{"pk":25890,"title":"Optimal stopping in a natural sampling task","subtitle":null,"abstract":"Sampling biases are often assumed to arise from the type of information that learners sample (Fiedler, 2008), the\npossibility of negative payoffs (Denrell, 2001), or the prevalence of small samples (Kareev et al., 2002). Here, we show that\neven in a natural sampling situation (repeated Bernoulli trials), in which a learner’s only decision is when to stop sampling,\ndifferent sampling goals can have an impact on sample composition and on inferences drawn from them. Specifically, we find\nthat learners sampling with a binary goal (”more heads/tails?”) versus a distributional goal (”how many heads?”) end up with\nsamples that differ not only in size but also content. Binary sampling leads to more samples with extreme distributions (many\nmore heads or tails) compared to distributional sampling. In this project, we explore the impact of those sampling goals on\nsubsequent decision-making on the basis of those samples.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[],"section":"Member Abstracts","is_remote":true,"remote_url":"https://escholarship.org/uc/item/93h9145b","frozenauthors":[{"first_name":"Anna","middle_name":"","last_name":"Coenen","name_suffix":"","institution":"New York University","department":""},{"first_name":"Todd","middle_name":"","last_name":"Gureckis","name_suffix":"","institution":"New York University","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2015-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/25890/galley/15514/download/"}]}