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Attention and Salience in Preference Reversals
Last registered on July 08, 2020


Trial Information
General Information
Attention and Salience in Preference Reversals
Initial registration date
July 06, 2020
Last updated
July 08, 2020 5:06 PM EDT

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Primary Investigator
University of Zurich
Other Primary Investigator(s)
PI Affiliation
University of Zurich
Additional Trial Information
In development
Start date
End date
Secondary IDs
The preference reversal phenomenon refers to a pattern of decisions under risk where decision makers explicitly value long-shot lotteries above more moderate ones but then choose the latter, in contradiction with Expected Utility Theory. The phenomenon is typically demonstrated in paradigms involving pairs of lotteries consisting of a relatively safe lottery, called the P-bet (for "probability"), and a riskier lottery offering a larger prize (a long shot), called the $-bet. Decision makers often choose the P-bet in the direct choice task, but explicitly value the $-bet above the P-bet, in contradiction with the most basic tenets of decision theories under risk, and specifically with the indifference between a lottery and its certainty equivalent. This phenomenon reveals an inconsistency between elicitation methods which should be equivalent.

Salience theory postulates that reversals can occur because certain states are overweighted due to their salience. From a process perspective, this has a direct implication for visual attention. To test this hypothesis we conduct an eye-tracking experiment analyzing decisions under risk while implementing two treatments which manipulate salience. See the hypotheses in the attached pdf.
External Link(s)
Registration Citation
Alós-Ferrer, Carlos and Alexander Ritschel. 2020. "Attention and Salience in Preference Reversals." AEA RCT Registry. July 08. https://doi.org/10.1257/rct.5985-1.0.
Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Saccades, Fixations, and proportion of preference reversals
Primary Outcomes (explanation)
Proportion of predicted reversals: Proportion of the $-bets being evaluated higher than the P-bets, when the P-bet was chosen.

Fixations: Number of fixations in pre-specified Areas of Interest (AOIs), averaged across the relevant lotteries.

Transitions: Number of saccades and transitions from outcome-to-outcome AOIs, averaged across the relevant lotteries. Specific transitions represent the states according to Salience Theory (Bordalo et al., 2012).
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Classical preference reversal experiment conducted in individual lab sessions where subjects choose and evaluate lottery pairs. There will be two within-subject treatments for evaluating lotteries and eye movements will be measured with an eye tracker.
Experimental Design Details
Not available
Randomization Method
We constructed sequences that determine which lotteries will be evaluated jointly or in isolation. The computer assigns sequences to subjects.
Randomization Unit
Lotteries within subjects. (Each subject evaluates half the lotteries in isolation and half jointly. Randomization determines which lotteries will be evaluated jointly and isolated for a subject.)
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
64 individuals
Sample size: planned number of observations
Transitions and fixations in 6144 trials (2048 choices, 2048 joint evaluations, 2048 isolated evaluations). 128 proportions of preference reversals (2 per individual). 64 individual average number of fixations in each evaluation treatment. 64 individual average number of saccades on the most salient state and 64 individual average number of saccades on the least salient state during choice phase.
Sample size (or number of clusters) by treatment arms
It is a within treatment, 64 individuals.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We aim to collect data that could detect a small-to-medium-sized effect (Cohen's d=.35), which requires a sample size of N=52 for a one-tailed Wilcoxon-Signed-Rank test with alpha=.05 and a power of .80. We increase this number to 64 participants to be a multiple of 16 (=2 treatments x 8 counterbalancing measures). A total of 64 subjects yields an actual power of .87 (with d=.35 and alpha=.05). A sample of N=64 is able to detect a minimal effect of d=.31 (with power of .80 and alpha=.05). We will, therefore, collect data from 64 participants.
Supporting Documents and Materials

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IRB Name
Human Subjects Committee of the Faculty of Economics, Business Administration, and Information Technology, University of Zurich
IRB Approval Date
IRB Approval Number
OEC IRB # 2020-026