Undersampling biases are common in the optimal stopping literature, especiallyfor economic full choice problems. Among these kinds of number-based studies,the moments of the distribution of values that generates the options (i.e., thegenerating distribution) seem to influence participants’ sampling rate.However, a recent study reported an oversampling bias on a different kind ofoptimal stopping task: where participants chose potential romantic partners fromimages of faces (Furl et al., 2019). The authors hypothesised that thisoversampling bias might be specific to mate choice. We preregistered thishypothesis and so, here, we test whether sampling rates across differentimage-based decision-making domains a) reflect different over- or undersamplingbiases, or b) depend on the moments of the generating distributions (as shownfor economic number-based tasks). In two studies (N = 208 andN = 96), we found evidence against the preregisteredhypothesis. Participants oversampled to the same degree across domains (comparedto a Bayesian ideal observer model), while their sampling rates depended on thegenerating distribution mean and skewness in a similar way as number-basedparadigms. Moreover, optimality model sampling to some extent depended on thethe skewness of the generating distribution in a similar way to participants. Weconclude that oversampling is not instigated by the mate choice domain and thatsampling rate in image-based paradigms, like number-based paradigms, depends onthe generating distribution.