Concepts and categorization
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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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Concepts and Categorization

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Concepts and categorization

Concepts and Categorization


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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