Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Observational Learning

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...

Automatic Processing and Automatic Social Behavior

Automatic Processing and Automatic Social Behavior

Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...

Behaviorism

Behaviorism

The field of behaviorism was pioneered by figures such as Ivan Pavlov, John B. Watson, and B.F. Skinner fundamentally shifted the focus of psychology to the observable and controllable aspects of human and animal behavior. This shift marked a critical evolution in the discipline, emphasizing scientific rigor and experimental methodology.
The core premise of behaviorism is its focus on observable behavior rather than internal thoughts or feelings. This approach argues that true scientific...

Generalization, Discrimination, and Extinction

Generalization, Discrimination, and Extinction

Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

Purposive Learning

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...

You might also read

Related Articles

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

Sort by

Same author

Detection and quantification of planar traveling waves in the EEG using spherical phase fitting.

Journal of neuroscience methods·2026

Same author

Consciousness indicators, mimicry, and internal variants.

Trends in cognitive sciences·2026

Same author

Modality-agnostic decoding of vision and language from fMRI.

eLife·2026

Same author

Age-related differences in memory encoding as a function of pupillary dynamics.

Neurobiology of aging·2026

Same author

Backward alpha band oscillations shape perceptual bias under probabilistic cues.

Communications biology·2026

Same author

Serotonergic-dependent awareness is required for trace conditioning in honeybees.

iScience·2025

Search research articles

Related Experiment Video

Updated: Nov 25, 2025

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

Published on: January 29, 2020

Comparing feedforward and recurrent neural network architectures with human behavior in artificial grammar learning.

Andrea Alamia¹, Victor Gauducheau², Dimitri Paisios^2,3

¹CerCo, CNRS, 31055, Toulouse, France. andrea.alamia@cnrs.fr.

Scientific Reports

|December 18, 2020

Summary

Recurrent neural networks better model human language acquisition than feedforward networks in artificial grammar learning tasks. Both architectures learn grammars, but recurrent networks show closer performance to human behavior, suggesting distinct roles in explicit and implicit learning.

More Related Videos

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Related Experiment Videos

Last Updated: Nov 25, 2025

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

Published on: January 29, 2020

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

Cognitive Science
Computational Neuroscience
Artificial Intelligence

Background:

Artificial neural networks (ANNs) now rival or exceed human performance in many domains, including language processing.
Comparing ANNs to human performance aids understanding of human cognition.
Artificial grammar learning (AGL) is crucial for language acquisition and can be learned by humans with minimal exposure and often implicit knowledge.

Purpose of the Study:

To investigate which neural network architecture, feedforward or recurrent, best models human behavior in artificial grammar learning.
To compare the learning capabilities of different neural network architectures with human performance in AGL.

Main Methods:

Tested human subjects and both feedforward and recurrent neural networks on four artificial grammars of varying complexity.
Utilized error back-propagation for neural network training.
Analyzed performance across different grammar complexity levels.

Main Results:

Both feedforward and recurrent networks learned the artificial grammars after a similar number of training sequences as humans.
Recurrent networks demonstrated performance closer to human behavior than feedforward networks, regardless of grammar complexity.
Performance differences across ten regular grammars suggest recurrent networks excel at explicit learning, while feedforward networks may model implicit learning.

Conclusions:

Recurrent neural network architectures provide a better computational model for human artificial grammar learning compared to feedforward architectures.
The findings support the hypothesis that recurrent networks are better suited for modeling explicit learning processes in language acquisition.
Feedforward networks may capture the dynamics of implicit learning, drawing parallels to their proposed roles in visual processing.