Human Resource Scheduling in Performing a Sequence of Discrete Responses

Abstract

Behavior in military domains typically requires a sequence of decisions and actions. Yet, characteristics and limitation of cognitive processing are typically based on discrete-trial laboratory studies. The broad objective of this work was to bridge the gap between basic science and applications by: (1) exploring the suitability of central bottleneck models as the basis for computational models of sequence behavior, (2) identifying emergent properties in scheduling behavioral sequences; and (3) determining if and how sequence execution is optimized. Studies examined eye movements and manual responses to sequences of speeded choice response time tasks arrayed linearly on a visual display. A consistent emergent property was discovered in the deferral of the first response, which was shown to be a strategy only loosely linked to resource constraints. Given this strategy central bottleneck theory provided an accurate account of sequence execution. Deferring the first response may represent an optimal response to stochastic fluctuations in the duration of internal processes. The distribution of eye fixation was well fit by reinforcement learning models, evidence for optimality with respect to target probability.

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Document Details

Document Type
Technical Report
Publication Date
Feb 28, 2009
Accession Number
ADA502763

Entities

People

  • Harold Pashler
  • Roger W. Remington
  • Shu-chieh Wu

Organizations

  • University of Queensland

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Analysis Of Variance
  • Biological Sciences
  • Cognition
  • Cognitive Science
  • Cognitive Systems Engineering
  • Cognitive Workload
  • Computer Programming
  • Data Science
  • Dwell Time
  • Eye
  • Eye Movements
  • Human Resources
  • Information Processing
  • Information Science
  • Psychology
  • Reinforcement Learning
  • Scheduling (Production)

Fields of Study

  • Biology
  • Psychology

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Operations Research
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms