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Editors' Introduction: Best Papers from the 18th International Conference on Cognitive Modeling.

Terrence C Stewart1, Christopher W Myers2

  • 1National Research Council Canada, University of Waterloo.

Topics in Cognitive Science
|June 30, 2021
PubMed
Summary

Researchers are advancing computational systems that mimic the mind. This work introduces new methods for complex cognitive models using Reinforcement Learning and Learning By Instruction, linking them to neuroscience.

Keywords:
Best papersCognitive modelingICCM

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • The International Conference on Cognitive Modeling (ICCM) fosters global research on computational systems modeling mental processes.
  • ICCM 2020, the 18th meeting, was notably the first to be held virtually.
  • This issue highlights four key papers from ICCM 2020, representing advancements in the field.

Purpose of the Study:

  • To present leading research in cognitive modeling from ICCM 2020.
  • To showcase novel computational techniques for building sophisticated cognitive models.
  • To explore the integration of cognitive models with neuroscientific findings.

Main Methods:

  • Development of novel techniques for cognitive model construction.
  • Application of Reinforcement Learning for creating larger, more complex models.
  • Utilization of Learning By Instruction paradigms for model development.
  • Integration of neuroscientific data from the hippocampus and cerebellum to inform cognitive processes.

Main Results:

  • Two papers introduce innovative methods: Reinforcement Learning and Learning By Instruction.
  • Two papers demonstrate the connection between cognitive models and neuroscience.
  • These models provide insights into memory and conditioning processes by constraining them with neural data.

Conclusions:

  • The presented work advances the field of cognitive modeling through novel computational approaches.
  • The findings underscore the importance of neuroscientific constraints in developing realistic cognitive models.
  • This research bridges computational approaches with biological plausibility for understanding the mind.