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Related Experiment Video

Updated: Jun 10, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Recognition-based inference: When is less more in the real world?

Thorsten Pachur¹

¹Department of Psychology, University of Basel, Missionsstr. 60/64, 4055 Basel, Switzerland. thorsten.pachur@unibas.ch

Psychonomic Bulletin & Review

|August 13, 2010

Summary

This summary is machine-generated.

The less-is-more effect in decision-making shows that recognizing fewer options can lead to more accurate inferences. This study reveals that dependencies between recognition validity and knowledge validity limit this effect.

Related Experiment Videos

Last Updated: Jun 10, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Area of Science:

Cognitive Psychology
Decision Making
Heuristics and Biases

Background:

The common assumption is that more information always improves decision-making.
Goldstein and Gigerenzer (2002) introduced the recognition heuristic and its counterintuitive 'less-is-more' effect, where recognizing fewer items can lead to better inferences.
A key condition for this effect is recognition validity exceeding knowledge validity, assuming independence from the number of recognized objects (n).

Purpose of the Study:

To investigate how violations of the independence assumption affect the less-is-more effect.
To determine the impact of validity dependencies (correlations between validities and n) on inference accuracy.
To explore cognitive and ecological factors influencing the relationship between recognition validity and the number of recognized objects.

Main Methods:

Analysis of empirical datasets to identify validity dependencies.
Computer simulations to model the impact of these dependencies on the less-is-more effect.
Discussion of cognitive (e.g., memory) and ecological (e.g., environmental frequencies) factors.

Main Results:

Validity dependencies between recognition and knowledge validity are common in real-world data.
These dependencies significantly limit the occurrence and strength of the less-is-more effect.
The number of recognized objects (n) is often correlated with validity, challenging prior assumptions.

Conclusions:

The less-is-more effect is more fragile than previously assumed, being sensitive to validity dependencies.
Cognitive and environmental factors can create these dependencies, influencing heuristic performance.
Future research should consider these dependencies when applying or studying the recognition heuristic.