L15 - Prototypes - Exemplars and Category Learning

Question 1

Multi-Dimensional Scaling (MDS) transfers similarity into ______

Answer 1

distances

Question 2

What is the major assumption of the Prototype view?

Answer 2

Our conceptual knowledge is stored in the form of an abstraction

Question 3

In the prototype view, how do we represent a category?

Answer 3

We abstract out the central tendency of a category on the basis of all our experiences with the category

• Category representation consists of a summary of all the examples of the category called the prototype*
• • this is an abstraction*

Question 4

What does the exemplar view suggest about category representation?

Answer 4

That we simply store in memory every example of a given category that we encounter.

Question 5

According to the exemplar view, our conceptual representation consists of -

Answer 5

all the individual members of a category, known as exemplars.

Question 6

How can you view the prototype and exemplar views?

Answer 6

As a continuum

Prototype = total abstraction

Exemplar = zero abstraction

Question 7

What is the positives and negatives of the prototype view?

Answer 7

Positive - it reduces memory load

Negative - memory load reduction comes at the cost of information

Question 8

What is the positives and negatives of the exemplar view?

Answer 8

Positive - useful because it retains specific information

Negative - but it comes at the cost of increased memory load

Question 9

What is the problem with the prototype view?

Answer 9

Can we store everything we know about a category member as a ‘prototype’?

- What does it look like, is it merely a set of features?

Question 10

What is the problem with the exemplar view?

Answer 10

Does it seem likely that store in my memory every example of a category that I come across?

Each view seems implausible at its extreme

Question 11

What is the issue with the fact that both exemplar and prototype models at their extreme are implausible?

Answer 11

That we need to choose between one or the other in order to implement a computational model

Question 12

The family resemblance model is an prototype or exemplar model?

Answer 12

Exemplar

The family resemblance model predicts typicality as a function of featural overlap between category members (exemplar)

Question 13

The poymorphous concept model is an prototype or exemplar model?

Answer 13

Prototype

The polymorphous concept model predicts typicality as a function of featural overlap between category members and an abstracted feature list representing the category name (prototype)

Question 14

Typicality can be predicted as a function of the distance between
each category member in a multidimensional space and the central
tendency of that category

Is this using a prototype or exemplar model and why

Answer 14

Prototype

Because it is a measure of central tendency

Question 15

Typicality can also be predicted as a function of the mean distance between each category member in a multidimensional space and each other category member in that space

Is this using a prototype or exemplar model and why

Answer 15

Exemplar

Involves all the members of the category

Question 16

What type of stimuli do category learning experiments use?

Answer 16

Tend to employ stimuli that bear little resemblance to ‘real-world’ categories, but can be easily manipulated

– Hue, Brightness, Saturation, Size, Shape, etc

Question 17

What are the two phases of category learning experiments?

Answer 17

1. Learning phase

2. Transfer phase

Question 18

In category learning experiments, what does the learning phase consist of?

4 steps

Answer 18

Question 19

In category learning experiments, what does the transfer phase consist of?

Answer 19

After the learning phase, you introduce new stimuli to the participants and ask them to categorize them

Record how they categorise the stimuli based on on what they experienced in the learning phase

Question 20

What happens when participants have to attend to “multiple category dimensions” in category learning experiments?

Answer 20

They find it harder to learn the categories and make more errors

The less dimensions to learn the quicker we learn the categories

Question 21

How do category learning experiments give us insight to?

Answer 21

This gives us insight into our ability to generalize from a stored category representation to novel stimuli.

• Manipulating the category structure allows us to test different theories about the processes underlying categorization and generalization.

Question 22

Answer 22

Question 23

Many category learning experiments consist of a third stage, what is this stage?

Answer 23

Similarity Rating Stage

The empirical categorization decisions and/or response latencies are computationally modelled using similarity rating in order to generate MDS scaling representation of the stimulus space.

Question 24

How do we computationally model category learning experiments?

Answer 24

Involves using a similarity rating in order to generate a Multi-dimensional scaling representation of the stimulus space.

Question 25

What is the most well known and commonly used model for the third stage of category learning

Answer 25

Generalized Context Model (GCM) (Nosofsky, 1986).

Question 26

What does Generalized Context Model (GCM) (Nosofsky, 1986) state about the probability of a stimulus being categorised as a member of a category.

Is this model prototype or exemplar?

Answer 26

The probability of a stimulus being categorized as a member of a given category is a weighted function of the distance between the target stimulus and the members of the two categories in the space

(exemplar)

We use distances in MDS to convert into similarities

Question 27

Describe The MDS-based Prototype Model (MPM) for calculating the probability of a stimulus being categorized as a member of a given category.

(Minda & Smith, 2001)

Answer 27

The probability of a stimulus being categorized as a member of a given category is a weighted function of the distance between the target stimulus and the prototypes (central tendencies) of the two categories in the space

Question 28

Is the GCM (exemplar) or the MPM (prototype) better at predicting which member will belong to a category

Answer 28

Both do well - however GCM does better than MPM

Question 29

Exemplar models seem to do slightly better at representing how we apply membership to categories.

Does this mean that this model is right? Why?

Answer 29

No

The true repesentation likely lies in between no abstraction (exemplar) and full abstraction (prototype)

Question 30

The true way we representation lies in between exemplar and prototype - what model was developed to account for this?

Answer 30

Vanpaemal & Storms (2008): Varying Abstraction Model (VAM)

Question 31

How did Smits, Storms Rosseel and De Boek (2002) test whether GCM or MPM models work when we categorise things in real life?

Answer 31

• • Data set: pictures of 79 well-known fruits and vegetables, and 30 novel stimuli (mainly tropical fruit and veg)
• • One group of participants made pairwise similarity ratings
• • One group asked to categorize all 109 stimuli as either fruits or vegetables

Question 32

What were the results of the Smits, Storms Rosseel and De Boek (2002) test which tests whether the GCM matched how people categorized objects in the real world?

Answer 32

The GCM is accurate for accounting how human categorize.

Similarity can account for these types of decisions

MPM also does equally well