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

Observational Learning01:12

Observational Learning

155
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...
155
Modeling in Therapy01:26

Modeling in Therapy

61
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
61
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

498
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...
498
Behaviorism01:28

Behaviorism

2.3K
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...
2.3K
Introduction to Learning01:18

Introduction to Learning

355
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
355
Steps in the Modeling Process01:14

Steps in the Modeling Process

193
Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
193

You might also read

Related Articles

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

Sort by
Same author

Large language models accurately identify decision reasons in verbal reports.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

A reporting checklist for large language models in behavioural science.

Nature human behaviour·2026
Same author

How context shapes neural decision processing across the lifespan: evidence from an event-related potential study.

The journals of gerontology. Series B, Psychological sciences and social sciences·2026
Same author

Escaping the Jingle-Jangle Jungle: Increasing Conceptual Clarity in Psychology Using Large Language Models.

Current directions in psychological science·2026
Same author

Individual differences in risk preference: Selection and socialization effects.

Journal of personality and social psychology·2026
Same author

Communicating risks more comprehensively using simulated experience.

Trends in cognitive sciences·2026
Same journal

Exploring psychological tradeoffs: Developing and demonstrating an R Shiny app for Pareto optimization.

Behavior research methods·2026
Same journal

The performance of Bayesian fit measures in detecting misspecified multilevel structural equation modeling.

Behavior research methods·2026
Same journal

Psychometric functions from multiple responses : Dedicated to the memory of Colin L. Mallows.

Behavior research methods·2026
Same journal

Low-cost, open-source, full-stack software and Arduino-based hardware for control of commercially available animal behavior systems.

Behavior research methods·2026
Same journal

PyNeon: A Python package for the analysis of Neon multimodal mobile eye-tracking data.

Behavior research methods·2026
Same journal

Talking surveys: How photorealistic embodied conversational agents shape response quality, engagement, and satisfaction.

Behavior research methods·2026
See all related articles

Related Experiment Video

Updated: Jun 16, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

527

A tutorial on open-source large language models for behavioral science.

Zak Hussain1,2, Marcel Binz3,4, Rui Mata5

  • 1University of Basel, Basel, Switzerland. zakir.a.s.hussain@gmail.com.

Behavior Research Methods
|August 15, 2024
PubMed
Summary
This summary is machine-generated.

Open-source large language models (LLMs) can accelerate behavioral science research. This tutorial guides researchers on using Hugging Face for advanced conceptual and empirical work, addressing challenges like interpretability.

Keywords:
Behavioral scienceHugging faceLarge language models

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.1K

Related Experiment Videos

Last Updated: Jun 16, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

527
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.1K

Area of Science:

  • Behavioral Science
  • Computational Social Science
  • Artificial Intelligence

Background:

  • Large language models (LLMs) offer transformative potential for behavioral science research.
  • Open-source LLM frameworks provide transparency, reproducibility, and data protection crucial for scientific rigor.

Purpose of the Study:

  • To provide a primer on the Hugging Face ecosystem for behavioral scientists.
  • To demonstrate practical applications of open-source LLMs in advancing research.
  • To discuss challenges and future directions for LLMs in behavioral science.

Main Methods:

  • Tutorial-based approach using the Hugging Face ecosystem.
  • Demonstration of LLM applications: feature extraction, model fine-tuning for prediction, and behavioral response generation.
  • Code repository provided for practical implementation.

Main Results:

  • Successful application of open-source LLMs for feature extraction in behavioral data.
  • Demonstrated efficacy of fine-tuning LLMs for predictive modeling in behavioral science.
  • Generated realistic behavioral responses using LLMs.

Conclusions:

  • Open-source LLMs, particularly via Hugging Face, are powerful tools for behavioral science research.
  • Addressing challenges in interpretability and safety is key for future LLM integration.
  • LLMs are poised to significantly advance the conceptualization, analysis, and empirical work in behavioral science.