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Related Concept Videos

Outliers and Influential Points01:08

Outliers and Influential Points

An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the vertical...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
What Are Outliers?01:12

What Are Outliers?

Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...

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Related Experiment Videos

Improving Parameter Recovery in Computational Models: Employing Outlier-Insensitive Loss Functions.

Mingqian Guo1, Karin Roelofs1,2, Bernd Figner1,2

  • 1Behavioural Science Institute, Radboud University, Thomas van Aquinostraat 4, Nijmegen, GD 6525 The Netherlands.

Computational Brain & Behavior
|July 16, 2026
PubMed
Summary

Outlier data biases computational models in decision-making tasks. Using outlier-insensitive loss functions significantly improves parameter recovery compared to standard log-likelihood, enhancing model reliability.

Related Experiment Videos

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Decision Science

Background:

  • Parameter estimation reliability is vital for computational models in decision-making.
  • Log-likelihood loss functions are standard but sensitive to outlier data.
  • Outliers can bias parameter estimation and subsequent analyses.

Purpose of the Study:

  • To compare the performance of outlier-insensitive loss functions against the standard log-likelihood function.
  • To evaluate parameter recovery in computational models under varying proportions of outlier data.
  • To assess the impact of outliers on statistical power in cognitive modeling.

Main Methods:

  • Compared three outlier-insensitive loss functions with log-likelihood.
  • Tested performance in a reinforcement learning model and an intertemporal choice model.
  • Systematically varied outlier presence from 0% to 25% of data.

Main Results:

  • Log-likelihood shows substantial impairment in parameter identification with even small outlier proportions.
  • Outlier-insensitive loss functions markedly improve computational model parameter recovery.
  • As few as 5% outlier trials can undermine statistical power to detect condition differences.

Conclusions:

  • Outlier-insensitive loss functions are recommended for non-hierarchical model estimation.
  • These functions enhance the reliability of cognitive models as measurement tools.
  • Accounting for outliers is crucial for accurate parameter estimation and valid conclusions.