Related Experiment Video
Updated: Jul 10, 2026
07:15
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
Learned predictions of error likelihood in the anterior cingulate cortex
Joshua W Brown1, Todd S Braver
1Department of Psychology, CB 1125, Washington University, St. Louis, MO 63130, USA. jwbrown@artsci.wustl.edu
Science (New York, N.Y.)
|February 19, 2005
Summary
The anterior cingulate cortex (ACC) learns to predict error likelihood based on context, not just errors or conflict. This supports a reinforcement learning theory of ACC function.
Area of Science:
- Neuroscience
- Cognitive Psychology
- Computational Neuroscience
Background:
- The anterior cingulate cortex (ACC) is crucial for cognitive control.
- ACC's role in error and conflict processing is known, but its context-specific development is unclear.
Purpose of the Study:
- To investigate how ACC develops context-specific error and conflict responses.
- To explore the predictive functions of the ACC.
Main Methods:
- Utilized a modified stop-signal task.
- Integrated computational neural modeling with neuroimaging studies.
Main Results:
- ACC demonstrates the ability to predict error likelihood within specific contexts.
- This predictive learning occurs even in the absence of actual errors or response conflicts.
- Findings suggest ACC's function extends beyond direct error detection.
Conclusions:
- The ACC operates on a broader principle of error-likelihood prediction.
- This aligns with reinforcement learning theories, where conflict and error detection are specific instances.
- ACC's predictive capabilities are key to adaptive cognitive control.
Related Concept Videos
01:49Probability Laws
Overview
01:12Hindsight Biases
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now?
01:14Probability in Statistics
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
01:03Prediction Intervals
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
The...
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
The...
01:19Survival 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...
Building a Survival Tree
Constructing a survival tree begins...
01:25First Derivative Test: Problem Solving
Imagine an asset price that crashes to a low point, rebounds sharply as bargain-hunters step in, and then gradually declines. Such behavior can be modeled with a smooth function whose turning points represent locally overvalued and undervalued regions. A convenient example that captures rebound followed by decay is:The high and low points of this curve are identified using the first derivative test, which determines where the function changes from increasing to decreasing or vice versa. To...