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Selective sampling and inductive inference: Drawing inferences based on observed and missing evidence.

Abstract

We propose and test a Bayesian model of property induction with evidence that has been selectively sampled leading to "censoring" or exclusion of potentially relevant data. A core model prediction is that identical evidence samples can lead to different patterns of inductive inference depending on the censoring mechanisms that cause some instances to be excluded. This prediction was confirmed in four experiments examining property induction following exposure to identical samples that were subject to different sampling frames. Each experiment found narrower generalization of a novel property when the sample instances were selected because they shared a common property (property sampling) than when they were selected because they belonged to the same category (category sampling). In line with model predictions, sampling frame effects were moderated by the addition of explicit negative evidence (Experiment 1), sample size (Experiment 2) and category base rates (Experiments 3-4). These data show that reasoners are sensitive to constraints on the sampling process when making property inferences; they consider both the observed evidence and the reasons why certain types of evidence has not been observed.

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  • Publisher Full Text
  • Authors+Show Affiliations

    ,

    University of New South Wales, Australia. Electronic address: B.hayes@unsw.edu.au.

    ,

    University of New South Wales, Australia.

    ,

    University of New South Wales, Australia.

    University of New South Wales, Australia.

    Source

    Cognitive psychology 113: 2019 Jun 11 pg 101221

    Pub Type(s)

    Journal Article

    Language

    eng

    PubMed ID

    31200210

    Citation

    Hayes, Brett K., et al. "Selective Sampling and Inductive Inference: Drawing Inferences Based On Observed and Missing Evidence." Cognitive Psychology, vol. 113, 2019, p. 101221.
    Hayes BK, Banner S, Forrester S, et al. Selective sampling and inductive inference: Drawing inferences based on observed and missing evidence. Cogn Psychol. 2019;113:101221.
    Hayes, B. K., Banner, S., Forrester, S., & Navarro, D. J. (2019). Selective sampling and inductive inference: Drawing inferences based on observed and missing evidence. Cognitive Psychology, 113, p. 101221. doi:10.1016/j.cogpsych.2019.05.003.
    Hayes BK, et al. Selective Sampling and Inductive Inference: Drawing Inferences Based On Observed and Missing Evidence. Cogn Psychol. 2019 Jun 11;113:101221. PubMed PMID: 31200210.
    * Article titles in AMA citation format should be in sentence-case
    TY - JOUR T1 - Selective sampling and inductive inference: Drawing inferences based on observed and missing evidence. AU - Hayes,Brett K, AU - Banner,Stephanie, AU - Forrester,Suzy, AU - Navarro,Danielle J, Y1 - 2019/06/11/ PY - 2018/06/14/received PY - 2019/04/11/revised PY - 2019/05/14/accepted PY - 2019/6/15/pubmed PY - 2019/6/15/medline PY - 2019/6/15/entrez KW - Bayesian models KW - Categorization KW - Inductive reasoning KW - Property inference SP - 101221 EP - 101221 JF - Cognitive psychology JO - Cogn Psychol VL - 113 N2 - We propose and test a Bayesian model of property induction with evidence that has been selectively sampled leading to "censoring" or exclusion of potentially relevant data. A core model prediction is that identical evidence samples can lead to different patterns of inductive inference depending on the censoring mechanisms that cause some instances to be excluded. This prediction was confirmed in four experiments examining property induction following exposure to identical samples that were subject to different sampling frames. Each experiment found narrower generalization of a novel property when the sample instances were selected because they shared a common property (property sampling) than when they were selected because they belonged to the same category (category sampling). In line with model predictions, sampling frame effects were moderated by the addition of explicit negative evidence (Experiment 1), sample size (Experiment 2) and category base rates (Experiments 3-4). These data show that reasoners are sensitive to constraints on the sampling process when making property inferences; they consider both the observed evidence and the reasons why certain types of evidence has not been observed. SN - 1095-5623 UR - https://www.unboundmedicine.com/medline/citation/31200210/Selective_sampling_and_inductive_inference:_Drawing_inferences_based_on_observed_and_missing_evidence L2 - https://linkinghub.elsevier.com/retrieve/pii/S0010-0285(18)30162-2 DB - PRIME DP - Unbound Medicine ER -