Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Confidence Coefficient01:24

Confidence Coefficient

The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under both the...
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Confidence Intervals01:21

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...

You might also read

Related Articles

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

Sort by
Same author

Representing objects and features in long-term memory: A case for direct feature-feature binding.

Journal of experimental psychology. Learning, memory, and cognition·2026
Same author

Extreme-value signal detection theory for recognition memory: The parametric road not taken.

Psychological review·2026
Same author

Examining the signal-detection account of visual working memory.

Journal of experimental psychology. Learning, memory, and cognition·2026
Same author

Limited cue integration in metacognitive control decisions.

Memory & cognition·2026
Same author

Mending metacognitive illusions in JOLs: when neither cognitive nor metacognitive feedback is effective.

Memory (Hove, England)·2026
Same author

Toward the cognitive modeling of dynamic decision making.

Psychonomic bulletin & review·2026

Related Experiment Video

Updated: May 14, 2026

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses
14:05

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses

Published on: January 23, 2017

Validating a two-high-threshold measurement model for confidence rating data in recognition.

Arndt Bröder1, David Kellen, Julia Schütz

  • 1a School of Social Sciences, Department of Psychology , University of Mannheim , Mannheim , Germany.

Memory (Hove, England)
|February 13, 2013
PubMed
Summary
This summary is machine-generated.

Signal Detection models and the Two-High-Threshold model (2HTM) effectively separate memory performance from response bias in recognition tasks. New experiments validate 2HTM parameters and show response mapping is influenced by rating scale use, independent of bias.

Related Experiment Videos

Last Updated: May 14, 2026

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses
14:05

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses

Published on: January 23, 2017

Area of Science:

  • Cognitive Psychology
  • Psychometric Modeling

Background:

  • Signal Detection Theory (SDT) and the Two-High-Threshold model (2HTM) are established measurement models for recognition tasks.
  • Confidence ratings are frequently used in recognition memory to construct Receiver Operating Characteristic (ROC) curves.
  • The 2HTM requires a mapping function to align latent memory states with available response options.

Purpose of the Study:

  • To validate the core memory parameters of the 2HTM using existing data.
  • To investigate the impact of rating scale usage manipulations on 2HTM parameters.
  • To compare the performance of the 2HTM with traditional SDT in recognition memory tasks.

Main Methods:

  • Utilized unpublished data from two experiments (Bröder & Schütz, 2009) to validate 2HTM memory parameters.
  • Conducted three new experiments manipulating rating scale usage.
  • Employed ROC analysis and model comparisons between 2HTM and SDT.

Main Results:

  • Core memory parameters of the 2HTM were validated by existing data.
  • Response mapping parameters were selectively affected by rating scale manipulations.
  • These effects on response mapping were independent of overall old/new response bias.
  • 2HTM and SDT models demonstrated similar performance characteristics.

Conclusions:

  • The 2HTM provides a robust framework for disentangling memory performance and response bias in recognition tasks.
  • Response mapping parameters offer insights into individual differences in rating scale usage.
  • Both 2HTM and SDT are valuable and complementary tools for researchers in cognitive psychology.