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: 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...
Decision Making01:20

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
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...
Theory of Attribution II: Kelley's Covariation Theory01:29

Theory of Attribution II: Kelley's Covariation Theory

Attribution theory plays a crucial role in social psychology, helping to explain how individuals interpret the causes of behavior. One prominent model within this field is Harold Kelley's covariation theory, which provides a systematic approach to determining whether internal traits or external circumstances drive a person's actions. The model posits that individuals rely on three key types of information—consensus, consistency, and distinctiveness—to make these judgments.Consensus: Comparing...
Attribution Theory00:56

Attribution Theory

Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958). An internal factor is an...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

You might also read

Related Articles

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

Sort by
Same author

Identity-specific reward expectations in orbitofrontal cortex guide goal-directed choices.

PLoS biology·2026
Same author

The oracle and the didact: Orbitofrontal influences on learning and dopaminergic error signaling.

Neuron·2026
Same author

Slow breathing impacts inter-organ dynamics modulating brain function and risk behavior.

Neuron·2026
Same author

Separable and integrated pleasantness coding for appetitive and aversive odors across olfactory and ventral prefrontal cortices.

Nature communications·2026
Same author

Body core compression enhances interoception leading to altered risk choices.

Biological psychology·2026
Same author

Neurocognition and food cue-related brain reactivity after fiber supplementation within a high-protein, plant-based diet in individuals with overweight and prediabetes: A randomized-controlled trial (the DISTAL-study).

Clinical nutrition (Edinburgh, Scotland)·2026

Related Experiment Video

Updated: Jun 12, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Decoding different roles for vmPFC and dlPFC in multi-attribute decision making.

Thorsten Kahnt1, Jakob Heinzle, Soyoung Q Park

  • 1Bernstein Center for Computational Neuroscience, Charité-Universitätsmedizin Berlin, Berlin, Germany.

Neuroimage
|June 1, 2010
PubMed
Summary

The brain integrates multi-attribute values for decision-making. The ventromedial prefrontal cortex (vmPFC) encodes the combined value, while the dorsolateral prefrontal cortex (dlPFC) handles attribute variability, aiding complex choices.

Related Experiment Videos

Last Updated: Jun 12, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Decision Science

Background:

  • Successful decision-making relies on accurate expected value representations.
  • Multi-attribute options require integrating individual attribute predictions into a unified expected value.
  • Attribute values can either align or conflict, influencing choice outcomes.

Purpose of the Study:

  • To investigate the neural encoding of combined reward predictions in the brain.
  • To identify brain regions responsible for encoding the variability of individual attribute value predictions.
  • To differentiate the roles of specific prefrontal cortex regions in multi-attribute decision processes.

Main Methods:

  • Functional magnetic resonance imaging (fMRI) data acquisition during a multi-attribute value integration task.
  • Application of time-resolved pattern recognition techniques, specifically support vector regression.
  • Analysis of fMRI patterns to decode neural representations of combined value and attribute variability.

Main Results:

  • Distributed fMRI patterns in the ventromedial prefrontal cortex (vmPFC) encode the combined expected value.
  • The dorsolateral prefrontal cortex (dlPFC) encodes the variability of individual attribute value predictions.
  • Neural encoding of combined value and attribute variability occurs in distinct, non-overlapping brain regions.

Conclusions:

  • The vmPFC plays a crucial role in representing integrated values to guide choices.
  • The dlPFC's encoding of attribute variability reflects choice ambiguity and integration difficulty.
  • Distinct prefrontal cortex regions support different computational aspects of multi-attribute decision-making.