The Representational Code of the Internal Model of Dynamic Systems: An Individual Differences and Dual Task Approach

Abstract

When a human operator monitors and controls complex dynamic processes, it is assumed that an internal representation of the process directs the operator's actions. This internal model is presumed to lie at some point along a verbal-spatial continuum. In order to determine the point on this continuum, nine subjects with high verbal and low spatial abilities, and nine with low verbal and high spatial abilities performed a multi-element failure detection task, either by itself, or concurrently with either a verbal or spatial secondary memory task. Patterns of interference between the maintaining and updating of the internal model and the performing of the memory tasks were used to infer the mode of internal model employed by the subjects. Interference results confirm that the failure detection task is spatial, and, as expected, verbal subjects performed better on the verbal secondary task and spatial subjects performed better on the spatial one. Both ability groups demonstrated similar failure detection abilities, and generated similar patterns of dual task interference. These results indicated that all subjects adopted the same strategy for failure detection. Keywords: Internal model, Mental model, Process control, Abilities, Performance(Human).

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1987
Accession Number
ADA190876

Entities

People

  • Annette Weingartner
  • Christopher Dow Wickens

Organizations

  • University of Illinois Urbana–Champaign

Tags

Communities of Interest

  • C4I
  • Human Systems

DTIC Thesaurus Topics

  • Cognitive Workload
  • Damage Detection
  • Detection
  • False Alarms
  • Human Factors Engineering
  • Human-Machine Systems
  • Information Processing
  • Instructions
  • Mental Processes
  • New York
  • Nuclear Power Plants
  • Psychology
  • Reaction Time
  • Reactor Cores
  • Social Sciences
  • Students
  • Task Performance And Analysis

Fields of Study

  • Biology

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Computational Modeling and Simulation

Technology Areas

  • AI & ML