1. Concepts and Categorization

2. Categorization and Concepts
Basic cognitive function is to categorize
Use experience to aid in future behavior and decision-making
Cognitive economy
Concepts
Mental representation of a category serving multiple functions
We can use associations to organize the environment and our behavior
Distill our experience (knowledge) by utilizing functional relations

3. Functions of Concepts Classification
Determine category membership
Understanding, making predictions, inference
Once classified one can then understand its relevant parts, know how to interact with it, infer other properties
Explanation and Reasoning
For example, of others’ behavior
Learning
New entities compared to and understood in terms of old and provide feedback for modification
Communication
Shared concepts and categorization allow for easier expression of ideas to others

4. Categories Categories
Collection of objects, attributes, or actions, etc.
List of concepts
Hierarchy
Set of entities or examples picked out by the concept
How is experience distilled? How are functional relations established?
Category learning
How is knowledge represented in a category?
Structure
Schema
General knowledge structure that integrates objects, attributes, and actions into a cohesive representation
Script
Sequence
How do we use categorical knowledge?

5. Classification
Determining the category membership of various things (objects, properties, abstractions etc.)
Allows for treating otherwise discriminable entities as similar
Similarity as the organizing principle for categories and categorization

6. Structure of Categories
Classical View
Natural categories were structured in terms of necessary and sufficient features
If some entity has the set of necessary and sufficient features, it belongs to that category, otherwise it does not
Rigid category boundaries

7. Classical view Problems
Duck-billed platypus and brown dwarf
There simply do not seem to be defining features for many categories
Perhaps features are not available to consciousness? Uncertain as to whether the necessary feature is present?
Unlikely as folks are in disagreement as to what would constitute category membership (even with themselves at different times)
Even when certain, some examples are obviously better than others
Bye-bye classical view

8. Probabilistic View
Certain features may be necessary, and so weighted heavily in categorization
Probabilistic features, which are usually present but not always, will also influence categorization
E.g. Flies, for birds
How might we classify and represent structured knowledge?
Features/Typicality
Theories
Prototype
Exemplar

9. Features and typicality
Some instances may have more features than others
The more frequently a category member’s properties appear within a category the more typical a member it is
Robins vs. Penguins
Arrange objects based on some attribution.
Comparison to average member (central tendency)
Based on experience with category which may be different for different folks
Great Dane
Chihuahua
Labrador
“Dogs”

10. Prototype
Categorization instead may reflect typicality judgments based on comparison to an ideal
Concepts as abstractions
People abstract common elements of a formed category and use a common representation to stand for that category
How is the category updated?
Family Resemblance
Overlap of common attributes
Classification is made based on overlap between prototype and exemplar

11. Prototype
The prototype view can explain both typicality effects and the fact that prototypes that had not been previously presented are correctly classified (even more accurately)
Problems with prototype explanation
Doesn’t take into account category size or variability in examples
Context
What may be more typical in one setting may not be elsewhere
Correlations among attributes
E.g. smaller birds more likely to sing
Implies linear separability among categories
Categorization is perfect by adding up and weighing the evidence from features present
If this is not the case for separating categories, one would be hard pressed to come up with worthwhile prototypes

12. Exemplar theory Exemplar theory
Sort of a bottom up approach to categorization
Each instance is compared to others from past experience
Category arises by the lumping together of similar exemplars
Similarity based retrieval
Since the exemplar approach retains more information about the category itself it gets around some of the problems faced by the prototype theory (e.g. context effects), but also how a prototype could be recognized at test when wasn’t presented previously
Has similarity to previous examples and activates those stored representations

13. Exemplar/Prototype theory
Hybrid view
Perhaps a little of both*
It may be that concepts rarely consist of only prototype or exemplar representation
Once rule is learned categorize according to it. When exceptions arise, use an exemplar approach
E.g. grammatical rules
MC’s thought for the day: metacategorization
How do we classify the empirical evidence as supporting (belonging to) one theory or another?

14. Between Category structure
Up to this point the discussion has focused on classifying items within one category or another i.e. how a particular category is represented
Within category structure
But how are categories themselves organized?
Between category structure

15. Types of Categories Examples Different processes required?
Abstract vs. Concrete
Love vs. Mammal
Hierarchical vs. Non
Mammal vs. woman
Different processes required?
Hard to determine difference in kind

16. Hierarchical
Membership assumes a hierarchy such that classification in a subordinate category means an exemplar belongs to the superordinate category
Poodle  Animal
Basic level
The default category classification
How will an item be typically classified?
Poodle as dog rather than animal
The basic level is found at a middle level of abstraction (e.g. between type of dog and more abstract categories like Living)
Typically learned first, the natural level at which objects are named and the level at which exemplars are likely to share the most features
With expertise, the basic level may move to a subordinate level
Child: Dog vs. Cat
Adult: Poodle vs. Irish setter
Expert: Minature vs. Toy

17. Structure of Categories
Rosch
Hierarchal structure of concepts
Vehicles
CAR
TRUCK
BOAT
Sedan
Sports
SUV
Garbage
Row
Yacht
-Corvette
-Mustang

18. Structure of Categories
Vertical = Level of abstraction
Horizontal = variability within category
Vehicles
CAR
TRUCK
BOAT
Sports
SUV
Garbage
Row
Yacht
Sedan

19. Vertical Structure Vehicles CAR TRUCK BOAT
Superordinate
CAR
TRUCK
BOAT
Basic
Sedan
Sports
SUV
Garbage
Row
Yacht
Subordinate
Superordinate = defines category
Basic = overlap of common features
Subordinate = examplars

