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Concepts and Categorization. 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

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categorization and concepts
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
functions of concepts
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
categories
Categories
  • Categories
    • Collection of objects, attributes, oractions, 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?
classification
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
structure of categories
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
classical view
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
probabilistic view
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
features and typicality
“Dogs”

Great Dane

Chihuahua

Labrador

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
prototype
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
prototype1
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
exemplar theory
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
exemplar prototype theory
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?
between category structure
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
types of categories
Types of Categories
  • Examples
    • Abstract vs. Concrete
      • Love vs. Mammal
    • Hierarchical vs. Non
      • Mammal vs. woman
  • Different processes required?
    • Hard to determine difference in kind
hierarchical
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
structure of categories1
Structure of Categories
  • Rosch
    • Hierarchal structure of concepts

Vehicles

CAR

TRUCK

BOAT

Sedan

Sports

SUV

Garbage

Row

Yacht

-Corvette

-Mustang

structure of categories2
Vehicles

CAR

TRUCK

BOAT

Sedan

Sports

SUV

Garbage

Row

Yacht

Structure of Categories
  • Vertical = Level of abstraction
  • Horizontal = variability within category
vertical structure
Vertical Structure

Vehicles

Superordinate

CAR

TRUCK

BOAT

Basic

Sedan

Sports

SUV

Garbage

Row

Yacht

Subordinate

Superordinate = defines category

Basic = overlap of common features

Subordinate = examplars

properties of hierarchy
Vehicles

Superordinate

CAR

TRUCK

BOAT

Basic

Sedan

Sports

SUV

Garbage

Row

Yacht

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

Subordinate

non hierarchical
Non-hierarchical
  • No clear structure
    • 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
what processes are involved in categorization
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
similarity
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)
classification by theory
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)
models of categorization
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
categorization and memory
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.
working memory
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)
working memory1
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.
episodic and semantic memory
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
non declarative memory
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
perceptual learning
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
use of categories in reasoning
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.
use of categories
Use of Categories
  • Classification
    • 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.
use of categories1
Use of Categories
  • Prediction
    • 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
other stuff
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’
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