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Modeling short- and long-term memory contributions to recent event recognition.

Robert M Nosofsky1, Rui Cao1, Samuel M Harding1

  • 1Department of Psychological and Brain Sciences.

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Summary

This study reveals that item-response learning in recognition memory is stronger for consistent-mapping foils than targets. This finding challenges previous assumptions about automatic learning in consistent-mapping tasks.

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Area of Science:

  • Cognitive Psychology
  • Human Memory Research
  • Experimental Psychology

Background:

  • Previous research suggested automatic item-response learning in consistent-mapping (CM) recognition tasks.
  • Past studies often used between-blocks designs, potentially allowing strategy and criterion shifts.
  • The role of item-response learning in CM versus varied-mapping (VM) and all-new (AN) conditions requires clarification.

Purpose of the Study:

  • To investigate item-response learning under consistent-mapping (CM) conditions using a mixed-list design.
  • To compare CM performance with varied-mapping (VM) and all-new (AN) conditions while controlling for strategies and criteria.
  • To test an exemplar-based random-walk model against observed recognition data.

Main Methods:

  • Employed a mixed-list design to present consistent-mapping (CM), varied-mapping (VM), and all-new (AN) items within the same experimental session.
  • Participants performed recognition judgments on pictures of everyday objects.
  • Developed and utilized an extended exemplar-based random-walk model for data analysis.

Main Results:

  • The mixed-list design yielded different performance patterns compared to pure-list designs.
  • Evidence suggests strong item-response learning for CM foils.
  • Evidence indicates weak item-response learning for CM targets.

Conclusions:

  • Item-response learning in recognition memory is not uniform across all item types within a consistent-mapping condition.
  • The findings suggest that item-response learning is more robust for foils than for targets in CM tasks.
  • The developed model successfully accounts for key qualitative effects observed in the recognition data.