Human-automated judgment learning: Applying interpersonal learning to investigate human interaction with alerting systems

Ellen J. Bass, Amy Pritchett

Research output: Contribution to conferencePaper

Abstract

The application of human-automated judgement learning (HAJL) in interpersonal learning for investigating human interaction with alerting systems was discussed. HAJL models the impact of the alerting systems on the human judge by accounting for the initial conflict between them, the compromise of the human judge, and the adaptation that the human judge makes with respect to the automated judge and the environment. It was found that the unit of investigation was the parallel judgements of the human and automated judges in the uncertain environment.

Original languageEnglish (US)
StatePublished - Jan 1 2002
EventAir Traffic Management for Commercial and Military Systems - Irvine, CA, United States
Duration: Oct 27 2002Oct 31 2002

Other

OtherAir Traffic Management for Commercial and Military Systems
CountryUnited States
CityIrvine, CA
Period10/27/0210/31/02

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Electrical and Electronic Engineering

Cite this

Bass, E. J., & Pritchett, A. (2002). Human-automated judgment learning: Applying interpersonal learning to investigate human interaction with alerting systems. Paper presented at Air Traffic Management for Commercial and Military Systems, Irvine, CA, United States.
Bass, Ellen J. ; Pritchett, Amy. / Human-automated judgment learning : Applying interpersonal learning to investigate human interaction with alerting systems. Paper presented at Air Traffic Management for Commercial and Military Systems, Irvine, CA, United States.
@conference{b5f2e4355ecb4090ab4c8626ff8fc3d1,
title = "Human-automated judgment learning: Applying interpersonal learning to investigate human interaction with alerting systems",
abstract = "The application of human-automated judgement learning (HAJL) in interpersonal learning for investigating human interaction with alerting systems was discussed. HAJL models the impact of the alerting systems on the human judge by accounting for the initial conflict between them, the compromise of the human judge, and the adaptation that the human judge makes with respect to the automated judge and the environment. It was found that the unit of investigation was the parallel judgements of the human and automated judges in the uncertain environment.",
author = "Bass, {Ellen J.} and Amy Pritchett",
year = "2002",
month = "1",
day = "1",
language = "English (US)",
note = "Air Traffic Management for Commercial and Military Systems ; Conference date: 27-10-2002 Through 31-10-2002",

}

Bass, EJ & Pritchett, A 2002, 'Human-automated judgment learning: Applying interpersonal learning to investigate human interaction with alerting systems', Paper presented at Air Traffic Management for Commercial and Military Systems, Irvine, CA, United States, 10/27/02 - 10/31/02.

Human-automated judgment learning : Applying interpersonal learning to investigate human interaction with alerting systems. / Bass, Ellen J.; Pritchett, Amy.

2002. Paper presented at Air Traffic Management for Commercial and Military Systems, Irvine, CA, United States.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Human-automated judgment learning

T2 - Applying interpersonal learning to investigate human interaction with alerting systems

AU - Bass, Ellen J.

AU - Pritchett, Amy

PY - 2002/1/1

Y1 - 2002/1/1

N2 - The application of human-automated judgement learning (HAJL) in interpersonal learning for investigating human interaction with alerting systems was discussed. HAJL models the impact of the alerting systems on the human judge by accounting for the initial conflict between them, the compromise of the human judge, and the adaptation that the human judge makes with respect to the automated judge and the environment. It was found that the unit of investigation was the parallel judgements of the human and automated judges in the uncertain environment.

AB - The application of human-automated judgement learning (HAJL) in interpersonal learning for investigating human interaction with alerting systems was discussed. HAJL models the impact of the alerting systems on the human judge by accounting for the initial conflict between them, the compromise of the human judge, and the adaptation that the human judge makes with respect to the automated judge and the environment. It was found that the unit of investigation was the parallel judgements of the human and automated judges in the uncertain environment.

UR - http://www.scopus.com/inward/record.url?scp=18244429682&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=18244429682&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:18244429682

ER -

Bass EJ, Pritchett A. Human-automated judgment learning: Applying interpersonal learning to investigate human interaction with alerting systems. 2002. Paper presented at Air Traffic Management for Commercial and Military Systems, Irvine, CA, United States.