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Updated: Jul 20, 2026

The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies
Published on: August 25, 2023
Decision biases in intertemporal choice and choice under uncertainty: testing a common account
Gretchen B Chapman1, Bethany J Weber
1Psychology Department, Rutgers University, 152 Frelinghuysen Road, Piscataway, NJ 08854-8020, USA. gbc@rci.rutgers.edu
Biases in risky and intertemporal choices stem from psychophysical properties. Different biases arise from different dimensions (time, probability), while similar biases reflect the same dimension (utility).
Area of Science:
- Decision-making research
- Behavioral economics
- Psychophysics
Background:
- Decision-making involves evaluating risks and future rewards.
- Biases in choices can be systematically observed and studied.
- Understanding these biases can inform economic and psychological theories.
Purpose of the Study:
- To investigate parallels between choice biases in risky and intertemporal decisions.
- To test if these biases originate from psychophysical properties of choice dimensions.
- To differentiate biases based on whether they relate to time, probability, or utility.
Main Methods:
- Two experiments were conducted comparing risky and intertemporal choice scenarios.
- Experiment 1 focused on the common difference and common ratio effects.
- Experiment 2 focused on the magnitude and peanuts effects.
Main Results:
- Common difference and common ratio effects were uncorrelated, suggesting different underlying dimensions (time vs. probability).
- Magnitude and peanuts effects were correlated, indicating shared underlying dimensions (utility).
- Biases correlated across monetary and health domains within specific effect types.
Conclusions:
- Choice biases can be explained by psychophysical properties of decision dimensions.
- Time and probability dimensions yield distinct biases, while utility yields similar biases.
- Findings support a unified psychophysical framework for understanding decision-making biases.
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