From: pfoltz@boulder.Colorado.EDU (Peter Foltz)
Subject: Re: Graph Comprehension
Reply-To: pfoltz@boulder.Colorado.EDU (Peter Foltz)
Organization: University of Colorado, Boulder
Date: Fri, 31 Jul 1992 20:07:27 GMT

In article <PSYCGRAD%92073112485670@UOTTAWA.BITNET>, ST8330@SIUCVMB.BITNET (Fred Detwiler) writes:
|> Hi Folks,
|> 
|> Been gone a while--working like a "dawg."
|> 
|> Info.  request:  Does anyone have or know of theories about HOW
|> people process graphs (particularly, x-y relational graphs) in relation
|> to text (s) about them?
|> Fred.

There's been a fair bit of research in this area in the HCI literature.  
Here are a couple of citations that may be of use. By the way, these
abstracts come from using the hcibib retrieval system.  If you want to try it out, send a blank message to hcibib@rumpus.colorado.edu.  A help file will
be mailed back to you telling you how to access and use the system.

%T A Cognitive Model for the Perception and Understanding of Graphs
%S Understanding Graphical Interfaces
%A Jerry Lohse
%B Proceedings of ACM CHI'91 Conference on Human Factors in Computing
Systems
%D 1991
%P 137-144
%K User-interface design issues, Graphic presentations, Screen layout,
Models of the user, GOMS
%* (c) Copyright 1991 Association for Computing Machinery
%X Despite the increasing importance of graphics in the design of
information systems, we have only a partial understanding of how people
perceive and process graphic information.  This paper describes a computer
program, UCIE, that simulates graphical perception.  The goal of the
program is to model the underlying perceptual and cognitive processes
people use to decode information from a graph.  The model predicts reaction
time from assumptions about the logical sequence of eye fixations, STM
capacity and duration limitations, and the relative level of difficulty to
acquire information in each glance.  The model summarizes a large body of
empirical results and can be the core of an expert advisor for the
construction of graphs.

%T Models of User Interactions with Graphical Interfaces: I. Statistical Graphs
%S Interfaces to Mathematical Systems
%A Douglas J. Gillan
%A Robert Lewis
%A Marianne Rudisill
%B Proceedings of ACM CHI'89 Conference on Human Factors in Computing
Systems
%D 1989
%P 375-380
%K Statistical graphs, User models, Cognitive models, Performance models
%* (c) Copyright 1989 Association for Computing Machinery
%X Three models of human interactions with computer-displayed statistical
graphics were developed and tested in an experiment which examined users'
speed and accuracy on identification and comparison questions using 17
graph types.  The results indicated that response time and accuracy were
influenced by the perceptual and informational complexity of the graph, as
well as the relation between the figure and axes, (Model 1); by the
physical elements of the graph -- points, lines, and areas (Model 2); and
by the data-ink ratio and data density (Model 3).  The discussion focuses
on the development of a single integrated model of interactions with
graphics.

%T Density in Scatterplots and the Estimation of Correlation
%A Thomas W. Lauer
%A Gerald V. Post
%J Behaviour and Information Technology
%D 1989
%V 8
%N 3
%P 235-244
%* (c) Copyright 1989 Taylor and Francis Ltd.
%X The construction of a graphical presentation involves the representation
of information by means of visual symbols.  The acquisition of information
from the resultant graph is a perceptual process that involves the decoding
and interpretation of the visual symbols.  Hence good design decisions will
be based on an understanding of the information acquisition process and in
particular graphical perception.  This study examines the perception of
bivariate normal data presented in a scatter diagram, and creates a model
that successfully explains how individuals perceive the information
contained in scatterplots.
   Subjects were shown a series of scatter diagrams on the CRT of a
microcomputer and were asked to estimate correlation.  Several variables
were examined that explain estimated correlation including regression
slope, dispersion, number of points displayed, and the size of the CRT
screen.  All of these factors were found to significantly affect subjects'
estimates of correlation.
-- 
-------------------------------------------------------------------
Peter W. Foltz			Box 345, Dept of Psychology
pfoltz@rumpus.colorado.edu	Univ of Colorado, Boulder, Co 80309
"All receipt of information is necessarily receipt of the news of difference."
--Gregory Bateson
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU