AN INVESTIGATION OF SPEED AND ACCURACY OF DIRECT MANUAL READOUT OF A CODED DATA BLOCK.

Abstract

Objectives of this study were (1) to obtain performance measures of speed and accuracy of direct manual readout using the MIL-STD 782(B) format and (2) to obtain estimates of the scanning rate required to locate a set of geographic coordinates in a roll of data blocks. A special purpose teaching machine was designed and built to teach subjects to read excess-three binary-coded decimals using the pattern-recognition method. Human readout rates and errors were measured in the performance of three tasks that were considered typical of those performed by an interpreter when manually reading a data block. These tasks were (1) readout of all information in one data block, (2) exact coordinate data block search, and (3) interpolated coordinate data block search. The following results were obtained: (1) The mean readout time for the entire data block was 72 seconds. Errors in this task were less than 1 percent. (2) The measured average time per frame scanned for locating an exact set of geographic coordinates was 3.8 seconds and the extrapolated time per frame was 1.7 seconds. There were no errors in performing this task. (3) The measured average time per frame scanned for locating an interpolated set of coordinates was 5.1 seconds and the extrapolated time per frame was 1.4 seconds. Errors in performing this task were 5.6 percent. 17

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1967
Accession Number
AD0807446

Entities

People

  • Anthony Santanelli

Organizations

  • United States Army Communications-Electronics Command

Tags

DTIC Thesaurus Topics

  • Accuracy
  • Education
  • Errors
  • Pattern Recognition
  • Recognition
  • Scanning
  • Teaching Machines

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Computer Programming and Software Development.
  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference