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Long-term memory is a relatively permanent type of memory, capable of storing vast amounts of information over extended periods. Its storage capacity is generally considered unlimited.
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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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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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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...
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Pattern memory cannot be completely and truly realized in deep neural networks.

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Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Computer Vision

Background:

  • Deep neural networks (DNNs) demonstrate advanced capabilities, often surpassing human performance in specific tasks.
  • However, DNNs lack interpretability and exhibit unpredictable behaviors, posing challenges for AI development.
  • The theoretical boundary between DNN computational power and human cognition remains a critical, unresolved issue.

Purpose of the Study:

  • To propose a novel framework for analyzing DNN working capabilities.
  • To investigate DNNs' cognitive response characteristics using visual illusion images.
  • To establish a new foundation for advancing artificial general intelligence (AGI).

Main Methods:

  • Developed a novel working capability analysis framework for DNNs.
  • Utilized innovative cognitive response characteristics on visual illusion images.
  • Employed a fine-adjustable sample image construction strategy.

Main Results:

  • DNNs can approximate human standards in pattern classification, object detection, and semantic segmentation.
  • DNNs are unable to achieve true independent pattern memorization.
  • DNNs' advanced abilities stem from powerful sample classification on known data.

Conclusions:

  • DNNs' super-cognitive abilities are based on sample classification, not genuine understanding or memorization.
  • This finding highlights a fundamental difference between DNNs and human cognition.
  • Establishes a new theoretical basis for future AGI research.