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The influence of context on information processing.

Jana Krivec1, Matej Guid2

  • 1School of Advanced Social Studies, Nova Gorica, Slovenia. jana.krivec@fuds.si.

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|February 23, 2020
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Summary
This summary is machine-generated.

Contextual factors like information dispersion, complexity, and positivity significantly impact information processing efficiency in chess. High dispersion and complexity, along with low positivity, hinder cognitive performance and working memory.

Keywords:
Chess gameCognitive loadContextual characteristicsInformation processing

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Area of Science:

  • Cognitive Psychology
  • Human-Computer Interaction
  • Artificial Intelligence

Background:

  • Information processing efficiency is influenced by contextual characteristics.
  • Understanding these relationships can optimize learning and system design.
  • The game of chess provides a complex domain for studying cognitive processes.

Purpose of the Study:

  • To investigate the association between contextual characteristics and information processing efficiency in chess.
  • To determine how context influences chunk size, chunk processing speed, and chunk recall.
  • To explore the impact of information dispersion, deviation, complexity, and positivity on cognitive performance.

Main Methods:

  • Utilized chess move sequences to define information processing efficiency and chunk characteristics.
  • Employed a modified chess program to extract context variables (dispersion, deviation, complexity, positivity).
  • Analyzed the relationship between extracted context variables and measures of information processing.

Main Results:

  • Higher information dispersion and complexity negatively correlated with processing efficiency.
  • Lower information positivity was associated with less efficient information processing.
  • Findings align with Cognitive Load Theory, ACT-R theory, and the Positivity Effect.

Conclusions:

  • Contextual factors significantly modulate information processing efficiency and working memory.
  • These findings have implications for developing intelligent tutoring and HCI systems.
  • Understanding cognitive load in complex domains can enhance user experience and learning outcomes.