%0 Journal Article %1 larkin1987 %A Larkin, J. H. %A Simon, H. A. %D 1987 %J Cognitive Science %K TOREAD cognition %N 1 %P 65--100 %T Why a Diagram is (Sometimes) Worth Ten Thousand Words %U http://www.sciencedirect.com/science/article/B6W48-4FW6JX3-4/2/9f39ec088401118e1fff1f847412dbe0 %V 11 %X We distinguish diagrammatic from sentential paper-and-pencil representations of information by developing alternative models of information-processing systems that are informationally equivalent and that can be characterized as sentential or diagrammatic. Sentential representations are sequential, like the propositions in a text. Diagrammatic representations are indexed by location in a plane. Diagrammatic representations also typically display information that is only implicit in sentential representations and that therefore has to be computed, sometimes at great cost, to make it explicit for use. We then contrast the computational efficiency of these representations for solving several illustrative problems in mathematics and physics. When two representations are informationally equivalent, their computational efficiency depends on the information-processing operators that act on them. Two sets of operators may differ in their capabilities for recognizing patterns, in the inferences they can carry out directly, and in their control strategies (in particular, the control of search). Diagrammatic and sentential representations support operators that differ in all of these respects. Operators working on one representation may recognize features readily or make inferences directly that are difficult to realize in the other representation. Most important, however, are differences in the efficiency of search for information and in the explicitness of information. In the representations we call diagrammatic, information is organized by location, and often much of the information needed to make an inference is present and explicit at a single location. In addition, cues to the next logical step in the problem may be present at an adjacent location. Therefore problem solving can proceed through a smooth traversal of the diagram, and may require very little search or computation of elements that had been implicit.

@article{larkin1987, abstract = {We distinguish diagrammatic from sentential paper-and-pencil representations of information by developing alternative models of information-processing systems that are informationally equivalent and that can be characterized as sentential or diagrammatic. Sentential representations are sequential, like the propositions in a text. Diagrammatic representations are indexed by location in a plane. Diagrammatic representations also typically display information that is only implicit in sentential representations and that therefore has to be computed, sometimes at great cost, to make it explicit for use. We then contrast the computational efficiency of these representations for solving several illustrative problems in mathematics and physics. When two representations are informationally equivalent, their computational efficiency depends on the information-processing operators that act on them. Two sets of operators may differ in their capabilities for recognizing patterns, in the inferences they can carry out directly, and in their control strategies (in particular, the control of search). Diagrammatic and sentential representations support operators that differ in all of these respects. Operators working on one representation may recognize features readily or make inferences directly that are difficult to realize in the other representation. Most important, however, are differences in the efficiency of search for information and in the explicitness of information. In the representations we call diagrammatic, information is organized by location, and often much of the information needed to make an inference is present and explicit at a single location. In addition, cues to the next logical step in the problem may be present at an adjacent location. Therefore problem solving can proceed through a smooth traversal of the diagram, and may require very little search or computation of elements that had been implicit.}, added-at = {2006-07-20T22:45:45.000+0200}, author = {Larkin, J. H. and Simon, H. A.}, biburl = {https://www.bibsonomy.org/bibtex/2480863c84b0301304586cf3be06fbccb/mstrohm}, comment = {- seminal study that analyzes why diagrams might be better than 'sentential' representation like text: namely, humans are adapted to understand diagrams and concepts like 'in' 'beside' 'on top' - are there other means of representing information (music? feel?) - p. 67 presents concepts of information and computational equivalence - computational equivalence is very fuzzy - not clear what 'operators' are - syntax? - the paper tries to turn the comparison into one paradigm of problem solving (production system). - suggests there is a difference between memory and external repr. I disagree. Not clear where boundary is (problem/general) - Anderson (84) argues that distinctions between two representations is more about what operations they provide than syntax - no 2 diagrams are the same - proposes search/recognize/infer linear model of problem solving. Some of this may be challenged by e.g. rasmussen - search is a big difference btw representations e.g. recognizing where you are. Does i* handle this? Search may be harder in some diagrams with no clear starting point. - p70 notes training might be a factor - perhaps the problem statement biases towards a sentential repr. - e.g. RE elictiation or Myst - no idea what the problem is because there is no description of it textually - you must divine the nature - is there a difference when comparing relative performance of computer and human? Seems like computer (Von Neumann arch) is biased towards sentential. -}, interhash = {0cdf89e38b0bfc129df03d1f4d9999be}, intrahash = {480863c84b0301304586cf3be06fbccb}, journal = {Cognitive Science}, keywords = {TOREAD cognition}, number = 1, pages = {65--100}, timestamp = {2006-07-20T22:45:45.000+0200}, title = {Why a Diagram is (Sometimes) Worth Ten Thousand Words}, url = {http://www.sciencedirect.com/science/article/B6W48-4FW6JX3-4/2/9f39ec088401118e1fff1f847412dbe0}, volume = 11, year = 1987 }