Where do hypotheses come from?
Why are human inferences sometimes remarkably close to the Bayesian ideal and other times systematically biased? In particular, why do humans make near-rational inferences in some natural domains where the candidate hypotheses are explicitly available, whereas tasks in similar domains requiring the...
| Publicado en: | Cognitive Psychology Vol. 96; pp. 1 - 26 |
|---|---|
| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Academic Press Inc.
Aug2017
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=123867338&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 123867338 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00100285 COP jtl: Cognitive Psychology issn: 00100285 maglogo: N pubinfo: dt: Aug2017 vid: 96 pid: 735 pub: Academic Press Inc. artinfo: ui: 123867338 10.1016/j.cogpsych.2017.05.001 ppf: 1 ppct: 25 formats: tig: atl: Where do hypotheses come from? aug: au: Dasgupta, Ishita Schulz, Eric Gershman, Samuel J. affil: Department of Physics and Center for Brain Science, Harvard University, United States Department of Experimental Psychology, University College London, United Kingdom Department of Psychology and Center for Brain Science, Harvard University, United States su: Cognitive load Hypothesis Monte Carlo method Bayesian analysis Algorithms Superadditivity sug: subj: Cognitive load Hypothesis Monte Carlo method Bayesian analysis Algorithms Superadditivity keyword: Bayesian inference Hypothesis generation Monte Carlo methods Bayesian inference Hypothesis generation Monte Carlo methods ab: Why are human inferences sometimes remarkably close to the Bayesian ideal and other times systematically biased? In particular, why do humans make near-rational inferences in some natural domains where the candidate hypotheses are explicitly available, whereas tasks in similar domains requiring the self-generation of hypotheses produce systematic deviations from rational inference. We propose that these deviations arise from algorithmic processes approximating Bayes’ rule. Specifically in our account, hypotheses are generated stochastically from a sampling process, such that the sampled hypotheses form a Monte Carlo approximation of the posterior. While this approximation will converge to the true posterior in the limit of infinite samples, we take a small number of samples as we expect that the number of samples humans take is limited. We show that this model recreates several well-documented experimental findings such as anchoring and adjustment, subadditivity, superadditivity, the crowd within as well as the self-generation effect, the weak evidence, and the dud alternative effects. We confirm the model’s prediction that superadditivity and subadditivity can be induced within the same paradigm by manipulating the unpacking and typicality of hypotheses. We also partially confirm our model’s prediction about the effect of time pressure and cognitive load on these effects. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|