Organization of Memory

Coding and Organization

Coding refers to the interpretations a person gives to experiences. The significance of experience for memory and action depends on the interpretation of the experience. The same events can be interpreted in dramatically different ways depending on a person's knowledge and expectations. To understand coding we must understand the organization and use of knowledge in interpreting experience. The interrelatedness of ideas is one of the most compelling facts of mental life. In personal memories, a single association with some present event can trigger detailed memories of past experiences. Psychology has developed several ideas about the nature of organization in memory.

We can illustrate the influence of coding by comparing the memories of two people with different degrees of knowledge: in this case, an expert and a non-expert about cars. They both see the same small red car. The expert identifies it as a Miata; the nonexpert can identify it only as a small red car. Would it surprise you if later the expert was able to state with some confidence that a small red Triumph was not the car seen earlier, while the nonexpert had more difficulty in making this discrimination? Each individual's knowledge influences the coding and thus the memory of the experience.

Human memory imposes organization on our experiences. Tulving (1962) and others have shown that when people learn a list of randomly selected words, they organize the words in recalling the list. As the list is learned, there is more and more consistency in the grouping of the words in recall.

Earlier, Bousfield (1953) showed that subjects recall lists of words as clusters of related words. For example, if the list contained some names of flowers, some names of people, some types of buildings, and so on, then the free recall of these words would group the similar items. This grouping occurs even though the words are presented in random order. Later Bower and his colleagues (Bower, 1970) showed that theories about the structure of memory could predict the organization of material to be learned. Bransford and Johnson (1972) studied passages that are difficult to remember unless people are led to give them appropriate interpretations. Their work is an impressive demonstration of the role of interpretation in remembering.

Organization of Memory

What leads to the organization of memories? Most answers to this question refer to association as at least one fundamental process of organization. Associations derive from the frequent temporal clustering of events. In the early part of the twentieth century, Pavlov (1927) discovered classical conditioning. This discovery led to extensive investigations of the formation and maintenance of associations. Pavlov found that after frequently presenting a neutral stimulus (e.g., a tone) in close proximity to the presentation of food, a dog would salivate at the sound of the tone even in the absence of food. Thus, an association formed between the tone and the food.

Garcia and Koelling (1966) found that some associations are learned more easily than others. Their laboratory rats learned to associate a novel taste with gastrointestinal illness much more easily than they learned the association between a flashing light and gastrointestinal illness. This result suggests that various constraints influence the formation of associations.

Associative Networks

In the direct representation of associations in the form of a network, concepts are shown as nodes and associations are shown by lines (or links) connecting the nodes. Schvaneveldt, Durso, and Dearholt (1989) presented a method of deriving such networks from proximity data such as judgments of relatedness among sets of concepts. Cooke, Durso, and Schvaneveldt (1986) found that networks can predict the way people organize the concepts when they learn a list of words. Goldsmith and Johnson (1990) were able to predict students' grades in a course on experimental methods from the degree of similarity of the students' and the instructor's networks of important concepts.

Semantic Networks and Semantic Features

Semantic networks also use network representations, but they specify more about the relations between concepts by using labeled links (Collins and Quillian, 1969; Meyer and Schvaneveldt, 1976; Quillian, 1969). For example, such a network would show that robin is a member of the class bird with an "isa" link (A robin is a bird). It would also show that a deer has antlers, and so on. Such networks can also support inferences such as concluding that a robin is an animal by retrieving a robin is a bird and a bird is an animal. Semantic networks have been used to explain experimental data from studies in language understanding and category judgments. Such networks are also often a part of computer programs designed to exhibit artificial intelligence (Quillian, 1989). Other theories propose that concepts consist of collections of features that define the concepts (Smith and Medin, 1981). The concept bird, for example, might consist of features such as has wings, flies, lays eggs, has feathers, and so on. According to feature theories, when people reason about concepts, they retrieve features from memory and use them to draw conclusions.

Schemata

Schemata are general representations of several different items of information together with the specification of the relations among the items (Bartlett, 1932; Minsky, 1975). For example, the schema for a room might specify that it must have a floor, a ceiling, walls, and a door as well as some spatial relations among these. Optionally, it might have additional doors and windows. Scripts are examples of schemata where actions are organized in familiar sequences such as going to a restaurant or visiting the doctor. Schemata invite inferences. Several studies suggest that memory includes inferred information (defaults) in addition to what we actually experience. For example, if we hear the sentence, "Fred drove the nail into the board," we are likely to infer that he used a hammer even though the sentence does not mention a hammer. If someone eats in a restaurant, we assume that he or she paid for the meal.

