Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Distributions to Estimate Population Parameter

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...

Introduction to Statistical Process Control

Introduction to Statistical Process Control

Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

Statistical Analysis: Overview

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...

Statistical Analysis System (SAS)

Statistical Analysis System (SAS)

SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...

Small-signal Diode Model

Small-signal Diode Model

In analyzing the behavior of diodes in circuits, the relationship between the current through a diode and the voltage across it is of particular interest, especially when considering the effect of a direct current (DC) bias voltage. When applied, this DC bias influences the diode's operating point, known as the Q point, around which the current-voltage (I-V) characteristic of the diode exhibits exponential behavior. Introducing a small, time-varying signal on top of this bias aids in examining...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A Distracted Scientist: The Life and Contributions of John Senders.

Human factors·2020

Same author

A review of recent research in social robotics.

Current opinion in psychology·2020

Same author

Individual Differences in Attributes of Trust in Automation: Measurement and Application to System Design.

Frontiers in psychology·2019

Same author

The Intricacies of User Adjustments of Alerting Thresholds.

Human factors·2017

Same author

A Closed-Loop Model of Operator Visual Attention, Situation Awareness, and Performance Across Automation Mode Transitions.

Human factors·2016

Same author

Human-Robot Interaction: Status and Challenges.

Human factors·2016

Search research articles

Related Experiment Video

Updated: Jan 28, 2026

Automated Detection and Analysis of Exocytosis

Automated Detection and Analysis of Exocytosis

Published on: September 11, 2021

Extending Three Existing Models to Analysis of Trust in Automation: Signal Detection, Statistical Parameter

Thomas B Sheridan¹

¹Massachusetts Institute of Technology, Lexington, USA.

|February 28, 2019

Summary

This summary is machine-generated.

This study proposes three quantitative models for trust in automation, adapting existing frameworks for signal detection, parameter estimation, and control systems. These models offer new ways to measure and understand trust in human-system interactions.

Keywords:

automation cognition cognitive architectures cognitive modeling expert systems human–automation interaction individual differences mathematical modeling methods and skills trust in automation

More Related Videos

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Related Experiment Videos

Last Updated: Jan 28, 2026

Automated Detection and Analysis of Exocytosis

Automated Detection and Analysis of Exocytosis

Published on: September 11, 2021

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Area of Science:

Human-Computer Interaction
Automation Systems
Cognitive Science

Background:

Existing literature on trust in automation lacks standardized definitions and frameworks.
Diverse theoretical perspectives complicate the measurement and application of trust in automation.

Purpose of the Study:

To propose three quantitative models for assessing trust in automation.
To adapt existing scientific models for application to trust in automation.
To provide a foundation for quantitative trust measures in human-system interaction design.

Main Methods:

Reviewed existing trust-in-automation literature, definitions, and frameworks.
Reinterpreted and revised three established quantitative models: signal detection, statistical parameter estimation calibration, and internal model-based control.
Applied these revised models to the context of trust in automation.

Main Results:

Developed three quantitative models for trust in automation.
Presented quantitative and graphical representations of the trust models.
Discussed measures for trust and trust calibration with illustrative examples.

Conclusions:

The proposed quantitative models can be applied to provide objective trust measures in future research and system design.
The models offer a unified approach to understanding trust across different automation contexts.
Facilitates improved human-system interaction design through quantifiable trust assessment.