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Las representaciones visuales de alto nivel en el cerebro humano están alineadas con grandes modelos de lenguaje

Adrien Doerig1,2,3, Tim C Kietzmann2, Emily Allen4,5

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Los grandes modelos de lenguaje (LLM) pueden modelar cómo el cerebro humano procesa información visual compleja de escenas naturales. Las incorporaciones de LLM de subtítulos de escenas mapean y reconstruyen con precisión la actividad cerebral, revelando información sobre la percepción visual.

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Neurociencia cognitivaCodificación neuronal

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Área de la Ciencia:

  • La neurociencia cognitiva
  • Inteligencia artificial
  • Visión por computadora

Sus antecedentes:

  • El cerebro humano procesa información visual compleja de escenas naturales, incluidas las relaciones de objetos e interacciones ambientales.
  • Actualmente falta un método cuantitativo para estudiar este complejo procesamiento de información visual en el cerebro.

Objetivo del estudio:

  • Investigar si la información contextual de los grandes modelos de lenguaje (LLM) puede ayudar a modelar la extracción del cerebro de información visual compleja de escenas naturales.
  • Determinar si las incorporaciones de LLM de las leyendas de la escena pueden caracterizar y predecir patrones de actividad cerebral.

Principales métodos:

  • Las incorporaciones de LLM de subtítulos de escenas se utilizaron para modelar la actividad cerebral evocada al ver escenas naturales.
  • Se realizaron comparaciones de modelos para evaluar la contribución del procesamiento integrado de la información de los LLM.
  • Las redes neuronales profundas fueron entrenadas para mapear las entradas de imagen a las representaciones de LLM.

Principales resultados:

  • Las incorporaciones de LLM de subtítulos de escenas caracterizaron con éxito la actividad cerebral, permitiendo una reconstrucción precisa de subtítulos de escenas a partir de datos neuronales.
  • La precisión del mapeo cerebral LLM proviene de la capacidad de los LLM para integrar información compleja más allá de las palabras individuales.
  • Las redes neuronales profundas entrenadas lograron una alineación superior con las representaciones cerebrales en comparación con los modelos de última generación, a pesar de los datos de entrenamiento limitados.

Conclusiones:

  • Las incorporaciones de LLM de subtítulos de escenas ofrecen un formato de representación valioso para comprender el procesamiento complejo de información visual en el cerebro.
  • Este enfoque proporciona un marco cuantitativo para el estudio de la cognición visual y sus fundamentos neuronales.
  • Los hallazgos destacan el potencial de los modelos de IA en la investigación de las neurociencias para decodificar las representaciones cerebrales.