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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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Neurotransmitters are integral to the brain's communication system, enabling neurons to transmit signals across synapses. This chemical exchange underpins various cognitive functions, including memory processes. The role of neurotransmitters in memory is multifaceted, influencing the encoding, consolidation, and retrieval of memories through their action on different neural circuits.
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Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
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Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
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Beyond Markov: Transformers, memory, and attention.

Thomas Parr1, Giovanni Pezzulo2, Karl Friston3

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Summary
This summary is machine-generated.

Transformer architectures, successful in AI, offer insights into brain function by using non-Markovian models and attention mechanisms for better sequential data prediction. This highlights the importance of memory and relevant context over recency.

Keywords:
Markovianattentionfactor graphsgenerativeinferencememorytransformers

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Cognitive science

Background:

  • Predictive processing models explain brain function.
  • Transformer architectures have shown significant success in AI tasks.
  • Both models utilize attention mechanisms.

Purpose of the Study:

  • To explore what predictive processing models can learn from transformer architectures.
  • To understand the role of non-Markovian generative models in sequential data processing.
  • To connect working memory concepts in cognitive science with transformer mechanisms.

Main Methods:

  • Analyzing transformer architectures as implicit non-Markovian generative models.
  • Characterizing deep temporal hierarchies and autoregressive models.
  • Examining the role of attention in contextualizing observations and predictions.

Main Results:

  • Transformer success stems from implicit non-Markovian generative models.
  • Attention mechanisms are crucial for both cognitive working memory and transformers.
  • Transformers leverage embedding spaces with strong metric priors on latent variables.

Conclusions:

  • Moving beyond the Markov assumption is vital for generative models handling sequential data.
  • Transformers effectively use metric priors and attention to prioritize relevant past information.
  • This approach enhances predictions for sequential data, including natural language.