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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

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Published on: January 23, 2017

Systematic variation in proportion judgments: Spatial features impact adults' strategies and decisions.

Michelle A Hurst¹, Susan C Levine²

¹Department of Psychology, Rutgers Center for Cognitive Science, Rutgers University, New Brunswick.

Journal of Experimental Psychology. General

|January 26, 2026

Summary

Proportional reasoning is often flawed due to discrete units, predictability, and spatial separation. Manipulating these visual factors significantly impacts adult performance and strategies in proportional tasks.

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Area of Science:

Cognitive Psychology
Developmental Psychology
Human Decision Making

Background:

Proportional reasoning is crucial for daily tasks, yet individuals often err, especially with discrete or spatially separated information.
Previous research shows format-dependent biases in proportional reasoning, but a systematic investigation of multiple visual factors is lacking.

Purpose of the Study:

To systematically investigate how discreteness, predictability, and spatial separateness of visual proportional stimuli affect reasoning.
To identify which spatial factors most significantly impact proportional reasoning performance and strategies in adults.

Main Methods:

Manipulated visual proportional stimuli based on discreteness (enumerable units), predictability, and spatial separateness.
Employed mathematical models to formalize and infer participants' reasoning strategies.

Main Results:

Discreteness, predictability, and spatial separateness significantly influenced adults' performance and strategy use.
Interactions among these factors were observed, with spatial separateness and predictability potentially playing a more critical role.

Conclusions:

Visual and spatial features of proportional stimuli critically impact reasoning, highlighting the interplay between spatial, numerical, and relational information.
Findings offer insights into the mechanisms of proportional reasoning and suggest avenues for improving proportional task design.