Building and Verifying a Predictive Model of Interruption Resumption

Abstract

We built and evaluated a predictive model for resuming after an interruption. Two different experiments were run. The first experiment showed that people used a transactive memory process, relying on another person to keep track of where they were after being interrupted while retelling a story. A memory for goals model was built using the ACT-R/E cognitive architecture that matched the cognitive and behavioral aspects of the experiment. In a second experiment, the memory for goals model was put on an embodied robot that listened to a story being told. When the human storyteller attempted to resume the story after an interruption, the robot used the memory for goals model to determine if the person had forgotten the last thing that was said. If the model predicted that the person was having trouble remembering the last thing said, the robot offered a suggestion on where to resume. Signal detection analyses showed that the model accurately predicted when the person needed help.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2012
Accession Number
ADA603850

Entities

People

  • Allison Jacobs
  • Anthony M. Harrison
  • J. Gregory Trafton

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Autonomous Systems
  • Cameras
  • Cognition
  • Cognitive Science
  • Computers
  • Detection
  • False Alarms
  • Human Behavior
  • Human-Robot Interaction
  • Instructions
  • Military Research
  • New York
  • Predictive Modeling
  • Psychology
  • Robotics
  • Robots
  • Signal Detection

Fields of Study

  • Psychology

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computational Modeling and Simulation
  • Military History of the United States in the 20th Century.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Autonomy