations generated using MDS appear to reflect the structure of the categories that are being represented.
- we see this in perceptual data, and importantly, we see this also in conceptual data.
- in many cases, the spatial structure of the categories is readily interpretable.
- sometimes the structure is not so easily interpretable, particularly when the optimal dimensionality of the representation is greater than 3.
- in perceptual data, we can often see that the spatial representations reflect perceptual qualities that were not explicitly referred to in the similarity data collection process.
- in conceptual data, we often find that the spatial representations reflect measures of graded category structure such as typicality, generation frequency, etc.
Category structure.
- typicality = distance from category centroid.
- same for other measures of graded category structure (generation frequency, categorisation response time).
- but not for non-structural measures such as familiarity, age of acquisition, word frequency etc.
- Typicality reflects the similarity structure of categories.
- Flying prototype
- Vector projection
Multidimensional scaling of similarity data
- multidimensional scaling of similarity data can also tell us about differences in the way that different groups of people mentally represent different categories.
