In this article we propose a theoretical framework of distributed representations and a methodology of representational analysis for the study of distributed cognitive tasks—tasks that require the processing of information distributed across the internal mind and the external environment. The basic principle of distributed representations Is that the representational system of a distributed cognitive task is a set of internal and external representations, which together represent the abstract structure of the task. The basic strategy of representational analysis is to decompose (...) the representation of a hierarchical task into its component levels so that the representational properties at each level can be independently examined. The theoretical framework and the methodology are used to analyze the hierarchical structure of the Tower of Hanoi problem. Based on this analysis, four experiments are designed to examine the representational properties of the Tower of Hanoi. Finally, the nature of external representations is discussed. (shrink)

Commentators expressed a wide variety of views on whether there is a basic capacity limit of 3 to 5 chunks and, among those who believe in it, about why it occurs. In this response, I conclude that the capacity limit is real and that the concept is strengthened by additional evidence offered by a number of commentators. I consider various arguments why the limit occurs and try to organize these arguments into a conceptual framework or “metatheory” of storage capacity limits (...) meant to be useful in future research to settle the issue. I suggest that principles of memory representation determine what parts of the representation will be most prominent but that limits of attention (or of a memory store that includes only items that have been most recently attended) determine the 3- to 5-chunk capacity limit. (shrink)

People frequently gesture when problem-solving, particularly on tasks that require spatial transformation. Gesture often facilitates task performance by interacting with internal mental representations, but how this process works is not well understood. We investigated this question by exploring the case of mental abacus, a technique in which users not only imagine moving beads on an abacus to compute sums, but also produce movements in gestures that accompany the calculations. Because the content of MA is transparent and readily manipulated, the task (...) offers a unique window onto how gestures interface with mental representations. We find that the size and number of MA gestures reflect the length and difficulty of math problems. Also, by selectively interfering with aspects of gesture, we find that participants perform significantly worse on MA under motor interference, but that perceptual feedback is not critical for success on the task. We conclude that premotor processes involved in the planning of gestures are critical to mental representation in MA. (shrink)