Presentation on theme: "What’s in memory?-- Categorization. The importance of categorization What’s this? Does it have seeds on the inside? Does it have lungs? How do you know."— Presentation transcript:
What’s in memory?-- Categorization
The importance of categorization What’s this? Does it have seeds on the inside? Does it have lungs? How do you know it’s an apple, given that you’ve never seen it before?
Concept: mental representation to which you compare a novel object, to know it’s properties. Compared to concepts for guitar, piano, dog, fruitcake, etc.... Match to apple concept Other, non-visual properties become available (it has seeds, it’s edible, etc). Category: group of objects that have something in common Exemplar: particular instance of a category. Generalize: the ability to apply information gathered about one exemplar and apply it to another exemplar.
First big question: What does this look like? Compared to concepts for guitar, piano, dog, fruitcake, etc.... Match to apple concept Other, non-visual properties become available (it has seeds, it’s edible, etc).
Classical view A category representation is a list of necessary and sufficient features. E.g. to be classified as a grandmother, you must: 1) Be female 2) Be a parent of a parent
Classical view Thus, by the classical view categories are definitions. This idea works okay for some terms (e.g. kinship or legal) but poorly for most others. E.g. what are the necessary and sufficient condition for being a pear?
Sand pear Another odd shape
Common ratings, highest to lowest: Robin Starling Falcon Penguin This effect works for many, many categories (probably all) not just birds. Rate how “birdy” each of the following are:
How “grandmothery” are these women?
Data like this were taken to mean that the classical view is wrong. If the classical view were correct, the task should seem stupid. But there are gradations to category membership--this is called typicality--and it goes beyond cute demonstrations. Typicality affects: How quickly you categorize How you reason
Robins like to eat onions Do you think starlings like to eat onions? Owls like to eat onions Do you think starlings like to eat onions?
Demonstration Get out a piece of paper
Write the first four fruits that come to mind Apple Orange Pear Banana
Typicality categorization More typical exemplars come to mind first--you are also better at confirming that they are a member of a category. Furniture--sofa
Faster to verify more typical instances.
Basic More than just typicality--levels Basic level categories: most inclusive but members still share most of their features. E.g., most “birds” are “winged,” “lay eggs,” “sing” etc. Superordinate level category is one level more abstract. E.g., “animals” do not all share features: some are “winged” some are not; some are “tailed” some are not; some are “warm-blooded” some are not, etc. Subordinate level categories are less abstract than basic level. E.g., “wrens” are all very similar; only a few features differentiate a “house wren” from a “marsh wren.” Subordinate Superordinate
More examples SuperordinateBasicSubordinate
Basic level categories are argued to be psychologically privileged, meaning that’s what we usually use when thinking. People usually use basic category in language, and people are faster to verify an object name at the basic level than at the superordinate or subordinate level.
Note that basic level depends on expertise. If you’re an expert, what is a subordinate level for everyone else may be a basic level for you. That is, if you are a carpenter, what is the subordinate level for everyone else (ball peen hammer) is basic for you.
What type of representation can capture these effects? ????? Compared to concepts for guitar, piano, dog, fruitcake, etc.... Match to apple concept Other, non-visual properties become available (it has seeds, it’s edible, etc).
An influential answer has been “probabilistic, or similarity models.” Called probabilistic, because category membership is a matter of probability, not all-or-none. Similarity, because the likelihood of being a member of a category is calculated by computing the similarity of the exemplar to the category
Similarity model 1: prototypes Start with dot patterns = two prototypes Create exemplars by randomly moving some dots. Prototype A Prototype B Exemplar A1 Exemplar A2 Exemplar B1
Prototype experiment Create a deck of cards with exemplars of A’s and B’s (but no prototypes). Study exemplars until can categorize correctly One week later, recognition test for old, new, and prototype. Subjects were as good on prototype (which they’ve never seen before) as on old items.
How a prototype model works When you see a new exemplar you compare it to the prototype; the more similar it is to the prototype, the more confident you are that it belongs to that category. Thus, when you see the prototype dot pattern, you’re very confident that you know its category.
Prototype model It almost feels like the prototype model has to be right. How else can you account for the success in recognizing the prototype so well?
Problem: Ad Hoc categories Note that these effects all depend on your having a prototype of each category stored in memory. BUT you get the same graded typicality effects for categories that are much less common that “dog”
What would make a nice snack?
Problem for prototype model You get all the typicality effects for these “ad hoc” categories. They are called ad hoc because it’s assumed that you don’t have a prototype for them.
Exemplar model Note: Exemplar model also accounts for the fact that you can recognize specific dogs, not just the prototypical dog.
Exemplar model How does the exemplar model produce typicality effects? Stored in memory are lots of exemplars of birds that sit in branches and go “tweet tweet.” Therefore you’re comparing the new to-be-categorized exemplar to lots of typical birds.
More general: problems with similarity models How to select features? Similarity depends on context At times, we do seem to categorize via rules (as the classical view suggested)
Feature selection: Compare George W. Bush & a pack of DoubleMint Similar Weigh less than 6 tons Hear a whisper at 3 miles Able to run a 4 minute mile Described as “sweet” at least on occasion Different Respiration Performance on standard IQ tests Ruler-of-free-world status Wears a hat on occasion Is there a principled way to select a set of features?
Similarity depends on context Subjects asked to judge similarity of A to (B, C or D) or of E to (F, G, or H). Percentages are S’s who thought that face was most similar to sample. Note that B and F are the same, but % differ greatly, based on other faces present (i.e., context).
Categorization based on rules “The object is 3 inches in diameter. Is the object a pizza or a quarter?” Most people say “pizza.” CATEGORIZATION “Take an object that is 3 inches in diameter: is it more similar to a pizza, or to a Quarter? Most people say “quarter.” SIMILARITY
Low similarity, high diagnosticity The person jumped in the pool with their clothing on. Is the person drunk?
So how do we deal with all these problems for similarity models?
Allen & Brooks Subjects trained to categorize diggers vs. builders. Some told to memorize exemplars learn the categories that way. Others told to categorize using a rule. At test they see creatures that follow the rule to define them as one creature, but they are more similar to the other creature. Rule = at least two of (long legs, angular body, spots)->builder
Results When confronted with builders who were similar to diggers, subjects’ categorization depended on the instructions they had received: Told to memorize: categorized based on similarity Told the rule: categorized based on the rule. Next question: when do people use rules, and when similarity?
Bottom line: We don’t know the answer to that yet. It’s been suggested that they use rules when there are features that are not free to vary; if the exemplar has the feature the exemplar is either definitely in or definitely out of the category. People use similarity in some situations and rules in others. The big question is when they use which.