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Purposive Learning01:22

Purposive Learning

411
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
411
Language Development01:22

Language Development

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
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Language01:16

Language

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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
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Components of Language01:24

Components of Language

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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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Language and Cognition01:27

Language and Cognition

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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Observational Learning01:12

Observational Learning

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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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Video Experimental Relacionado

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SpikeBERT: Un spikformer de lenguaje aprendido de BERT con destilación de conocimiento

Changze Lv1, Tianlong Li1, Weiming Qiao1

  • 1School of Computer Science, Fudan University, Shanghai, China; Shanghai Key Laboratory of Intelligent Information Processing, Shanghai, China.

Neural networks : the official journal of the International Neural Network Society
|December 25, 2025
PubMed
Resumen

SpikeBERT, un enfoque novedoso de red neuronal de espigas (SNN), logra un rendimiento a nivel de BERT en tareas de lenguaje con un consumo de energía significativamente reducido. Este método mejora las SNN profundas para el procesamiento del lenguaje natural.

Palabras clave:
BERTDestilación de conocimientoComprensión del lenguajeRedes neuronales de espigas

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Área de la Ciencia:

  • Inteligencia Artificial
  • Neurociencia Computacional
  • Procesamiento del Lenguaje Natural

Sus antecedentes:

  • Las redes neuronales de espigas (SNN) ofrecen un aprendizaje profundo eficiente en energía, pero se quedan atrás en el rendimiento de las tareas de lenguaje debido a arquitecturas superficiales.
  • Las SNN existentes para tareas de lenguaje muestran una brecha de rendimiento en comparación con modelos transformadores como BERT.

Objetivo del estudio:

  • Mejorar Spikformer, un Transformer de espigas, para el procesamiento avanzado de tareas de lenguaje.
  • Desarrollar un método de destilación de conocimiento en dos etapas para entrenar SNN profundas.

Principales métodos:

  • Arquitectura Spikformer mejorada para tareas de lenguaje.
  • Destilación de conocimiento en dos etapas: preentrenamiento y ajuste fino de BERT.
  • Entrenamiento de SpikeBERT utilizando grandes conjuntos de datos no etiquetados y específicos de la tarea.

Principales resultados:

  • SpikeBERT logra un rendimiento de vanguardia entre las SNN.
  • Resultados comparables a BERT en la clasificación de texto en inglés y chino.
  • Demuestra un consumo de energía significativamente menor que BERT.

Conclusiones:

  • El método propuesto entrena eficazmente SNN profundas para tareas de lenguaje.
  • SpikeBERT ofrece una alternativa viable y energéticamente eficiente a los modelos transformadores para PNL.
  • Esta investigación cierra la brecha de rendimiento entre las SNN y los transformadores en el procesamiento del lenguaje.