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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.7K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.7K
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

447
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
447
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

3.0K
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...
3.0K
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

2.5K
The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
2.5K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

4.3K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
4.3K
Transient and Steady-state Response01:24

Transient and Steady-state Response

613
In control systems, test signals are essential for evaluating performance under various conditions. The ramp function is effective for systems undergoing gradual changes, while the step function is suitable for assessing systems facing sudden disturbances. For systems subjected to shock inputs, the impulse function is the most appropriate test signal.
These test signals are integral in designing control systems to exhibit two key performance aspects: transient response and steady-state...
613

You might also read

Related Articles

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

Sort by
Same author

Serial effects in choice and confidence modulate each other: Evidence from 26 experiments.

Cognition·2026
Same author

Confidence in naturalistic decision making.

Neuroscience of consciousness·2026
Same author

Response Time as a Proxy for Decision Confidence: Insights From Type-2 ROC Analysis.

Open mind : discoveries in cognitive science·2026
Same author

The ethical impasse of current consciousness science.

Neuron·2026
Same author

Confidence-accuracy dissociations in perceptual decision making.

Vision research·2026
Same author

Type-1 and type-2 decisions feature computational noise of similar magnitude.

Communications psychology·2026

Related Experiment Video

Updated: Mar 6, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.4K

Correcting for unequal variance in signal detection models using response time.

Kiyofumi Miyoshi1, Dobromir Rahnev2, Hakwan Lau3,4,5

  • 1Graduate School of Informatics, Kyoto University, Kyoto, Japan.

Iscience
|March 5, 2026
PubMed
Summary

Signal detection theory (SDT) analysis using response time (RT) data provides a cost-effective method for measuring perceptual performance. This approach accurately quantifies detection sensitivity, accounting for unequal variances often missed by traditional methods.

Keywords:
Behavioral neuroscienceClassification DescriptionNeurosciencePsychology

More Related Videos

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.9K
A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

11.5K

Related Experiment Videos

Last Updated: Mar 6, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.4K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.9K
A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

11.5K

Area of Science:

  • Cognitive Psychology
  • Psychophysics
  • Neuroscience

Background:

  • Signal detection theory (SDT) traditionally uses confidence ratings to assess perceptual performance.
  • Canonical SDT models assume equal variances, which is often violated in detection tasks, leading to inaccurate performance estimates.
  • Asymmetric ROC curves in detection tasks suggest unequal signal variability between stimulus-present and absent trials.

Purpose of the Study:

  • To implement and validate an unequal-variance SDT model using response time (RT) data.
  • To compare RT-based SDT parameter estimates with traditional confidence-based methods.
  • To evaluate the accuracy of RT-derived sensitivity measures, particularly the unequal-variance extension da.

Main Methods:

  • Analysis of perceptual detection performance using response time (RT) data.
  • Implementation of an unequal-variance SDT model.
  • Comparison of RT-based SDT parameter estimates (σ, μ) with confidence-based estimates.
  • Calculation and comparison of sensitivity measures, including da and conventional d'.

Main Results:

  • RT-based estimates for SDT parameters (σ and μ) closely aligned with confidence-based estimates.
  • The sensitivity measure da, derived from both RT and confidence data, demonstrated strong consistency.
  • Conventional d' systematically overestimated detection performance compared to da measures, underscoring the impact of unequal variances.

Conclusions:

  • RT-based SDT analysis offers a robust and cost-effective alternative for quantifying perceptual detection performance.
  • Accounting for unequal variances is crucial for accurate SDT assessments, as demonstrated by the superiority of da over d'.
  • RT-based SDT is particularly valuable in scenarios where collecting confidence ratings is impractical.