Category Explorer Examples



This page includes examples of some categorization tasks you might want to try. For each example, you can download the images for the experiment. When you click on an experiment, you will download a compressed file that includes all of the images. Uncompress the file by double-clicking on it. The images are organized into folders. Each folder is one of the categories in the experiment, and the labels for the categories should be the same as the folder label.

Categorizing Drawings and Photographs

Do people categorize photographs or drawings better? On the one hand, photographs more closely resemble actual objects in the real world, and so people presumably have more experience with things that look like photographs. On the other hand, drawings can strategically emphasize important characteristics of the objects that belong to a category and that distinguish it from other categories. Whether it is better to learn a category from realistic vs idealized examples is an important question with general implications for learning and education. In this example, you will learn to categorize toads vs frogs, using both drawings and photographs. The folders/categories for this experiment are:
Drawing of a Frog
Photo of a Frog
Drawing of a Toad
Photo of a Toad
Create 4 categories for "frog drawing", "frog photo", "toad drawing", "toad photo". Open up each of the folders and drag the 10 pictures inside the folder into the appropriate category box. You will be looking to see whether photographs are generally better categorized, in terms of both accuracy and speed, than drawings. Another interesting question would be whether categorizing drawings better prepares people for subsequently categorizing photographs, compared to a photographs-then-drawings order of learning. To explore that question, you would need two different category learning phases, and two different groups of participants.

Categorizing Border, Prototype, and Caricature Faces

When categorizing objects such as faces into two categories, how is the difficulty of categorizing objects affected by how far they are from the boundary between the categories? In looking at this question, it is useful to distinguish three kinds of objects: A set of 6 faces is constructed by blending between two faces in different amounts
Caricature of A
Prototype of A
Border of A
Border of B
Prototype of B
Caricature of B
Create 3 categories for prototype, caricature, and border items. Put the A and B faces from each of these 3 categories into their appropriate category, being sure to specify the "A" key as the right response for the A faces and "B" for the B faces. Most theories would predict that the border items will be hard to categorize because they are so close to both categories. Theories, however, differ on whether the prototypes or caricatures will be more easily categorized. Prototypes have the advantage that they are the most similar face, on average, to all of the faces within their category. Caricatures have the advantage of emphasizing features of the faces that distinguish one category from the other. Which of these accounts fits your results?

Basic and Superodinate Level Categorization

The cognitive psychologist Eleanor Rosch noticed that we make categorizations at multiple levels. The same thing could be categorized as a living thing, an animal, a mammal, a dog, a golden retriever, or the family's golden retriever, Mr. G. She argued that categories like "dog", "apple", "chair", and "hammer" are basic-level categories, and tend to be learned early in chilhood, have short words associated with them, and be the highest level in which objects belonging to the category have the same overall shape. In contrast, categories like "mammal", "fruit", and "furniture" are superordinate categories -- higher, more encompassing, and diverse categories. Consider a categorization requiring one basic level category (e.g. dog) to be distinguished from another basic level category (e.g. deer) in which both categories belong to the same superordinate level category (e.g. mammals). Now consider a categorization requiring one superordinate category (e.g. mammals) to be distinguished from another superordinate category (e.g. fruits). Which do you think is easier? On the one hand, the first might be easier if one initially thinks about basic level categories and only later thinks about superodinate categories. On the other hand, very broad, obvious features might allow one to distinguish mammals from fruits and so maybe this categorization is easier. There are six categories of objects that can be used: In a basic-level categorization task, the 10 deer pictures could be categorized by pressing the "A" key while the 10 dog pictures are categorized by pressing the "B" key. Another basic-level categorization task could use the apple and banana pictures. In a superordinate level categorization task, the 10 fruit pictures could be categorized by pressing the "A" key while the 10 mammal pictures are categorized by pressing the "B" key. You will need to compare response times across the basic-level and superordinate-level tasks.

