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Our findings demonstrate that how people evaluate described and experienced options depends on the learning mode of the other option in the choice set, highlighting a previously overlooked boundary condition of discrepancies between description- and
experience-based choice.

Finally, participants searched for more information when there was only one experienced option in the mixed-mode condition than when both options were experienced in the pure-experience condition.

Also, there was no DE gap when operationalized as over/underweighting of rare events based on the latent payoff distribution in the mixed-mode condition. Further, participants’ choices were not biased toward the described or experienced option.

In the mixed-mode condition, however, the value and probability weighting functions did not differ between the described and the experienced options, suggesting that people evaluated them based on a joint representation despite the different learning modes.

Using CPT’s value and probability weighting functions to characterize how observed outcome and probability information was subjectively distorted in people’s choices, we found clear differences between the pure description and experience conditions in line with previous research.

To test this, we studied risky choices between options that are presented in either the same or in different learning modes—that is, choices between two described or two experienced options or between a described and an experienced option (i.e., a mixed learning mode; see image).

In this research, we studied how the evaluation of risky options, choice, and search behavior depend on the choice context in terms of learning mode (description vs. experience).

Research on the description–experience (DE) gap has primarily compared scenarios where all options are either described or experienced through sampling. However, it's unclear how decisions are made when learning modes between options are inconsistent.