Concepts and Categorization

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 • We can use associations to organize the environment and our behavior • Distill our experience (knowledge) by utilizing functional relations

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 • 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 • 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 • 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 • 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 • 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

“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 • 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

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

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

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

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

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 • 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? • 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 • 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 • 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 • 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 • 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 • 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 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 • 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 • 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 • 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 • 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 • 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 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 • 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’

Concepts and Categorization