The role of categorization and scale endpoint comparisons in numerical information processing: A two-process model

Area of Science:

• Cognitive Psychology
• Social Psychology
• Decision Making

Background:

• Understanding numerical information processing is crucial for decision-making.
• Bounded scales influence how individuals interpret numerical data.
• Previous models may not fully capture the nuances of score evaluation.

Purpose of the Study:

• To propose and test a two-process model of numerical information processing for bounded scales.
• To investigate how people form impressions of scores presented on scales.
• To identify factors influencing score evaluation and judgment.

Main Methods:

• Development of a two-process conceptualization.
• Conducting six experiments to test the model's predictions.
• Manipulating scale range, cognitive load, need for cognition, and regulatory focus.

Main Results:

• Scores are evaluated through spontaneous categorization and, when uncertain, comparison to scale endpoints.
• Larger scale ranges (e.g., 0-100 vs. 0-10) result in less extreme score evaluations.
• The impact of scale magnitude is reduced under high cognitive load, low need for cognition, or when evaluating extreme scores.

Conclusions:

• The proposed two-process model effectively explains score evaluation on bounded scales.
• Cognitive factors like certainty, motivation, and cognitive load moderate the judgment process.
• Regulatory focus can influence the choice of scale endpoint for comparison.