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Memory-Based Simple Heuristics as Attribute Substitution: Competitive Tests of Binary Choice Inference Models.

Hidehito Honda¹, Toshihiko Matsuka², Kazuhiro Ueda^1,3

¹Graduate School of Arts and Sciences, The University of Tokyo.

Cognitive Science

|July 21, 2016

Summary

People use simple heuristics for difficult binary choices and knowledge for easy ones, aligning with attribute substitution theory. This research explores the boundary between heuristic and knowledge-based inference.

Keywords:

Attribute substitution Binary choice inference Knowledge-based inference The familiarity heuristic The fluency heuristic The recognition heuristic

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Area of Science:

Cognitive Psychology
Decision Science

Background:

Debate exists on whether binary choice inference relies on simple heuristics (e.g., recognition, fluency, familiarity) or available knowledge.
The attribute substitution framework proposes that inference strategy depends on task difficulty.

Purpose of the Study:

To investigate the boundary between heuristic and knowledge-based inference in binary choice tasks.
To test predictions of the attribute substitution framework regarding task difficulty and inference strategy.

Main Methods:

Competitive testing of binary choice inference models.
Models included simple heuristics (fluency, familiarity) and knowledge-based inference.

Main Results:

A familiarity heuristic model best explained inference patterns in subjectively difficult tasks.
A knowledge-based inference model best explained inference patterns in subjectively easy tasks.
Findings support the attribute substitution framework's predictions.

Conclusions:

Inference strategy in binary choices shifts based on subjective task difficulty.
Heuristic use (familiarity) dominates difficult tasks, while knowledge-based processing prevails in easy tasks.