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Related Concept Videos

Hindsight Biases01:12

Hindsight 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?
The Representativeness Heuristic02:13

The Representativeness Heuristic

The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Prediction Intervals01:03

Prediction 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. 
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First Derivative Test: Problem Solving01:25

First 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...
Residual Plots01:07

Residual Plots

A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
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Related Experiment Videos

Past performance is indicative of future returns.

Nathaniel J Smith1, Nandakumar S Narayanan, Mark Laubach

  • 1Interdepartmental Neuroscience Program, Yale University, Yale University School of Medicine, New Haven, CT 06519, USA.

Neuron
|July 31, 2009
PubMed
Summary
This summary is machine-generated.

Neuronal activity after a trial outcome persists until the next one, influencing behavior. This persistent activity in the prefrontal cortex and caudate nucleus is key for learning and performance adjustment.

Related Experiment Videos

Area of Science:

  • Neuroscience
  • Cognitive Neuroscience
  • Decision Making

Background:

  • Neuronal activity following trial outcomes is thought to guide learning and behavioral adjustments.
  • Understanding the temporal dynamics of this activity is crucial for elucidating learning mechanisms.

Purpose of the Study:

  • To investigate the persistence of neuronal activity related to trial outcomes in specific brain regions.
  • To determine the correlation between this persistent activity and subsequent behavioral adjustments.

Main Methods:

  • Electrophysiological recordings in rodents.
  • Analysis of neuronal firing patterns in the prefrontal cortex and caudate nucleus.
  • Correlation analysis between neuronal activity and behavioral data.

Main Results:

  • Persistent neuronal activity, time-locked to trial outcomes, was observed.
  • This activity extended until the initiation of the subsequent trial.
  • The duration and magnitude of this persistent activity correlated significantly with behavioral adjustments.

Conclusions:

  • Outcome-related neuronal activity persists beyond the immediate trial, suggesting a role in maintaining information for future decisions.
  • The prefrontal cortex and caudate nucleus are key areas involved in this persistent signaling.
  • This sustained activity provides a neural basis for learning and adaptive behavior.