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Related Concept Videos

Decision Making: Traditional Method01:14

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Related Experiment Video

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Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
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Crossword expertise as recognitional decision making: an artificial intelligence approach.

Kejkaew Thanasuan1, Shane T Mueller1

  • 1Department of Cognitive and Learning Sciences, Michigan Technological University Houghton, MI, USA.

Frontiers in Psychology
|October 14, 2014
PubMed
Summary

Solving crossword puzzles requires semantic and orthographic lexical memory skills. Experts excel by fluently retrieving information and strategically using orthographic patterns, unlike traditional AI models.

Keywords:
AIcrossword puzzlesexpertiselexical memory searchrecognitional decision making

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Area of Science:

  • Cognitive Psychology
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Crossword puzzle solving involves lexical memory, encompassing semantic (clue meaning) and orthographic (pattern recognition) aspects.
  • Existing models, like Mueller and Thanasuan (2013), focus on individual clue solving but don't fully capture expert strategies for complete puzzles.

Purpose of the Study:

  • To develop a computational model of crossword solving that integrates strategic factors for human-like performance.
  • To understand the comprehensive skill set required for solving entire crossword puzzles.
  • To compare model performance with human expert and novice solvers to identify key strategic and structural impacts.

Main Methods:

  • Development of a computational model incorporating strategic elements beyond simple memory access.
  • Comparison of the computational model's performance against human expert and novice crossword solvers.
  • Analysis of how strategic and structural factors influence overall crossword solving performance.

Main Results:

  • Expert crossword solvers demonstrate highly fluent semantic memory search and retrieval capabilities.
  • Experts effectively leverage orthographic-route solutions and employ specific strategies for utilizing orthographic information.
  • Traditional AI processes like error correction and backtracking are less critical for human crossword solvers compared to semantic and strategic approaches.

Conclusions:

  • Expert crossword solving is characterized by efficient semantic memory access and strategic utilization of orthographic information.
  • The developed computational model offers insights into the cognitive processes underlying human expertise in crossword solving.
  • Human crossword solving relies more on strategic memory retrieval and pattern matching than on error-driven computational methods.