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Related Concept Videos

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Chunking is a powerful cognitive technique that improves short-term memory retention by organizing information into smaller, more manageable units. The brain, limited by working memory capacity, can more easily process and store information when it is divided into "chunks" rather than presented as discrete, unrelated elements. Chunking is especially useful when dealing with large amounts of information, such as numerical sequences, words, or complex ideas.
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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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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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Natural chunk-and-pass language processing: Just another joint source-channel coding model?

Kevin B Clark1,2,3,4

  • 1Research and Development Service, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA.

Communicative & Integrative Biology
|August 8, 2018
PubMed
Summary
This summary is machine-generated.

The "Chunk-and-Pass" model addresses language evolution challenges using a "Now-or-Never" bottleneck. This processing method relies on efficient coding and predictions, potentially representing a form of joint source-channel coding.

Keywords:
fault-tolerance and correctionhierarchical language representationlanguage coding and decodinglanguage compressionmultilevel lossy code-transmission rate and securitynatural language acquisition and pattern dualityshannon's noisy channel coding and separation theorems

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Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Evolutionary Biology

Background:

  • The "Now-or-Never" bottleneck poses significant challenges for language processing and evolution.
  • Existing models struggle to fully account for the complexities of language acquisition and development.
  • Christiansen and Chater's theoretical treatment introduces the "Chunk-and-Pass" model to address these issues.

Purpose of the Study:

  • To analyze the theoretical framework of the "Chunk-and-Pass" model.
  • To evaluate how the model addresses the "Now-or-Never" bottleneck in language processing.
  • To explore the implications of the model for language evolution and computational linguistics.

Main Methods:

  • Theoretical analysis of the "Chunk-and-Pass" model.
  • Examination of the model's reliance on linguistic coding, compression, and hierarchical representation.
  • Information-theoretic interpretation of the "Now-or-Never" bottleneck and source-channel coding principles.

Main Results:

  • The "Chunk-and-Pass" model mitigates the "Now-or-Never" bottleneck through specific processing mechanisms.
  • The model's effectiveness appears linked to optimal joint source-channel coding principles.
  • Information-theoretic analysis reveals inherent tradeoffs in rate and security within the model.

Conclusions:

  • The "Chunk-and-Pass" model offers a promising explanation for language structure and development.
  • The model's mechanisms align with information-theoretic concepts of source-channel coding.
  • Evolvable associative networks may support "Chunk-and-Pass" speech processing, suggesting it's a specialized form of joint source-channel coding.