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

733
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...
733
Cognitive Learning01:21

Cognitive Learning

916
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
916
Social Foundations of Self I: Play and Game01:24

Social Foundations of Self I: Play and Game

138
The development of self in children is deeply rooted in social interactions, mainly through stages of play and structured games. These stages, outlined by sociologist George Herbert Mead, illustrate how children progressively learn to understand and adopt social roles, forming a cohesive sense of self.The Play Stage: Imitation and Simple Role-TakingIn the early years of childhood, the play stage is characterized by imitative behavior, where children engage in role-playing based on familiar...
138
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.4K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.4K
Purposive Learning01:22

Purposive Learning

373
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...
373
Introduction to Learning01:18

Introduction to Learning

825
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...
825

You might also read

Related Articles

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

Sort by
Same author

Species Assembly Is Lineage Specific: Phylogenetic Divergent Species Aggregate in Some Lineages but Segregate in Others.

Ecology letters·2026
Same author

Differential 'resuscitation' from the seed microbiota: a plant-holobiont ecological strategy for buffering stresses.

The New phytologist·2026
Same author

Chemoautotrophic carbon fixation in thermokarst lakes on the Tibetan Plateau.

Nature communications·2025
Same author

A Review of Abrupt Permafrost Thaw: Definitions, Usage, and a Proposed Conceptual Framework.

Current climate change reports·2025
Same author

Functional traits of Asteraceae species vary with arbuscular mycorrhizal fungal identity and phylogeny.

Mycorrhiza·2025
Same author

Nutrient limitation and seasonality associated with phytoplankton communities and cyanotoxin production in a large, hypereutrophic lake.

Harmful algae·2025
Same journal

The host-microbiome dimension of ecological regime shifts.

Trends in ecology & evolution·2026
Same journal

The emerging field of wild animal welfare science.

Trends in ecology & evolution·2026
Same journal

Integrating nutritional mutualists into the evolution of defense.

Trends in ecology & evolution·2026
Same journal

Formation of three great Asian plateaus, climate change, and biodiversity: (Trends Ecol. Evol. 40, 970-982; 2025).

Trends in ecology & evolution·2026
Same journal

Digital twins as a tool for ecosystem research.

Trends in ecology & evolution·2026
Same journal

Constraint and convergence in the evolution of vertebrate sound production.

Trends in ecology & evolution·2026
See all related articles

Related Experiment Video

Updated: Dec 21, 2025

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking
13:40

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

Published on: December 16, 2010

17.0K

Artificial Intelligence Accidentally Learned Ecology through Video Games.

Lou Barbe1, Cendrine Mony1, Benjamin W Abbott2

  • 1ECOBIO, OSUR, CNRS, Université de Rennes 1, 35000 Rennes, France.

Trends in Ecology & Evolution
|May 12, 2020
PubMed
Summary
This summary is machine-generated.

An advanced artificial intelligence system beat top human players in StarCraft II. Researchers propose using this AI to test complex ecological hypotheses in virtual ecosystems.

More Related Videos

Automated Interactive Video Playback for Studies of Animal Communication
07:21

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

13.9K
The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.7K

Related Experiment Videos

Last Updated: Dec 21, 2025

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking
13:40

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

Published on: December 16, 2010

17.0K
Automated Interactive Video Playback for Studies of Animal Communication
07:21

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

13.9K
The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.7K

Area of Science:

  • Artificial intelligence
  • Ecology
  • Computational biology

Background:

  • Advanced artificial intelligence (AI) systems have achieved superhuman performance in complex strategy games like StarCraft II.
  • Real-time strategy games, such as StarCraft II, create virtual ecosystems where players compete for resources and habitats.
  • These virtual environments unintentionally mirror many ecological phenomena and dynamics.

Purpose of the Study:

  • To repurpose a state-of-the-art AI system, originally designed for strategic gameplay, as a novel tool for ecological research.
  • To address previously intractable ecological hypotheses by leveraging the AI's advanced capabilities in complex system simulation.
  • To explore the potential of AI in advancing ecological understanding through virtual experimentation.

Main Methods:

  • Utilizing an advanced AI system that has demonstrated superior performance against human experts in StarCraft II.
  • Adapting the AI's capabilities to simulate and analyze virtual ecosystems within the StarCraft II environment.
  • Employing the AI to systematically test and validate ecological theories and hypotheses that are difficult to study using traditional methods.

Main Results:

  • The AI system successfully navigated and competed within the virtual ecosystem of StarCraft II.
  • The virtual ecosystem dynamics generated by the AI unintentionally replicated key ecological principles.
  • The AI demonstrated potential for generating novel insights into ecological processes.

Conclusions:

  • Advanced AI systems developed for gaming can be repurposed as powerful tools for ecological research.
  • Virtual ecosystems simulated by AI offer a promising platform for testing complex ecological hypotheses.
  • This interdisciplinary approach has the potential to significantly advance ecological science and our understanding of natural systems.