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

Variation01:19

Variation

7.2K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
7.2K
Hindsight Biases01:12

Hindsight Biases

3.9K
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? 
3.9K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

884
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
884
Confirmation Biases01:31

Confirmation Biases

7.2K
The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
7.2K

You might also read

Related Articles

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

Sort by
Same author

Parafoveal preview differentially modulates word frequency and contextual predictability effects during reading.

Journal of vision·2026
Same author

Using Simulations to Explore Sampling Distributions: An Antidote to Hasty and Extravagant Inferences.

eNeuro·2025
Same author

Beyond Letters: Optimal Transport as a Model for Sub-Letter Orthographic Processing.

Neurobiology of language (Cambridge, Mass.)·2025
Same author

Measuring the semantic priming effect across many languages.

Nature human behaviour·2025
Same author

Crowdsourcing multiverse analyses to explore the impact of different data-processing and analysis decisions: A tutorial.

Psychological methods·2025
Same author

Parafoveal preview benefits magnified.

Cognition·2025

Related Experiment Video

Updated: Sep 11, 2025

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology
05:38

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Published on: June 29, 2021

2.5K

Can prediction error explain predictability effects on the N1 during picture-word verification?

Jack E Taylor1,2, Guillaume A Rousselet2, Sara C Sereno2

  • 1Department of Psychology, Goethe University Frankfurt, Frankfurt, Germany.

Imaging Neuroscience (Cambridge, Mass.)
|August 13, 2025
PubMed
Summary

Early visual word recognition may not follow simple prediction error models. Our study found event-related potential (ERP) effects opposite to predictions, challenging existing theories of predictive coding in language processing.

Keywords:
N1N170predictionpredictive codingword recognition

More Related Videos

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.6K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

670

Related Experiment Videos

Last Updated: Sep 11, 2025

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology
05:38

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Published on: June 29, 2021

2.5K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.6K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

670

Area of Science:

  • Cognitive Neuroscience
  • Psycholinguistics
  • Computational Neuroscience

Background:

  • Predictability effects in visual word recognition are often observed in early electrophysiological responses, specifically the N1 component of event-related potentials (ERPs).
  • Existing research suggests predicted words elicit lower amplitude N1s, aligning with simple predictive coding theories.
  • However, findings on the magnitude and interactions of predictability effects have been inconsistent, with limitations in past study designs.

Purpose of the Study:

  • To test whether early effects of predictability in visual word recognition reflect prediction error by examining the interaction between prediction magnitude and certainty.
  • To investigate the validity of a simple predictive coding account in explaining the N1 component's response to predictable words.

Main Methods:

  • A preregistered study using a picture-word verification paradigm.
  • Manipulated the continuous predictability of target nouns (picture-congruent) based on picture-name association norms.
  • Recorded event-related potentials (ERPs) from 68 participants during the task.

Main Results:

  • Observed a pattern of N1 effects that was opposite to the predictions of a simple predictive coding framework.
  • The interaction between prediction magnitude and certainty did not yield results consistent with basic predictive coding models.

Conclusions:

  • The findings challenge the straightforward application of simple predictive coding accounts to explain early predictability effects in visual word recognition.
  • Suggests that the neural mechanisms underlying word processing may involve more complex predictive processes than previously assumed.