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Action Potentials

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Graded Potential

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Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or...

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Related Experiment Video

Updated: Jul 1, 2025

Investigating the Effects of Antipsychotics and Schizotypy on the N400 Using Event-Related Potentials and Semantic Categorization

Investigating the Effects of Antipsychotics and Schizotypy on the N400 Using Event-Related Potentials and Semantic Categorization

Published on: November 19, 2014

A predictive coding model of the N400.

Samer Nour Eddine¹, Trevor Brothers², Lin Wang³

¹Department of Psychology and Center for Cognitive Science, Tufts University, United States of America.

|March 1, 2024

Summary

This summary is machine-generated.

Predictive coding offers a unifying theory for the N400 brain response during language comprehension. This computational model explains the N400 as prediction error, linking neural activity to meaning inference.

Keywords:

Bayesian inference Language comprehension Orthographic Prediction Prediction error Semantic

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Published on: April 12, 2018

Area of Science:

Cognitive Neuroscience
Computational Neuroscience
Psycholinguistics

Background:

The N400 event-related potential is crucial for understanding language comprehension.
Existing research lacks a unified theory for the N400's temporal and functional characteristics.

Purpose of the Study:

To propose predictive coding as a unifying framework for the N400.
To formalize the N400 as a lexico-semantic prediction error within a computational model.

Main Methods:

Implementation of a predictive coding computational model.
Analysis of model outputs to explain N400 properties.

Main Results:

The model successfully formalizes the N400 as lexico-semantic prediction error.
Model predictions align with N400 sensitivity to lexical variables, priming, and context.
The predictive coding algorithm naturally explains N400 temporal dynamics and neural links.

Conclusions:

Predictive coding provides a biologically plausible framework for understanding the N400.
This approach links the N400 to Bayesian inference and prediction error signaling in the brain.
Suggests a common computational mechanism for inference across different cognitive domains.