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

Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

295
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...
295
Causes of Social Behavior II: Cognitive Processes01:15

Causes of Social Behavior II: Cognitive Processes

248
Cognitive processes affect social behavior by guiding how individuals perceive, interpret, and respond to social stimuli. These mental processes enable individuals to assess others' behaviors, attribute causes to their actions, and form expectations based on past experiences.Causes of Behavior and Social JudgmentsIndividuals determine the causes of others' behaviors by distinguishing between personal traits and external circumstances. For example, if a friend frequently arrives late, an...
248
Scientific Nature of Social Psychology01:30

Scientific Nature of Social Psychology

708
Social psychology is a scientific discipline dedicated to understanding how individuals think, feel, and behave in social contexts. Unlike common sense, which relies on anecdotal experiences and intuition, social psychology employs systematic research and empirical methods to ensure objectivity and reliability. This distinction is fundamental in distinguishing scientifically supported findings from mere speculation.Four fundamental scientific values guide a structured approach to research in...
708
Behaviorism01:28

Behaviorism

7.2K
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...
7.2K
Causes of Social Behavior I: Actions and Characteristics of Individuals01:30

Causes of Social Behavior I: Actions and Characteristics of Individuals

403
The actions and characteristics of others heavily influence the causes of social behaviors. Emotional expressions serve as powerful social signals, shaping behaviors and interactions in significant ways. Whether through direct observation or subconscious processing, individuals constantly adjust their responses based on the emotions and attributes of those around them.Emotional Cues and Social ResponsesFacial expressions, tone of voice, and body language provide crucial emotional cues that...
403
Causes of Social Behavior III: Biological and Environmental Influences01:28

Causes of Social Behavior III: Biological and Environmental Influences

391
Social behavior is a complex phenomenon that arises from the interaction between biological predispositions and environmental influences. This intricate interplay shapes how individuals think, feel, and act in various social contexts. Understanding these mechanisms requires insights from psychology, neuroscience, genetics, and evolutionary theory.Environmental Influences on Social BehaviorEnvironmental factors, including temperature, odors, and visual stimuli, play a crucial role in shaping...
391

You might also read

Related Articles

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

Sort by
Same author

Caregiver Holding, Not Vocalizing, Supports Real-Time Vagal Regulation.

Infancy : the official journal of the International Society on Infant Studies·2025
Same author

An Application of Time Series Analysis to Single-Case Designs in an Intensive Behavioral Intervention for ADHD.

Journal of attention disorders·2025
Same author

Strategies to Implement a Community-Based, Longitudinal Cohort Study: The Whole Communities-Whole Health Case Study.

JMIR formative research·2024
Same author

Mothers speak less to infants during detected real-world phone use.

Child development·2024
Same author

Maternal contingent responses to distress facilitate infant soothing but not in mothers with depression or infants high in negative affect.

Developmental psychology·2023
Same author

Spontaneous infant crying modulates vagal activity in real time.

Developmental psychobiology·2023

Related Experiment Video

Updated: Mar 10, 2026

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

8.3K

Thinking Critically About Algorithms for Automated Detection of Behavior: 11 Guidelines for Social and Behavioral

Kaya de Barbaro1, Anna Madden-Rusnak2, Adela Timmons1

  • 1Department of Psychology, The University of Texas at Austin, Austin, Texas, USA.

Developmental Science
|March 9, 2026
PubMed
Summary

Developmental psychologists can now use AI and sensors to track behavior, but need guidelines to ensure accuracy and ethical use. This research offers practical advice for implementing these technologies effectively in developmental science.

Keywords:
accuracyartificial intelligencebest practicesgeneralizabilitymachine learningmobile sensing

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

9.9K
Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach
08:24

Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach

Published on: May 15, 2016

9.1K

Related Experiment Videos

Last Updated: Mar 10, 2026

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

8.3K
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

9.9K
Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach
08:24

Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach

Published on: May 15, 2016

9.1K

Area of Science:

  • Developmental Psychology
  • Human-Computer Interaction
  • Machine Learning in Behavioral Science

Background:

  • Mobile and wearable sensors with AI are increasingly used to detect behaviors and interactions in developmental psychology.
  • These technologies offer novel ways to study real-world learning experiences, impacting basic science and interventions.
  • However, researchers often lack the expertise to evaluate AI model accuracy and navigate implementation challenges.

Purpose of the Study:

  • To provide practical guidelines for developmental psychologists using AI and sensor technology.
  • To equip researchers with the knowledge to critically assess AI models and implement them effectively.
  • To foster technically sound, ethically responsive, and practical applications of AI in developmental research.

Main Methods:

  • Development of 11 practical guidelines for researchers.
  • Focus on common pitfalls and best practices in AI implementation for behavioral detection.
  • Incorporation of insights on model generalizability, accuracy interpretation, feasibility, ethics, and collaboration.

Main Results:

  • Guidelines address critical aspects of AI deployment in developmental science.
  • Recommendations cover interpreting accuracy statistics and understanding model generalizability.
  • Emphasis on real-world feasibility, ethical considerations, and interdisciplinary collaboration.

Conclusions:

  • The provided guidelines aim to enhance the rigor, equity, and impact of AI tools in developmental science.
  • Researchers are encouraged to adopt a critical perspective when leveraging AI for activity recognition.
  • Successful implementation requires technical soundness, ethical awareness, and community engagement.