| Summary: | Undersampling biases
are common in the optimal stopping literature, especially for economic full
choice problems. Among these kinds of number-based studies, the moments of the
distribution of values that generates the options (i.e., the generating
distribution) seem to influence participants' sampling rate. However, a recent
study reported an oversampling bias on a different kind of optimal stopping
task: where participants chose potential romantic partners from images of
faces. The authors hypothesised that this oversampling bias might be specific
to mate choice. We preregistered this hypothesis and so, here, we test whether
sampling rates across different image-based decision-making domains a) reflect
different over- or undersampling biases, or b) depend on the moments of the
generating distributions (as shown for economic number-based tasks). In two
studies (N = 208 and N = 96), we found evidence against the preregistered
hypothesis. Participants oversampled to the same degree across domains
(compared to a Bayesian ideal observer model), while their sampling rates
depended on the generating distribution mean and skewness in a similar way as
number-based paradigms. Moreover, optimality model sampling to some extent
depended on the the skewness of the generating distribution in a similar way to
participants. We conclude that oversampling is not instigated by the mate
choice domain and that sampling rate in image-based paradigms, like
number-based paradigms, depends on the generating distribution.
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