Visual Search Processes and the Multivariate Point Symbol
Bibliographic Data
| ID | 14701949 |
|---|---|
| Authors | Elisabeth S Nelson (0009-0009-2290-1604, San Diego State University, corresponding author), Elisabeth Nelson (San Diego State University), David Dow (San Diego State University), David R Dow (San Diego State University), Chris Lukinbeal (0000-0003-1827-7764, San Diego State University), Christopher Lukinbeal (San Diego State University), Ray Farley (San Diego State University) |
| Year | 1997 |
| Volume | 34 |
| Issue | 4 |
| Pages | 19-33 |
| Publication date | 1997-01-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Cartographica The International Journal for Geographic Information and Geovisualization (JOURNAL) |
| Journal identifiers | ISSN: 0317-7173 • E-ISSN: 1911-9925 |
| Publisher | University of Toronto Press Inc. (UTPress) (PUBLISHER) |
| DOI | 10.3138/15t3-3222-x25h-35ju |
| OpenAlex | W1991760336 |
| Language | EN |
| Citations received | 5 |
| References cited | 34 |
This study reviews the major theories of visual search processes and applies some of their concepts to searching for multivariate point symbols in a map environment. The act of searching a map for information is a primary activity undertaken during map-reading. The complexity of this process will vary, of course, with symbol design and map content. Multivariate symbols, for example, will be more difficult to search for efficiently than univariate symbols. The purpose of this research was to examine the cognitive processes used by map readers when searching for multivariate point symbols on a map. The experiment used Chernoff Faces as the test symbol, and a symbol-detection task to assess how accurately and how efficiently target symbols composed of different combinations of facial features could be detected. Of particular interest was assessing the role that different combinations of symbol dimensions and different combinations of symbol parts played in moderating search efficiency. Subject reaction times and error rates were used to evaluate the efficiency of the searches. Results suggested all searches employed serial search processes, although feature searches (those in which a target symbol consists of a unique feature) were by far the easiest for subjects to complete. It was also demonstrated that hierarchical relationships could be manipulated within symbols to increase search efficiency for searches in which the target does not have a unique features (conjunctive search)
Feature (linguistics · Linguistics · Machine learning · Multivariate statistics · Pattern recognition (psychology · Point (geometry · Symbol (formal · Univariate · Visual Search · Categorization, perception, and language · Computer Science · Geographic Information Systems Studies · Mathematics · Spatial Cognition and Navigation · Artificial Intelligence
Exploratory data analysis
Visual search and stimulus similarity.
Feature analysis in early vision
Features and Objects
The Future of Perceptual Cartography
Colour Detection on Bivariate Choropleth Maps
The Ellipse/a Useful Cartographic Symbol
A Continuous Shading Scheme for Two-Variable Mapping
Visual Search Processes Used in Map Reading
Complementary-Color, Two-Variable Maps
Spectrally Encoded Two-Variable Maps∗
A feature-integration theory of attention
Statistical Graphics
| Unique citing works | 5 |
|---|---|
| Citations per year | 0,19 |
| Citation span | 2000 - 2024 (25) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 5 |