Types of approximation for probabilistic cognition: Sampling and variational.
A basic challenge for probabilistic models of cognition is explaining how probabilistically correct solutions are approximated by the limited brain, and how to explain mismatches with human behavior. An emerging approach to solving this problem is to use the same approximation algorithms that were b...
| Publicado en: | Brain & Cognition Vol. 112; pp. 98 - 102 |
|---|---|
| Autor principal: | |
| Formato: | review Journal Article |
| Publicado: |
Academic Press Inc.
Mar2017
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=121242906&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121242906 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02782626 88Z jtl: Brain & Cognition issn: 02782626 maglogo: N pubinfo: dt: Mar2017 vid: 112 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 121242906 121242906 NLM26228974 121242906 10.1016/j.bandc.2015.06.008 NLM26228974 121242906 ppf: 98 ppct: 4 formats: tig: atl: Types of approximation for probabilistic cognition: Sampling and variational. aug: au: Sanborn, Adam N. affil: Department of Psychology, University of Warwick, Coventry CV4 7AL, United Kingdom sug: subj: Models, Statistical Computer Simulation Cognition Brain Physiology Algorithms ab: A basic challenge for probabilistic models of cognition is explaining how probabilistically correct solutions are approximated by the limited brain, and how to explain mismatches with human behavior. An emerging approach to solving this problem is to use the same approximation algorithms that were been developed in computer science and statistics for working with complex probabilistic models. Two types of approximation algorithms have been used for this purpose: sampling algorithms, such as importance sampling and Markov chain Monte Carlo, and variational algorithms, such as mean-field approximations and assumed density filtering. Here I briefly review this work, outlining how the algorithms work, how they can explain behavioral biases, and how they might be implemented in the brain. There are characteristic differences between how these two types of approximation are applied in brain and behavior, which points to how they could be combined in future research. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|