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10.1016/j.cogpsych.2017.11.006 http://scihub22266oqcxt.onion/10.1016/j.cogpsych.2017.11.006 C5886040!5886040 !29500961     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\29500961 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Cogn+Psychol 2018 ; 102 (ä): 127-144 Nephropedia Template TP {{tp|p=29500961 |t=2018. Heuristics as Bayesian inference under extreme priors |pdf=|usr=}}{{29500961 }} gab.com Text Heuristics as Bayesian inference under extreme priors JO: Cogn Psychol http://www.kidney.de/pget.php?&tw=1&pmid=29500961 #FOAvip Simple heuristics are often regarded as tractable decision strategies because they ignore a great deal of information in the input data. One puzzle is why heuristics can outperform full-information models, such as linear regression, which make full use of the available information. These "less-is-more" effects, in which a relatively simpler model outperforms a more complex model, are prevalent throughout cognitive science, and are frequently argued to demonstrate an inherent advantage of simplifying computation or ignoring information. In contrast, we show at the computational level (where algorithmic restrictions are set aside) that it is never optimal to discard information. Through a formal Bayesian analysis, we prove that popular heuristics, such as tallying and take-the-best, are formally equivalent to Bayesian inference under the limit of infinitely strong priors. Varying the strength of the prior yields a continuum of Bayesian models with the heuristics at one end and ordinary regression at the other. Critically, intermediate models perform better across all our simulations, suggesting that down-weighting information with the appropriate prior is preferable to entirely ignoring it. Rather than because of their simplicity, our analyses suggest heuristics perform well because they implement strong priors that approximate the actual structure of the environment. We end by considering how new heuristics could be derived by infinitely strengthening the priors of other Bayesian models. These formal results have implications for work in psychology, machine learning and economics. Twit Text FOAVip Heuristics as Bayesian inference under extreme priors ????Cogn Psychol http://www.kidney.de/pget.php?&tw=1&pmid=29500961 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29500961 #FOAvip English Wikipedia <ref>{{Cite pmid|29500961 }}</ref> Heuristics as Bayesian inference under extreme priors #MMPMID29500961 Parpart P ; Jones M ; Love BC Cogn Psychol 2018[May]; 102 (ä): 127-144 PMID29500961 show ga Simple heuristics are often regarded as tractable decision strategies because they ignore a great deal of information in the input data. One puzzle is why heuristics can outperform full-information models, such as linear regression, which make full use of the available information. These "less-is-more" effects, in which a relatively simpler model outperforms a more complex model, are prevalent throughout cognitive science, and are frequently argued to demonstrate an inherent advantage of simplifying computation or ignoring information. In contrast, we show at the computational level (where algorithmic restrictions are set aside) that it is never optimal to discard information. Through a formal Bayesian analysis, we prove that popular heuristics, such as tallying and take-the-best, are formally equivalent to Bayesian inference under the limit of infinitely strong priors. Varying the strength of the prior yields a continuum of Bayesian models with the heuristics at one end and ordinary regression at the other. Critically, intermediate models perform better across all our simulations, suggesting that down-weighting information with the appropriate prior is preferable to entirely ignoring it. Rather than because of their simplicity, our analyses suggest heuristics perform well because they implement strong priors that approximate the actual structure of the environment. We end by considering how new heuristics could be derived by infinitely strengthening the priors of other Bayesian models. These formal results have implications for work in psychology, machine learning and economics. |*Heuristics [MESH] |*Models, Psychological [MESH] |*Models, Statistical [MESH] |Bayes Theorem [MESH] |Decision Making/*physiology [MESH]Heuristics as Bayesian inference under extreme priors (epmc).pdf DeepDyve Pubget Overpricing