20. Properties of Hierarchy
Each level gives a similar degree of information
Converging operations for Basic Level
Common attributes
Shape similarity
Ease of labeling
Similar verification time
Superordinate
Vehicles
CAR
TRUCK
BOAT
Sports
SUV
Garbage
Row
Yacht
Sedan
Basic
Subordinate

21. Non-hierarchical No clear structure No clear hierarchy, no basic level
How would you classify yourself?
No clear hierarchy, no basic level
E.g. socially relevant categories to which a member may belong to several
The various applicable categories can be seen as competing for classification rights
Those used more frequently and recently will be more likely applied for classifying a new instance
E.g. gender, race

22. What processes are involved in categorization?
Does judgment of similarity in and of itself explain categorization?
Variable
People’s judgments of similarity change depending on the situation
Medin Goldstone & Gentner (1993)
Depending on which pair of objects shown would change what determined a judgment of similarity

23. Similarity
What constraints if any are placed on determinations of similarity? What constraints does similarity place on what counts as a feature?
Rocks and squirrels
Both exist, are bounded, can be run over etc.
Can similarity alone explain classification?
Perhaps serves as guideline rather than definitive delineator
Abandoned if additional info suggests it is misleading
Gelman & Markman (1986)

24. Classification by theory
Organization of concepts is knowledge-based as opposed to similarity-based
Apply theory to the data
Concepts develop and change with experience/evidence
E.g. various mental disorders
Theory and Similarity
Theories will affect similarity judgments
Similarity constrains theory
Psychological essentialism
The way people approach the world
Essences of things (e.g. what makes male or female)

25. Models of Categorization
Generalized Context Model
Exemplar-Based Random Walk
See Nosofsky link on class webpage
ALCOVE
Combinations of exemplar and rule-based processing
Decision-bound approaches
Rational model
Anderson

26. Categorization and memory
What memory system or systems are used during category learning?
Essentially theories of category learning virtually all assumed a single category learning system
E.g. exemplar theory
When a novel stimulus is encountered, its similarity is computed to the memory representation of every previously seen exemplar from each potentially relevant category, and a response is chosen on the basis of these similarity computations
Category learning uses many, or perhaps all of the major memory systems that have been hypothesized by memory researchers.

27. Working memory Heavily used in reasoning and problem solving
Could be the primary mediating memory system in tasks where the categories are learned quickly.
Two possibilities:
The categories contain few enough exemplars that the process of explicitly memorizing their category labels does not exceed the span of working memory
Though possible, probably unlikely, however if comparisons are made to a single ideal or prototype perhaps
Working memory could be used if the category structures were simple enough that they could be discovered quickly via a logical reasoning process.
In other words if the means of categorization can be reduced to one or two dimensions (e.g. some rule)

28. Working memory Evidence
Single rule-based categorization is interfered with in divided attention tasks where more complex category learning is not
Rule-based category learning is possibly mediated by a conscious process of hypothesis generation and testing.
If the feedback indicates response was incorrect, then must decide whether to try the same rule again, or whether to switch to a new rule
If the latter decision is made then a new rule must be selected and attention must be switched from the old rule to the new.
Such operations require attention and working memory.

29. Episodic and semantic memory
Memory for personally experienced events and general world knowledge
No empirical evidence from category learning suggests separate contributions of episodic and semantic memory systems
These declarative memory systems are used during explicit memorization, so category structures that encourage memorization are especially likely to be learned via these systems.
Two conditions:
First, memorization is an especially effective strategy if each category contains a small number of perceptually distinct exemplars.
Second, other simpler strategies are ineffective
Indirect evidence from successful exemplar-based models that assume use of stored representations from prior learning
Some direct evidence from amnesiacs that suffer in category learning

30. Non-declarative memory
Procedural knowledge
Memories of skills that are learned through practice
Little awareness of details
Is slow and incremental and it requires immediate and consistent feedback
Like declarative memory systems, would not be utilized for simple rule-based categorization
Example of radiologists and tumors
Many exemplars in the set of X-rays, but identification takes practice and process is not well-defined by practitioners
Evidence
Information integration (more complex multi-dimensional categorization) tasks affected similarly as serial reaction time tasks
Changing the way in which one responds (key press) leads to poorer performance that is not seen in simple rule-based categorization tasks
As with procedural tasks, complex category learning can be hindered without appropriately timed feedback

31. Perceptual learning
The specific and relatively permanent modification of perception and behavior following sensory experience
No behavioral evidence implicating the perceptual representation system, jury out on neuropsych evidence

32. Use of categories in reasoning
Ad hoc categories
Spontaneously constructed for the purposes of some goal
Constructed differently from other categories?
Show similar results e.g. typicality effects, however, more of a comparison to an ideal rather than prototype
Gist: goals can affect category structure
Conceptual combination
Construction of new concepts by combining the previous representations
Recall structural alignment
Typicality may not be predictable from previous concepts
Properties of new concepts may not be present in old.

33. Use of Categories Classification Explanation
Process of assigning objects to categories
Treat (use) different “things” as the same
Explanation
Bringing knowledge to bear in novel situation
By classifying a novel event into an existing category, an explanation is provided.

34. Use of Categories Prediction Reasoning
Understanding of an event guides reactions and behaviors
Allows us to expect certain outcomes or properties
Reasoning
Categories are the basis for inferences
Allow categorical knowledge to stand for an event
Allows for “filling-in” of ambiguous information
Allow for conceptual combinations
Paper Bee
Wooden Spoon

35. Other stuff
Just because two instances might be lumped together under one category, does not mean we experience them similarly
Ferrari vs a Tempo
Some would say we experience events, not categories
Recall ‘situated action’