具有注意力的序列对序列模型机械地映射到人类记忆搜索架构的架构
Nikolaus Salvatore1, Qiong Zhang2,3,4
1Rutgers University-New Brunswick, Department of Computer Science, Piscataway, NJ, USA.
Communications psychology
|October 14, 2025
概括
神经机器翻译模型,特别是带有注意力的循环神经网络 (RNN),反映了人类记忆检索机制. 这种融合为记忆中的上下文的功能作用提供了新的见解,并为人类记忆搜索提供了一个新的计算模型.
科学领域:
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 人类的记忆检索严重依赖于上下文信息.
- 现有的基于上下文的记忆模型可以解释现象,但不能解释这种架构的进化优势.
- 神经机器翻译 (NMT) 模型为理解记忆机制提供了一个潜在的平行.
研究的目的:
- 调查基础NMT架构是否表现出类似于人类记忆模型的机制.
- 为了利用NMT和人类记忆模型之间的融合,为一种新的记忆搜索认知模型.
- 通过NMT的透视来探索语境在人类记忆中的功能作用.
主要方法:
- 利用基于循环神经网络 (RNN) 的序列对序列模型,并从NMT.
- 实现了一个NMT模型作为人类记忆搜索的认知模型.
- 通过使用免费回忆数据集 (171名参与者) 对人类行为模式评估模型的性能.
主要成果:
- 带有注意力的NMT模型展示了与人类记忆的上下文维护和检索 (CMR) 模型保持一致的机制.
- 实施的NMT认知模型有效地捕获了平均和最佳的人类记忆搜索模式.
- 模型组件的相互作用揭示了新兴的内存搜索性能动态.
结论:
- 基础的NMT架构为上下文依赖记忆提供了功能解释.
- NMT模型作为人类记忆搜索的可解释和有效的认知模型.
- 这种跨学科的方法加深了对记忆功能作用的理解,并提供了新的建模途径.
相关概念视频
01:23Storage
361
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...
361
01:07Elaborative Rehearsals
335
Elaborative rehearsal is a crucial cognitive strategy that strengthens information encoding in long-term memory by making meaningful connections between new data and pre-existing knowledge. This approach contrasts with maintenance rehearsal, which involves simple repetition without delving into the significance of the information. While maintenance rehearsal might temporarily keep information active in short-term memory, it is less effective for long-term retention.
The effectiveness of...
The effectiveness of...
335
01:23System of Memory
7.2K
Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...
7.2K
01:22Chunking and Rehearsal in Sensory Memory
557
Improving short-term memory can be achieved through techniques like chunking and rehearsal. Chunking involves organizing information into larger, more manageable units. This technique is particularly useful for information that exceeds the typical memory span of between five and nine items. For instance, logging into an online account with a password like "ta89vq0179gz" involves grouping letters and numbers into three chunks—ta89, vq01, and 79gz. It makes large amounts of...
557
01:19Understanding Memory
1.3K
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...
1.3K
01:26Higher Mental Functions of Brain: Learning and Memory
1.9K
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...
1.9K