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Videos de Conceptos Relacionados

Agonism and Antagonism: Quantification01:14

Agonism and Antagonism: Quantification

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When drugs are administered, they can elicit either an agonist or antagonist effect on the body. Agonism occurs when a drug activates a specific receptor, triggering a biological response. On the other hand, antagonism happens when a drug binds to the same receptors but blocks their activation, thereby preventing a biological response.
To quantify these effects, researchers use a dose-response curve, which provides valuable information about the potency and efficacy of a drug. Potency refers to...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Cognitivism01:17

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Cognitive psychology emerged as a significant field in the mid-20th century. It focused on understanding humans' internal mental processes. This approach emphasizes how people perceive, remember, think, and solve problems—elements critical to human cognition.
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Observational Learning01:12

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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...
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Naturalistic Observations02:30

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If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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Attribution Theory00:56

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Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).
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Video Experimental Relacionado

Updated: Sep 8, 2025

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

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Capturando el argumento en modelos basados en agentes

Leon Assaad1, Rafael Fuchs2, Kirsty Phillips3

  • 1Munich Center for Mathematical Philosophy, LMU, Geschwister-Scholl-Platz 1, 80539 Munich, Bavaria Germany.

Topoi : an international review of philosophy
|August 20, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Los modelos basados en agentes (ABM) pueden modelar argumentos complejos al distinguir argumentos como razones, silogismos y dialéctica. El marco NormAN, utilizando redes bayesianas, ofrece una nueva forma de comparar estos modelos y estudiar la difusión de argumentos.

Palabras clave:
Modelado basado en agentesArgumentaciónEpistemología BayesianaLa dialécticaLa epistemología social

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Área de la Ciencia:

  • Ciencias sociales computacionales
  • Inteligencia artificial
  • Ciencias cognitivas

Sus antecedentes:

  • Los modelos basados en agentes (ABM) son frecuentes para estudiar sistemas complejos y propiedades emergentes.
  • La creencia y la dinámica de opinión en ABM son relevantes para las redes sociales en línea y el discurso científico.
  • Los ABM existentes no han capturado completamente los escenarios de argumentación rica.

Objetivo del estudio:

  • Introducir un marco para los modelos de argumentación basados en agentes.
  • Para distinguir y analizar tres nociones de argumento: razones, silogismos y dialéctica.
  • Proporcionar un esquema de organización para comparar y elegir modelos de argumento.

Principales métodos:

  • Distinguir los argumentos como contenido proposicional (razones), relaciones premisa-conclusión (silogismos) y despliegue conversacional (dialéctica).
  • Utilizando el marco NormAN, que construye ABM en redes bayesianas, como modelo de referencia.
  • Analizando el continuo de complejidad en el modelado de cada noción de argumento.

Principales resultados:

  • Un nuevo esquema de organización para comparar y seleccionar modelos de argumento basados en agentes.
  • Aclaración de cómo las tres nociones de argumento se limitan entre sí.
  • Demostración de que el marco NormAN captura las facetas de argumentación familiares y las influencias dialécticas en la difusión de argumentos.

Conclusiones:

  • Los modelos basados en agentes pueden representar efectivamente la argumentación compleja, incluidos los aspectos dialécticos.
  • El marco NormAN proporciona un enfoque versátil para modelar el intercambio de argumentos.
  • La investigación adicional puede explorar cómo las consideraciones dialécticas impactan la difusión de argumentos en las poblaciones.