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Los modelos de lenguaje grandes aprenden como los humanos: práctica intercalada y espaciada en el aprendizaje

Ying Xiong¹, Shiyu Wu¹

¹Shanghai Jiao Tong University, China.

Acta psychologica

|September 6, 2025

Resumen

Los humanos y los grandes modelos de lenguaje (LLM) aprenden lenguajes artificiales de manera diferente. Los LLM superan a los humanos, pero no replican las estrategias de aprendizaje humanas como los efectos de intercalación y espaciado.

Palabras clave:

Efecto de interconexión Los grandes modelos de lenguaje (LLM)La metacognición Aprendizaje morfológico Efecto de espaciado

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Área de la Ciencia:

Ciencias cognitivas
Inteligencia artificial
Lingüística computacional

Sus antecedentes:

Comprender los mecanismos de aprendizaje humanos es crucial para desarrollar una IA avanzada.
Los grandes modelos lingüísticos (LLM) son prometedores en la adquisición de idiomas, pero sus procesos de aprendizaje no se comprenden completamente.
Investigar cómo los LLM aprenden idiomas artificiales puede revelar diferencias fundamentales en comparación con la cognición humana.

Objetivo del estudio:

Comparar la adquisición de patrones morfológicos en un lenguaje artificial entre humanos y LLM.
Examinar cómo la estructura de entrada (bloqueada vs. entrelazada, yuxtapuesta vs. espaciada) afecta el aprendizaje en ambos sistemas.
Identificar las divergencias en los mecanismos de aprendizaje y las estrategias cognitivas.

Principales métodos:

Se empleó un paradigma de aprendizaje de lenguaje artificial.
Los participantes incluían humanos y tres LLM (GPT4mini, DeepSeek_V3, Llama3.1).
Se utilizaron tareas de clasificación e inflexión de verbos con una secuencia y presentación de entrada variadas.

Principales resultados:

Los LLM superaron constantemente a los humanos, demostrando un aprendizaje superior de pocos disparos.
El aprendizaje humano fue influenciado por los efectos de intercalación y espaciado, consistentes con las teorías cognitivas.
Los LLM mostraron respuestas dependientes del modelo a la estructura de entrada, que divergen de los patrones de aprendizaje humanos.
Los humanos exhibieron ilusiones metacognitivas con respecto a las preferencias de aprendizaje, a diferencia de la mayoría de los LLM.

Conclusiones:

Los mecanismos de aprendizaje humanos como el entrelazamiento y el espaciado no se traducen directamente en LLM.
El rendimiento de LLM es sensible a la estructura de entrada, pero de maneras distintas de la cognición humana.
Las diferentes arquitecturas de LLM exhiben diferentes grados de sensibilidad a la secuenciación y presentación de entrada.