Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

Social Scripts02:10

Social Scripts

9.4K
People tend to know what behavior is expected of them in specific, familiar settings. A script is a person’s knowledge about the sequence of events expected in a specific setting (Schank & Abelson, 1977). Essentially, scripts are a particular kind of schema, one containing default values for the features within an event. In the restaurant example, the script's features include the props (e.g., tables, menu, food, and money), the roles to be played (e.g., customer and waiter),...
9.4K
Natural Selection and Adaptation01:15

Natural Selection and Adaptation

158
Natural selection, a fundamental concept in evolutionary biology, is the mechanism by which evolution is driven, favoring organisms that are best adapted to their environments. This process enhances their chances of survival and reproduction. Adaptation, a key outcome of this process, involves genetic modifications that optimize an organism's functionality under specific environmental challenges, such as extreme cold or thinner air at high altitudes.
Beyond physical adaptations,...
158
Steps in the Modeling Process01:14

Steps in the Modeling Process

173
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...
173
Concepts and Prototypes01:24

Concepts and Prototypes

85
The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
85
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

3.6K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
3.6K
Modeling in Therapy01:26

Modeling in Therapy

43
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...
43

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

Iridium nitrenoid-catalyzed atroposelective C2 arylation of indoles.

Chemical science·2026
Same author

Ethics and Regulation of Human Brain Organoid Research: Recommendations from the Asia Pacific Neuroethics Working Group.

Asian bioethics review·2026
Same author

A Hypothalamic Inhibitory Circuit Encoding the Scalability of Stress Responses.

bioRxiv : the preprint server for biology·2026
Same author

Bilateral Rasmussen's Aneurysms: A Rare Etiology of Hemoptysis.

Annals of African medicine·2026
Same author

Structural and property synergistic correlations and sequential temperature-response mechanism of black carbon and dissolved black carbon.

Bioresource technology·2026
Same author

Magnetic Resonance Imaging in Pelvic Endometriosis: Imaging Characteristics and Anatomical Distribution at a Tertiary Care Study.

Annals of African medicine·2026

Video Experimental Relacionado

Updated: May 27, 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

16.6K

Modelos de acción mundial y humana hacia la ideación del juego

Anssi Kanervisto1, Dave Bignell1, Linda Yilin Wen1

  • 1Microsoft Research, Cambridge, UK.

Nature
|February 19, 2025
PubMed
Resumen

La inteligencia artificial generativa (IA) puede mejorar la ideación creativa al apoyar el diseño iterativo. Un nuevo modelo, WHAM, genera una jugabilidad consistente y modificaciones del usuario, alineando la IA con las prácticas creativas.

Más Videos Relacionados

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

9.3K
Measuring Engagement of Spectators of Social Digital Games
14:02

Measuring Engagement of Spectators of Social Digital Games

Published on: July 3, 2021

3.4K

Videos de Experimentos Relacionados

Last Updated: May 27, 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

16.6K
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

9.3K
Measuring Engagement of Spectators of Social Digital Games
14:02

Measuring Engagement of Spectators of Social Digital Games

Published on: July 3, 2021

3.4K

Área de la Ciencia:

  • Ciencias de la computación
  • Interacción hombre-computadora
  • Inteligencia artificial

Sus antecedentes:

  • La IA generativa ofrece un potencial para las industrias creativas, pero se enfrenta a desafíos en el apoyo de prácticas creativas básicas como el ajuste iterativo y el pensamiento divergente.
  • Los modelos actuales de IA generativa no apoyan adecuadamente los procesos creativos humanos esenciales, lo que limita su integración en los flujos de trabajo creativos.

Objetivo del estudio:

  • Alinear el desarrollo de modelos de IA generativos con las necesidades de los usuarios en prácticas creativas, específicamente dentro del desarrollo de juegos.
  • Introducir y evaluar un nuevo modelo de IA generativa que aborde las limitaciones en el apoyo a los procesos creativos iterativos y divergentes.

Principales métodos:

  • Desarrolló y evaluó el Modelo de Acción Mundial y Humana (WHAM), un modelo de IA generativa diseñado para el apoyo creativo.
  • Centrado en las necesidades del usuario dentro del desarrollo del juego para guiar las capacidades de la IA, enfatizando la consistencia, la diversidad y la persistencia de las modificaciones del usuario.

Principales resultados:

  • WHAM demuestra la capacidad de generar secuencias de juego consistentes y diversas.
  • El modelo persiste con éxito las modificaciones del usuario, una característica crítica para alinear la IA con flujos de trabajo creativos iterativos.
  • WHAM aprende la estructura relevante de los datos, lo que permite aplicaciones más amplias en comparación con las herramientas específicas de dominio anteriores.

Conclusiones:

  • La IA generativa, ejemplificada por WHAM, se puede desarrollar y evaluar de manera efectiva en función de las necesidades del usuario para apoyar la ideación y las prácticas creativas.
  • Las capacidades demostradas de WHAM representan un avance significativo en las herramientas de apoyo a la creatividad impulsadas por la IA, particularmente en dominios dinámicos como el desarrollo de juegos.