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

Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the population that is...
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...
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...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...

You might also read

Related Articles

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

Sort by
Same author

Contrasting holistic-compensatory with probabilistic heuristic strategies in multi-attribute decisions.

Psychonomic bulletin & review·2026
Same author

Rhythmic sampling of multiple decision alternatives in the human brain.

Nature communications·2026
Same author

Grounding mathematics in an integrated conceptual structure, part II: intervention study demonstrating robust learning and retention through a grounded curriculum.

Frontiers in psychology·2026
Same author

Within-alternative processing supports transitivity of preferences in multiattribute choice.

Psychological review·2025
Same author

The role of attention in multi attribute decision making.

Nature communications·2025
Same author

Learning to Decompose: Human-Like Subgoal Preferences Emerge in Neural Networks Learning Graph Traversal.

Open mind : discoveries in cognitive science·2025
Same journal

Evaluation of an open-face 8-channel transmit 64-channel receive 7T head coil for neuroimaging.

Frontiers in neuroscience·2026
Same journal

Acoustic stimulation in pain management: neurobiological mechanisms and clinical applications-a narrative review.

Frontiers in neuroscience·2026
Same journal

Local brain connectome parameters across the spectrum of clinical cognitive decline.

Frontiers in neuroscience·2026
Same journal

Body mass index affects EEG microstate dynamics through blood viscosity in high-altitude environments.

Frontiers in neuroscience·2026
Same journal

Disrupted glymphatic function and its relationship with sleep and cognitive impairment in ME/CFS assessed via DTI-ALPS.

Frontiers in neuroscience·2026
Same journal

Neuromorphic-inspired multi-view global-local fusion for IR-UWB radar dynamic gesture recognition.

Frontiers in neuroscience·2026
See all related articles

Related Experiment Video

Updated: Jun 1, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Testing multi-alternative decision models with non-stationary evidence.

Konstantinos Tsetsos1, Marius Usher, James L McClelland

  • 1Department of Psychology, Cognitive, Perceptual and Brain Sciences, University College London London, UK.

Frontiers in Neuroscience
|May 24, 2011
PubMed
Summary
This summary is machine-generated.

Participants in decision-making tasks show a preference for options with unique evidence profiles. The non-linear leaky competing accumulator (LCA) model effectively explains this choice behavior in multi-alternative scenarios.

Keywords:
diffusioninhibitionleaky integrationmultiple alternativesperceptual decisions

Related Experiment Videos

Last Updated: Jun 1, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Area of Science:

  • Cognitive psychology
  • Computational neuroscience
  • Decision science

Background:

  • Sequential sampling models, including race and diffusion models, are established frameworks for understanding decision-making.
  • The non-linear leaky competing accumulator (LCA) model offers a sophisticated approach to modeling choice behavior.

Purpose of the Study:

  • To extend existing sequential sampling models to multi-alternative choice scenarios.
  • To investigate how dynamic, temporally correlated evidence influences choice behavior.
  • To evaluate model performance against psychophysical experimental data.

Main Methods:

  • Development of extended sequential sampling models for multi-alternative choices.
  • Design and execution of a psychophysical experiment with dynamically changing evidence.
  • Analysis of participant choices in relation to temporal evidence correlations.

Main Results:

  • Participants demonstrated a bias towards selecting alternatives with temporally anti-correlated evidence profiles.
  • This observed bias was accurately captured by the LCA model.
  • The findings challenge the explanatory power of several alternative multi-alternative choice models.

Conclusions:

  • The LCA model provides a robust framework for understanding choice in multi-alternative scenarios with dynamic evidence.
  • Temporal correlations in evidence significantly impact decision-making strategies.
  • Future research should consider the role of evidence dynamics in choice models.