Meaning is a critical variable in representation.
Sachs example and data, Wanner, Gernsbacher, Anderson, Bransford, Example of meaningful differences remembered better. What counts as meaningful?
Meaningful content of pictures. Subjects don't notice if left-right is reversed after a time. 
Mandler and Richie, Subjects don't notice dress pattern. But--Channing Johnson. What is remembered is a function of task during learning. The task can determine to some extent what is a meaningful difference.
Droodles: Giving meaning to silly cartoons enhances memory for them

Propositions: The concept of proposition is basic to many of the information processing systems. It is widely used in analyses of representations in psychology, logic, linguistics, and computer science (Artificial Intelligence) Propositions are representations that keep semantic meaning constant over a variety of linguistic forms.  Their structure and their value. Question to consider!  Do people encode  and represent information in a propositional format?
Propositions can be ordered into hierarchies and networks: Structured combinations of propositions.
Collins and Quillian hierarchical representation of a series of interlinking propositions.
Conrad follow-up leads to a question of efficiency of storage and efficiency of retrieval.

Propositional representations can be used to explain much of what we have discussed.

There still are problems in propositional analyses
Bransford and Franks.--Subjects recalled more integrated set of propositions better than more isolated ones. "The ants in the kitchen ate the sweet jelly which was on the table."
Lawson inferences and assertions--Propositions that were not asserted, but are inferable from others, are at times recalled faster than some asserted ones. The more distant the two objects, the faster the decision time, regardless of whether it was asserted or not. Also, subjects often do not know which ones they've heard.

A is higher than B
B is higher than C
C is higher than D
D is higher than E
True or False:
B is higher than C
B is higher than D

These meaning representations may have similar forms in many cases as the analogical ones described in Chapter 4.

Schemas and structured information.   Scripts, frames. Most of what we learn about something from language is not actually said, but inferred from previous knowledge of schemas. This is the primary reason new courses in a topic are hard.
Default values--Birds fly
    non-monotonic reasoning--penguins are birds and they do not fly. It is better to identify a schema and point out the exceptions than to try to state everything from scratch. Social stereotypes is one negative if we do not realize that one cannot assume that default values are true in a complex system.

Categorization: If you have a category, how is it represented in mind, and how do you identify something as being a member of the category? Square or circle is easy, but--What objects are  furniture (chairs, beds, rugs, table lamps, pillows) What is a cup? (ratio of height to width, is size relevant? Is what is in it relevant?) What is a fish? (trout, salmon, sea horse, shark, whale) What are the properties of a bird? Republican? mother?

•     Classical view (Aristotle) and alternatives
•     Wittgenstein and family resemblances
•     Prototype theory--Zadeh, Elinor Rosch, J.D. Smith--Category members are defined by how similar they are to the best or most central member of the category which is set up as the prototype.
•     Instance theory--Nosofsky, Medin and Schaffer--Membership in a category is determined by how close an example is to the set of instances of category members, compared to other instances that are not members of the category.
•     Weights on feature lists (neural networks, e.g., Gluck and Bower (1988): identify members of a category by weighting the features which are relevant to discriminating it from nonmembers of the category. since members of a category cannot be identified

Return to Syllabus