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

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. 
The...
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting the...
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?

You might also read

Related Articles

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

Sort by
Same author

Frequency-dependent modulation of foveal contrast sensitivity by fine-scale exogenously triggered attention.

eLife·2026
Same author

Outcomes of clinical pharmacists with extended prescribing rights in a hemodialysis unit.

Journal of the American Pharmacists Association : JAPhA·2026
Same author

Involvement of the FTO A > T polymorphism in body composition and lipid profile changes after aerobic training in adults with overweight and obesity.

Diabetes, obesity & metabolism·2026
Same author

Compensation in audiovisual speech perception: Discounting the pen in the mouth.

Journal of experimental psychology. Learning, memory, and cognition·2025
Same author

Structural Priming Treatment in Aphasia: The Role of Lexical and Abstract Syntactic Representations.

American journal of speech-language pathology·2025
Same author

Frequency-selective contrast sensitivity modulation driven by fine-tuned exogenous attention at the foveal scale.

bioRxiv : the preprint server for biology·2025
Same journal

Are language models models?

The Behavioral and brain sciences·2026
Same journal

Large language models illuminate the mechanistic underpinnings of the creative aspect of language use (CALU), long regarded as a mystery.

The Behavioral and brain sciences·2026
Same journal

LLMs as a platform for studying constraint interaction: Motivation and challenges.

The Behavioral and brain sciences·2026
Same journal

Beyond the data gap: Children create languages, violate their input statistics, and exhibit critical periods.

The Behavioral and brain sciences·2026
Same journal

Not-so-strange love: Language models and generative linguistic theories are more compatible than they appear.

The Behavioral and brain sciences·2026
Same journal

Rich data drive generalization: Lessons from machine learning for linguistics and cognitive science.

The Behavioral and brain sciences·2026
See all related articles

Related Experiment Videos

Seeking predictions from a predictive framework.

T Florian Jaeger1, Victor Ferreira

  • 1Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14627-0268, USA. fjaeger@bcs.rochester.edu

The Behavioral and Brain Sciences
|June 25, 2013
PubMed
Summary
This summary is machine-generated.

Forward models are proposed for language processing, but require a learning context. Future research should focus on the prediction error these models aim to minimize for robust language understanding.

Related Experiment Videos

Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Neuroscience

Background:

  • Forward models offer a framework for understanding predictive processes in language.
  • Existing proposals for forward models in language processing lack a sufficient learning context.
  • Pickering & Garrod's (P&G) proposal highlights the need for a stronger theoretical foundation.

Purpose of the Study:

  • To advocate for integrating learning principles into forward models of language processing.
  • To identify the critical role of prediction error minimization within these models.
  • To establish a robust framework for future research on predictive language mechanisms.

Main Methods:

  • Conceptual analysis of existing forward modeling approaches in psycholinguistics.
  • Theoretical integration of machine learning principles (e.g., prediction error) with language acquisition.
  • Literature review and synthesis of research on predictive coding and language.

Main Results:

  • Forward models must be situated within a learning framework to be effective.
  • The concept of prediction error is central to the function of forward models in language.
  • A learning-based approach provides a more comprehensive understanding of predictive language processing.

Conclusions:

  • Forward models are essential for understanding language prediction, but must incorporate learning.
  • Defining the specific prediction error is crucial for advancing the utility of forward models.
  • This work provides a foundation for developing more sophisticated computational models of language processing.