Chase and Simon (1973) reported a classic demonstration of the power of schemata using memory for the positions of pieces on a chess board. They found that chess masters were no better than novices at reconstructing a board with randomly placed pieces, but the masters were far superior in recalling the positions of pieces from the middle of an actual chess game. Experts presumably have elaborate schemata that can code the positions of the pieces on the board when the positions make sense.

Embodiment and the Need for Representations

In recent years challenges to traditional ideas about the role of mental representations have arisen from researchers in cognitive science. A major concern is that traditional approaches have neglected the constraints imposed on learning and development that stem from the physical body and from the environment. At the extreme, theorists advocating a dynamic systems approach claim that grounding cognition in the interaction of the body and the world obviates the need to propose mental representations that mediate perception and action (Edelman, 1992; Freeman, 1995; Johnson, 1987; Thelen and Smith, 1994; van Gelder, 1997). The grounding of concepts in perception and action helps explain how concepts are learned (Bickard, 2000). Consequently, coding is constrained by the history and situation of the individual.

Conclusion

Coding is the interpretation of events in light of what we know. Such interpretation can have beneficial consequences, as in the superiority of the memory of chess masters for real board positions. Sometimes interpretation leads to false memories of related information that was not actually experienced (Loftus and Ketcham, 1991). Understanding the memory of an event requires an understanding of the coding that arises from cumulative knowledge. An important question for theory and research concerns the extent to which memory depends on stored representations as opposed to cues available from the body and the environment.

See also:CODING PROCESSES: IMAGERY; CODING PROCESSES: LEVELS OF PROCESSING; FALSE MEMORIES

Bibliography

Bartlett, F. C. (1932). Remembering: A study in experimental and social psychology. Cambridge, UK: Cambridge University Press.

Bickard, M. H. (2000). Dynamic representing and representational dynamics. In E. Dietrich and A. Markman, eds., Cognitive dynamics: Conceptual and representational change in humans and machines. Mahwah, NJ: Erlbaum.

Bousfield, W. A. (1953). The occurrence of clustering in the recall of randomly arranged associates. Journal of General Psychology 49, 229-240.

Bower, G. H. (1970). Organizational factors in memory. Cognitive Psychology 1, 18-46.

Bransford, J. D., and Johnson, M. K. (1972). Contextual prerequisites for understanding: Some investigations of comprehension and recall. Journal of Verbal Learning and Verbal Behavior 11, 717-726.

Chase, W. G., and Simon, H. A. (1973). Perception in chess. Cognitive Psychology 4, 55-81.

Collins, A. M., and Quillian, M. R. (1969). Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behavior 8, 240-247.

Cooke, N. M., Durso, F. T., and Schvaneveldt, R. W. (1986). Recall and measures of memory organization. Journal of Experimental Psychology: Learning, Memory, and Cognition 12, 538-549.

Edelman, G. M. (1992). Bright air, brilliant fire: On the matter of mind. New York: Basic Books.

Freeman, W. J. (1995). Societies of brains. Hillsdale, NJ: Erlbaum.

Garcia, J., and Koelling, R. A. (1966). Relation of cue to consequence in avoidance learning. Psychonomic Science 4, 123-124.

Goldsmith, T. E., and Johnson, P. J. (1990). A structural assessment of classroom learning. In R. Schvaneveldt, ed., Pathfinder associative networks: Studies in knowledge organization. Norwood, NJ: Ablex.

Johnson, M. (1987). The body in the mind: The bodily basis of meaning, imagination, and reason. Chicago: University of Chicago Press.

Loftus, E. F., and Ketcham, K. (1991). Witness for the defense. New York: St. Martin's.

Meyer, D. E., and Schvaneveldt, R. W. (1976). Meaning, memory structure, and mental processes. Science 192, 27-33.

Minsky, M. (1975). A framework for representing knowledge. In P. Winston, ed., The psychology of computer vision. New York: McGraw-Hill.

Pavlov, I. P. (1927). Conditioned reflexes, trans. G. V. Anrep. London: Oxford University Press.

Quillian, M. R. (1969). The teachable language comprehender. Communications of the ACM 12, 459-476.

Schvaneveldt, R. W., Durso, F. T., and Dearholt, D. W. (1989). Network structures in proximity data. In G. H. Bower, ed., The psychology of learning and motivation: Advances in research and theory, Vol. 24. New York: Academic Press.

Smith, E. E., and Medin, D. L. (1981). Categories and concepts. Cambridge, MA: Harvard University Press.

Thelen, E., and Smith, L. B. (1994). A dynamic systems approach to the development of cognition and action. Cambridge, MA: MIT Press.

Tulving, E. (1962). Subjective organization in free recall of "unrelated" words. Psychological Review 69, 344-354.

van Gelder, T. (1997). Dynamics and cognition. In J. Haugland, ed., Mind design II. Cambridge, MA: MIT Press.

Roger W.Schvaneveldt