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

Steps in the Modeling Process01:14

Steps in the Modeling Process

404
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...
404

You might also read

Related Articles

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

Sort by
Same author

Reverse engineering what makes a symbol memorable.

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

What divides and unites our memories: Multifactor trial-wise predictions of memory across 6+ million trials.

The American psychologist·2026
Same author

Distinctive places make memories stick.

Nature human behaviour·2026
Same author

The neural underpinnings of aphantasia: a case study of identical twins.

Cerebral cortex (New York, N.Y. : 1991)·2025
Same author

The memorability of voices is predictable and consistent across listeners.

Nature human behaviour·2025
Same author

Navigating Memorability Landscapes: Hyperbolic Geometry Reveals Hierarchical Structures in Object Concept Memory.

bioRxiv : the preprint server for biology·2024
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: Oct 26, 2025

Group Synchronization During Collaborative Drawing Using Functional Near-Infrared Spectroscopy
07:53

Group Synchronization During Collaborative Drawing Using Functional Near-Infrared Spectroscopy

Published on: August 5, 2022

2.2K

A tutorial on capturing mental representations through drawing and crowd-sourced scoring.

Wilma A Bainbridge1

  • 1Department of Psychology, University of Chicago, 5848 S University Ave, Beecher Hall 303, Chicago, IL, 60637, USA. wilma@uchicago.edu.

Behavior Research Methods
|August 3, 2021
PubMed
Summary
This summary is machine-generated.

This tutorial introduces modern methods for quantifying drawings in psychological research. It covers designing, recording, and analyzing drawing data using computer vision and crowd-sourcing for objective measurement.

Keywords:
Computer visionMental representationsOnline experimentsVisual production

More Related Videos

Investigating the Effect of Visual Imagery and Learning Shape-Audio Regularities on Bouba and Kiki
07:31

Investigating the Effect of Visual Imagery and Learning Shape-Audio Regularities on Bouba and Kiki

Published on: September 13, 2019

10.3K
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.5K

Related Experiment Videos

Last Updated: Oct 26, 2025

Group Synchronization During Collaborative Drawing Using Functional Near-Infrared Spectroscopy
07:53

Group Synchronization During Collaborative Drawing Using Functional Near-Infrared Spectroscopy

Published on: August 5, 2022

2.2K
Investigating the Effect of Visual Imagery and Learning Shape-Audio Regularities on Bouba and Kiki
07:31

Investigating the Effect of Visual Imagery and Learning Shape-Audio Regularities on Bouba and Kiki

Published on: September 13, 2019

10.3K
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.5K

Area of Science:

  • Psychology
  • Cognitive Science
  • Human-Computer Interaction

Background:

  • Drawings capture rich mental representations but are underutilized in psychology due to quantification challenges.
  • Traditional drawing analysis methods suffer from high variance and subjectivity.
  • Recent technological advancements offer new objective quantification possibilities.

Purpose of the Study:

  • To provide a comprehensive tutorial on modern methods for drawing experiments in psychology.
  • To demonstrate how to design, record, and objectively quantify drawing data.
  • To empower researchers with skills for utilizing drawings as a psychological measure.

Main Methods:

  • Leveraging pen-tracking technology for detailed drawing data capture.
  • Employing computer vision techniques for objective image analysis.
  • Utilizing online crowd-sourcing for scalable data quantification.
  • Presenting code examples and web architecture tutorials for accessibility.

Main Results:

  • Demonstration of objective quantification methods for drawing data.
  • Practical guidance on designing and implementing drawing experiments.
  • Accessibility of methods through code examples and tutorials for various skill levels.

Conclusions:

  • Modern methods enable objective quantification of drawings, overcoming previous limitations.
  • Drawing data can be effectively captured and analyzed for psychological insights.
  • This tutorial equips researchers to incorporate drawing-based measures into their studies.