Sex and Majors

Do people have expectations about men and women that interfere with categorizing words? People may have an expectation that connects women with humanities majors like English and Art History, while they connect men with science majors like Physics and Chemistry. If so, then the sex of a face in the background may influence how people categorize majors even though the faces are irrelevant for the word categorization task. The folders/categories for this experiment are: One hypothesis would be that people are better (faster and more accurate) at categorizing the "man science" and the "woman humanities" stimuli than the "man humanities" and "woman science" stimuli. What different theories could explain that result? Some ideas you might want to consider are: built up associations over time, stereotypes, statistical patterns in the world, and cultural expectations.

Implicit Attitudes Test

Researchers have argued that people have implicit attitudes based on race, and sometimes are not even aware of these attitudes. However, these attitudes may be revealed in categorization performance. For example, people may have an implicit attitude favoring their own race compared to others. If so, then they may find it easy to place positive words like "win" and "healthy" into the same category as faces from their own race, and place negative words like "lose" and "sick" into the same category as faces from a different race. There are two tasks a participant would be asked to do: For a white participant with an implicit attitude favoring whites over blacks, Task 1 is predicted to be easier than Task 2. Task 1 would be placing items that have the same positive/negative valence into the same category, whereas Task 2 puts items that that have different valences together in the same category and so might be less intuitive.

The folders/categories for this experiment are: For half of the participants, assign Task 1 before Task 2, and reverse this sequence for the other half. Overall, which Task is easier? Do your results depend on the race of the participant? Is it easier for your participants to categorize (by race) white or black faces? Depending on your results, some ideas that may be useful in explaining the results are: prejudice, in-group and out-group perception, automatic associations, and media portrayal of races.

Race and Positive/Negative words

Using very similar stimuli, another way to see if people have different implicit attitudes toward white and black faces is to combine a face and a word on a single stimulus. Each stimulus has a face (white or black) and also a word (positive or negative) on it. The task for a participant would be to decide whether a presented word is good or bad, ignoring the face in the background. The folders/categories for this experiment are: Given these stimuli, one might expect a cross-over interaction such that a white person would find the "white positive" and "black negative" categorizaions easier than the "white negative" and "black positive" categorizations. These stimuli could also be used to determine if a white vs black race categorization task is influenced by words (positive or negative).

Number and Position

Does experience with number lines in school make people automatically think of large numbers as being to the right of small numbers? If so, then if people are asked to determine if a number is small (less than 6) then they may be faster if the number is on the left side of the screen rather than the right. Conversely, people may be faster to respond that a number is large (more than 5) if the number is on the right side of the screen rather than the left. The folders/categories for this experiment are: These same folders and stimuli can be used to test whether the magnitude of a number influences spatial judgments. That is, are people faster at categorizing a number as appearing on LEFT side when the number is small rather than large, and faster at categorizing a number as appearing on the RIGHT side when the number is large rather than small. To run this experiment, you would simply stipulate that the first two categories ("left small" and "left large") get a "L" response (for Large) while the second two categories get a "R" response (for Right).

Brightness and Size

Objects are organized in terms of their dimensions - the different aspects along which they vary. Do people have a natural tendency to think of a dimesion of an object in terms of its magnitude: whether it is more or less? If so then different dimesions of an object may be related to each other by virtue of their magnitude. Examples of big dimesions might be large, loud, dark, high, thick, and fast things. Examples of small dimesions might be small, quiet, bright, low, thin, and slow objects. If different dimesions are united/blended in people's minds by their magnitude, then large may be congruent with dark (both have large magnitudes) and small may be congruent with bright (both have small magnitudes), and so categorizing the big dark square and the small light square should be relatively fast. Large and bright would be incongruent with each other, as would be small and dark, and so the big light square and the small dark square should be relatively slow. Only 4 images are needed to test this: Alternatively, you could make the categorization based on size rather than brightness, with the two big objects receiving a "L" response for "Large" and the two small objects receiving a "S" response for small. These experiments can reveal whether brightness automatically intrudes on size categorizations, and whether size automatically intrudes on brightness categorizations. Why might people automatically think about different dimensions of things in terms of their magnitudes? What are other examples of dimension values that people may automatically think of as being large?