week 3:Categorization

Question 1

definition of concept/ category

Answer 1

A concept refers to a mentally possessed idea or notion,
- category refers to a set of entities that are grouped together.

Question 2

what is Categorisation

Answer 2

the ability to form equivalence classes of discriminable entities

Question 3

the ability of concept

Answer 3

• allow us to identify ambiguous features
• allow for generalization. The probability of generalizing a category drops off quickly with decreasing similarity
  -reduce the complexity of environment and allow for the organization of knowledge

Question 4

Grounding by similarity groups

Answer 4

• Natural categories
• Man-made artifacts
• Ad hoc categories
• Abstract schema or metaphors

Question 5

The structure of categories

Answer 5

-Categories are organised into hierarchies: Superordinate, Basic, Subordinate
-its hard to guess which item belong to which category without seeing all the available data
-Most of the information is contained at the Basic Level

Question 6

shape similarity of basic trait

Answer 6

-Basic level shape averages were identified more accurately than superordinate level shape averages
-Briefly presented shapes were identified more accurately when the shape was drawn from a basic level category
-Basic level objects were identified faster

Question 7

Within-category feature similarity

Answer 7

Cue validity: do objects with a certain feature (wings) belong to the category (birds)?
Category validity: do members of the category (birds) have a certain feature (wings)?

Question 8

interesting property of basic level

Answer 8

When shown a picture, people tend to use the basic level name
Basic Level Names are Learned First
high within category similarity

Question 9

Classical View of Categorisation

Answer 9

• Categories are definitions of necessary and sufficient conditions
• The definition works for every member of the category
• Categorisation requires checking for the presence of these definitional features
• Rule-based theories arise from this view

Question 10

Listing attributes:

Answer 10

® Rosch (1978) showed that participants listed a far greater number of attributes for the basic level compared to the superordinate level of categorisation, and only slightly more for the subordinate level compared to the basic level.
For the superordinate level participants listed function terms (e.g. makes music);for the
basic level participants listed nouns & adjectives (e.g. strings, wooden); for the subordinate level participants listed adjectives (e.g. brown).
® When Rosch conducted this same experiment with biological categories, a different pattern was observed. For biological categories, the number of attributes listed for the superordinate category was far closer to the basic level than for the nonbiological level categories. This suggests that the basic level terms that we have for biological taxonomies are used in a more superordinate level.
® Rosch argued that people’s basic level categories preserve the intrinsic correlational structure of the world

Question 11

Hypothesis Testing Strategies

Answer 11

• Four types of strategies were identified: Scanning strategies and focusing strategies
® Simultaneous scanning: where the participant considers all of the attributes at once; the category never contains attributes that do not belong to the target (e.g. ‘the target is three green shaded squiggles’).
® Successive scanning: where the participant considers only one attribute, until that attribute gets disconfirmed. This is not demanding, but is inefficient (because it takes a lot longer to work your way through all of the different attributes).
® Conservative focusing: where the participant changes only one attribute on each trial. If the feedback is ‘no’, then it can be assumed that the attribute is not part of the category For example – (Trial 1) 3 red shaded diamonds, (Trial 2) 3 green shaded
diamonds).
® Focus gambling: where the participant changes all but one attribute. If the feedback is ‘yes’, then that attribute can be identified as the target-defining attribute. However, the probability of receiving affirmative feedback using this strategy is very low.

Question 12

Rule-learning Studies

Answer 12

• Number of attributes comprising the target category can be varied
• The way in which the attributes were combined could also be manipulated

Question 13

Learning logical concepts of classical theory

Answer 13

® As the complexity of the category increases, the proportion correct after spending a period of time examining the different category allocation decreases

Question 14

Support for the classical view

Answer 14

• People seem to use rule-based hypothesis testing strategies in category verification studies
• Concepts defined by logical rules can be quantified by their complexity
– Complexity affects accuracy and learning

Question 15

Challenges to the Classical View

Answer 15

• The structure of natural categories is not characterised by definitions
• Natural categories have a fuzzy structure in which some members are more typical than others
• Natural categories have a hierarchical structure in which some levels are more “basic” (i.e., readily accessible) than others

Question 16

Typicality and Family Resemblance

Answer 16

-Natural categories are not homogenous Some members are more typical than others
-Natural categories are organised based on family resemblance
-Most attributes are listed for a single item. Shared attributes are very rare, on average, there is only one attribute shared by all 20 category members
- correlations show that the more typical an item was, the higher number of attributes that it shared with other objects

Question 17

Characteristic of typical member

Answer 17

-Typical category members are verified more rapidly
-Typical instances are learned faster
-priming results in faster TIs for more typical category members

Question 18

Typicality and Generalisation

Answer 18

• Category Induction tasks illustrate key principles of how typicality affects generalisation
• Induction: Generalizing from the particular to the general
– Given a set of examples, what is the general conclusion that one could draw

Question 19

Category Induction

Answer 19

-Generalization is much greater for typical category members
-Generalizability is dependent upon the conclusion category size. A smaller conclusion category size leads to greater generalizability, presumably due to the size of the generalization required
-Generalizability is greater when premise examples are more variable. The less similar the premises are amongst themselves

Question 20

Theories of categorisation: Prototype model

Answer 20

® Assumes that we represent categories through abstractions of members from that category.
® This allows us to explain typicality effects by allowing individual category members to be compared to a ‘prototypical’ category member.
® The prototype is similar to members of its category, and is highly dissimilar to members of other categories

Question 21

dot pattern task

Answer 21

-a prototype dot is used to create a series of dot picture. the distortion is then used to create two category, test subject would need to identify which of cat they are in
-the result show: prototype,low,high,random

Question 22

The 5-4 Task

Answer 22

-the prototype are generally put into the correct category

Question 23

Problem with prototype model

Answer 23

® Prototype models cannot solve categorisation problems with non-linearly separable rules but people can do it

Question 24

Theories of categorisation: exemplar theory

Answer 24

® Suggests that we store categories by storing all of the members of those categories in our memory, and basing category decisions on our memory retrieval.
® People are more likely to categorize an object into which categories are retrieved from our memory more often based on their similarity.
-exemplar model can predict performance on non-linearly separable structure and rule like seperation
-Exemplar models also predicts prototype enhancement
-Incorporates a mechanism for selective attention, attention increase accuracy