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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...
Finding Critical Values for Chi-Square01:18

Finding Critical Values for Chi-Square

Consider a curve representing sample data drawn randomly from a normally distributed population. One must construct confidence intervals to estimate or to test a claim regarding the population standard deviation. For example, a 95% confidence interval covers 95% of the area under the curve, and the remaining 5% is equally distributed on either side of the curve. To achieve such confidence intervals, one must determine the critical values. The critical values are simply the values separating the...
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the rated...
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...
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...

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Updated: Jul 17, 2026

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
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How Sure is the Driver? Modelling Drivers' Confidence in Left-Turn Gap Acceptance Decisions.

Floor Bontje1, Arkady Zgonnikov1

  • 1Department of Cognitive Robotics, Faculty of Mechanical Engineering, Delft University of Technology, Mekelweg 2, Delft, 2628 CD The Netherlands.

Computational Brain & Behavior
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Summary

Driver confidence in left-turn decisions depends on gap size, mirroring basic task findings. This study links confidence judgments to naturalistic driving behavior using a dynamic drift-diffusion model.

Keywords:
ConfidenceDecision makingDriver behaviorEvidence accumulationMetacognition

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Area of Science:

  • Cognitive Psychology
  • Human Factors Engineering
  • Neuroscience

Background:

  • Confidence judgments are subjective probability assessments accompanying decisions.
  • Understanding confidence mechanisms offers insights into human behavior.
  • Confidence in naturalistic dynamic tasks, like driving, remains understudied compared to laboratory settings.

Purpose of the Study:

  • To investigate driver confidence in left-turn gap acceptance decisions within a naturalistic driving context.
  • To connect fundamental research on confidence judgments with real-world driving behavior.
  • To model confidence judgments in dynamic decision-making during driving.

Main Methods:

  • A driver simulator experiment with 17 participants was conducted.
  • Investigated confidence in left-turn gap acceptance decisions.
  • Utilized an extended dynamic drift-diffusion model to capture confidence judgments.

Main Results:

  • Driver confidence was significantly influenced by the size of the gap to oncoming vehicles.
  • Confidence increased with gap size for accepted turns and decreased for rejected turns.
  • Confidence judgments correlated negatively with response times and positively with action dynamics, consistent with basic tasks.
  • The extended drift-diffusion model, incorporating gap size-dependent parameters and post-decision accumulation, accurately described confidence judgments.

Conclusions:

  • Fundamental principles of confidence research are applicable to dynamic, naturalistic decision-making, such as driving.
  • Gap size is a critical factor influencing confidence in driving decisions.
  • Computational models can effectively capture and predict confidence judgments in complex, real-world scenarios.