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Natural and Artificial Concepts01:24

Natural and Artificial Concepts

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In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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Visual System01:26

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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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In general, a schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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Consider a man with a mass of 70 kg seated in a chair connected to a pin support through a member BC. If the man maintains an upright position, the task is to determine the horizontal and vertical reactions of the chair on the man when the member makes a 45° angle with the horizontal. At this moment, the man has a speed of 5 m/s, increasing at a rate of 1 m/s².
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Deductive Reasoning01:16

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Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
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Video Experimental Relacionado

Updated: Jan 7, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Diálogo visual con consistencia semántica: un enfoque impulsado por conocimiento externo

Shanshan Du1, Hanli Wang1

  • 1The College of Electronic and Information Engineering, Tongji University, Shanghai, China; The School of Computer Science and Technology, Tongji University, Shanghai, China; The Key Laboratory of Embedded System and Service Computing (Ministry of Education), Tongji University, Shanghai, China.

Neural networks : the official journal of the International Neural Network Society
|December 30, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta un nuevo modelo de diálogo visual (SCVD+) que utiliza grafos de escena estructurados y conocimiento externo para mejorar la precisión. Aborda problemas de sesgo y conocimiento en la IA multimodal para una mejor interacción humano-máquina.

Palabras clave:
razonamiento de conocimiento transmodalgrafo de conocimiento multimodalconsistencia semánticadiálogo visual

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Área de la Ciencia:

  • Inteligencia Artificial
  • Interacción Humano-Computadora
  • Visión por Computadora

Sus antecedentes:

  • El diálogo visual, una parte clave de la interacción inteligente humano-máquina, enfrenta desafíos en la respuesta a preguntas multivuelta basadas en el contexto visual y el historial del diálogo.
  • Los modelos existentes sufren de sesgos en el modelado multimodal, incluida la asimetría de información y la inconsistencia de representación, lo que lleva a una comprensión incompleta y decisiones sesgadas.
  • La dependencia del conocimiento externo introduce ruido y reduce la precisión debido a la mala calidad y la diversidad limitada.

Objetivo del estudio:

  • Proponer un novedoso modelo de diálogo visual con consistencia semántica mejorado por conocimiento externo (SCVD+) para abordar los desafíos existentes.
  • Mitigar la asimetría de información y la inconsistencia de representación en el modelado multimodal para el diálogo visual.
  • Mejorar la precisión, coherencia y capacidades de razonamiento de los sistemas de diálogo visual.

Principales métodos:

  • Construcción de grafos de escena visuales y textuales estructurados y de grano fino para capturar relaciones de objetos y asociaciones de palabras.
  • Integración de conocimiento de sentido común externo para reducir la inconsistencia de representación y mejorar la interpretabilidad del modelo.
  • Empleo de una estrategia de fusión y razonamiento de conocimiento de doble nivel para integrar pistas implícitas de modelos preentrenados grandes con información explícita del grafo de escena.

Principales resultados:

  • El modelo propuesto SCVD+ aborda eficazmente la asimetría de información y la inconsistencia de representación.
  • La integración de conocimiento externo y una novedosa estrategia de fusión mejora la diversidad del conocimiento y las capacidades de razonamiento.
  • Los resultados experimentales en los conjuntos de datos VisDial v0.9, VisDial v1.0 y OpenVisDial 2.0 demuestran la efectividad del método.

Conclusiones:

  • El modelo SCVD+ ofrece un avance significativo en los sistemas de diálogo visual al mejorar la consistencia semántica y la integración del conocimiento.
  • El enfoque mejora la comprensión multimodal y la toma de decisiones, allanando el camino para una interacción humano-máquina inteligente más robusta.
  • El estudio destaca la importancia de los grafos de escena estructurados y el conocimiento externo diverso para respuestas de diálogo visual precisas y coherentes.