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

Survival Tree01:19

Survival Tree

497
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...
497
Phylogenetic Trees03:21

Phylogenetic Trees

52.0K
Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.
52.0K
Phylogenetic Trees03:21

Phylogenetic Trees

6.8K
6.8K
Growth Models with Integration: Problem Solving01:27

Growth Models with Integration: Problem Solving

149
In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...
149
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

7.3K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
7.3K
Microbial Phylogeny01:28

Microbial Phylogeny

70
Understanding the evolutionary relationships among microorganisms is fundamental to microbial ecology and taxonomy. Phylogenetic trees are essential tools for inferring these relationships, relying primarily on comparative analyses of molecular sequences such as DNA, RNA, or proteins. In microbial studies, these trees typically depict the evolutionary paths of diverse bacterial and archaeal species by mapping genetic differences accumulated over time.Phylogenetic trees are composed of tips,...
70

You might also read

Related Articles

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

Sort by
Same author

AI-Discovered Cognitive Models Reveal Novel Insights into Human and Animal Learning.

bioRxiv : the preprint server for biology·2026
Same author

Information uncertainty influences learning strategy from sequentially delayed rewards.

PLoS computational biology·2026
Same author

Deep mechanism design: Learning social and economic policies for human benefit.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Whole-body physics simulation of fruit fly locomotion.

Nature·2025
Same author

How should the advancement of large language models affect the practice of science?

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Understanding dual process cognition via the minimum description length principle.

PLoS computational biology·2024
Same journal

Tau protein as a regulator of mitochondrial function and dynamics.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

A scalable, dividing cell model for the robust propagation and quantification of human sporadic Creutzfeldt-Jakob disease prions.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Epigenetic regulation of mesenchymal BMP signaling directs postnatal organ innervation.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Single-shot wide-field biochemical imaging at 1 kHz frame rate.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Morphogenesis and topological evolution of a frustrated nematic liquid crystal under confinement.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

B cell-intrinsic CXCR3 drives efficient generation of ectopic pulmonary germinal center responses to influenza A virus infection.

Proceedings of the National Academy of Sciences of the United States of America·2026
See all related articles

Related Experiment Video

Updated: Apr 4, 2026

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

36.3K

Evidence integration in model-based tree search.

Alec Solway1, Matthew M Botvinick2

  • 1Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544; asolway@vt.edu.

Proceedings of the National Academy of Sciences of the United States of America
|September 2, 2015
PubMed
Summary
This summary is machine-generated.

This study explores multi-step decision making, extending evidence accumulation models to understand how people choose between sequential options with cumulative rewards. The research offers a new computational framework for complex goal-directed choices.

Keywords:
drift-diffusion modelreinforcement learningreward-based decision making

More Related Videos

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.9K
Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

16.7K

Related Experiment Videos

Last Updated: Apr 4, 2026

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

36.3K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.9K
Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

16.7K

Area of Science:

  • Cognitive Neuroscience
  • Decision Science
  • Computational Psychology

Background:

  • Decision-making research often simplifies choices to single, mutually exclusive options.
  • Evidence accumulation models explain simple choices by tracking accumulating evidence for each option.
  • Real-world decisions frequently involve multiple sequential actions and cumulative rewards.

Purpose of the Study:

  • To investigate the deliberation process in multi-step, goal-directed decision making.
  • To extend existing evidence accumulation frameworks to complex, sequential choices.
  • To develop and test a computational model for understanding multi-step decision strategies.

Main Methods:

  • Conducted two experiments using techniques adapted from simple choice research.
  • Analyzed participant behavior in tasks requiring sequential actions and consideration of cumulative rewards.
  • Developed a novel computational model to interpret experimental findings.

Main Results:

  • Experimental data provided insights into the deliberation process for multi-step choices.
  • The proposed computational model successfully accounts for decision-making dynamics in sequential tasks.
  • Findings suggest that evidence accumulation principles can be extended to more complex decision structures.

Conclusions:

  • Multi-step decision making involves complex deliberation that can be modeled using extended evidence accumulation principles.
  • The new computational model offers a framework for understanding how individuals navigate choices with recursive structures and cumulative rewards.
  • This research bridges the gap between simple choice models and the complexities of real-life decision making.