Showing papers by "James L. McClelland published in 1985"

Distributed memory and the representation of general and specific information.

Abstract: We describe a distributed model of information processing and memory and apply it to the representation of general and specific information. The model consists of a large number of simple processing elements which send excitatory and inhibitory signals to each other via modifiable connections. Information processing is thought of as the process whereby patterns of activation are formed over the units in the model through their excitatory and inhibitory interactions. The memory trace of a processing event is the change or increment to the strengths of the interconnections that results from the processing event. The traces of separate events are superimposed on each other in the values of the connection strengths that result from the entire set of traces stored in the memory. The model is applied to a number of findings related to the question of whether we store abstract representations or an enumeration of specific experiences in memory. The model simulates the results of a number of important experiments which have been taken as evidence for the enumeration of specific experiences. At the same time, it shows how the functional equivalent of abstract representations--prototypes, logogens, and even rules--can emerge from the superposition of traces of specific experiences, when the conditions are right for this to happen. In essence, the model captures the structure present in a set of input patterns; thus, it behaves as though it had learned prototypes or rules, to the extent that the structure of the environment it has learned about can be captured by describing it in terms of these abstractions.

Levels indeed! A response to Broadbent

Abstract: Although Broadbent concedes that we are probably correct in supposing that memory representations are distributed, he argues that psychological evidence is irrelevant to our argument because our point is relevant only at what Marr (1982) has called the implementation^ level of description and that psychological theory is only properly concerned with what Marr calls the computational level. We believe that Broadbent is wrong on both counts. First, our model is stated at a third level between the other two, Marr's representational and algorithmic level. Second, we believe that psychology is properly concerned with all three of these levels and that the information processing approach to psychology has been primarily concerned with the same level that we are, namely, the algorithmic level. Thus, our model is a competitor of the logogen model and other models of human information processing. We discuss these and other aspects of the question of levels, concluding that distributed models may ultimately provide more compelling accounts of a number of aspects of cognitive processes than other, competing algorithmic accounts.

Distributed models of cognitive processes. Applications to learning and memory.

Abstract: computational level. They differ from computer programs, though, in three fundamental ways. ( I ) Processing is thought to emerge from the interactions of a large number of simple (abstract) computational elements called nodes, rather than through the activity of a single, central processing unit. (2) The knowledge that underlies processing in distributed models is thought to be stored in the strengths of the connections between the nodes, rather than in data structures or compiled computer code interpreted by the central processing unit. (3) Learning in distributed models amounts to changes in the strengths of connections between the nodes, rather than changing the contents of data structures or recompilation of the program. 'Supported by a grant from the Systems Development Foundation. The author is a recipient of a Research Scientist Career Development Award (MH 00385).